CompTIA DA0-001 Dumps

The correct answer is C. Indexing documents.

Indexing documents is a query optimization technique that involves creating a data structure that allows faster access to the data in the documents. Indexing documents can reduce the amount of data that needs to be scanned for a particular query, thus improving the performance and efficiency of the query. Indexing documents can also help with searching, sorting, filtering, and aggregating the data in the documents12

The ‘Status’ column in the dataset shows different terms such as “yes”, “completed”, “done”, and “Y” that likely represent the same outcome - that a task has been completed. This variation in terms leads to inconsistency within the data. Data profiling aims to ensure that data is consistent, among other quality metrics, to facilitate accurate analysis and reporting. By recoding the ‘Status’ column, the analyst seeks to address this inconsistency, ensuring that all entries indicating completion are represented uniformly. This enhances the data quality and usability for subsequent data analysis tasks.References:

The action of recoding is taken to standardize the data entries and eliminate inconsistencies, which is crucial for maintaining data integrity and ensuring reliable data analysis.

This is because redundant data is a type of data that is unnecessary or irrelevant for the analysis or purpose, which can affect the efficiency and performance of the analysis or process. Redundant data can be caused by having multiple data fields that store the same or similar information, such as patient height in inches and patient height in centimeters in this case. Redundant data can be eliminated or reduced by using data cleansing techniques, such as removing or merging the redundant data fields. The other types of data are not examples of data that is unnecessary or irrelevant for the analysis or purpose. Here is what they mean in terms of data quality:

• Dependent data is a type of data that relies on or is influenced by another data field or value, such as a formula or a calculation that uses other data fields or values as inputs or outputs. Dependent data can be useful or important for the analysis or purpose, as it can provide additional information or insights based on the existing data.
• Duplicate data is a type of data that is repeated or copied in a data set, which can affect the quality and validity of the analysis or process. Duplicate data can be caused by having multiple records or rows that have the same or similar values for one or more data fields or columns, such as customer ID or order ID. Duplicate data can be eliminated or reduced by using data cleansing techniques, such as removing or filtering out the duplicate records or rows.
• Invalid data is a type of data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis or process. Invalid data can be caused by having values that do not match the expected format, type, range, or rule for a data field or column, such as an email address that does not have an @ symbol or a date that does not follow the YYYY-MM-DD format. Invalid data can be eliminated or reduced by using data cleansing techniques, such as validating or correcting the invalid values.

The correct answer is A. 13.2%.

The total order conversion rate is the ratio of the total number of orders to the total number of clicks, expressed as a percentage. To calculate the total order conversion rate, we need to sum up the clicks and orders from all the channels, and then divide the orders by the clicks and multiply by 100.

Using the data from the table, we can do the following:

• Total clicks = 580 + 800 + 1,200 + 300 + 620 = 3,500
• Total orders = 55 + 100 + 220 + 60 + 85 = 520
• Total order conversion rate = (520 / 3,500) x 100 = 14.857%
• Rounding to one decimal place, we get 14.9%

Therefore, the total order conversion rate is 14.9%.

To ensure that a report is point-in-time and static, it should include the date when the report was last accessed and the date on which the report was run. These components confirm the specific time frame the data represents, making the report a fixed reference that does not change with subsequent data updates or accesses. This is crucial for accurate historical analysis and for maintaining the integrity of the data as it was at the time of the report’s creation.

References:

• Best practices in business reporting.
• Importance of time-stamping in data analysis.
• Guidelines for creating static reports in data analytics.

Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patients in a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.

Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

Explanation

SPSS Modeler.

QuickSight, Tableau, and Power BI are all powerful analytics and reporting tools that can pull data from a variety of sources. SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise.

Explanation

Lubridate is an R package that makes it easier to work with dates and times.

This is a simulation question that requires you to create a dashboard with visualizations that meet the director’s needs. Here are the steps to complete the task:

• Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.
• Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.
• Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.
• Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use “County Outages” as the title for the map, “Status” as the title for the pie chart, and “Trend” as the title for the line graph. You can also use “County”, “Number of Outages”, “Active”, “Restored”, “Pending”, “Time”, and “Number of Outages” as labels for the axes and legends of the visualizations.

Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the number of phone calls that a call center receives in a day are all examples of discrete data. Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is D. References: [Discrete vs Continuous Data: Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]

This is because an INNER JOIN is a type of join that combines two tables based on a matching condition and returns only the rows that satisfy the condition. An INNER JOIN can be used to merge data from different tables that have a common column or a key, such as customer ID or order ID. To perform an INNER JOIN of the customer and order tables, we can use the following SQL statement:

This statement will select all the columns (*) from both tables and join them on the customer ID column, which is the common column between them. The result of this statement will be a new table that has seven rows and eight columns, as shown below:

The reason why there are seven rows and eight columns in the result table is because:

• There are seven rows because there are six customers and six orders in the original tables, but only five customers have matching orders based on the customer ID column. Therefore, only five rows will have data from both tables, while one row will have data only from the customer table (customer 5), and one row will have no data at all (null values).
• There are eight columns because there are four columns in each of the original tables, and all of them are selected and joined in the result table. Therefore, the result table will have four columns from the customer table (customer ID, first name, last name, and email) and four columns from the order table (order ID, order date, product, and quantity).

The correct answer is B. Transcription.

Transcription is a necessary step on the way to interpretation when a data set was recorded using multimedia technology. Multimedia technology refers to the use of various forms of media, such as audio, video, images, and text, to capture and present information1 Transcription is the process of converting multimedia data into written or textual form, which can then be analyzed using various methods and tools2 Transcription can help to make the data more accessible, searchable, and manageable, as well as to preserve the data for future use.

Structural equation modeling is not correct, because it is a statistical technique that tests the causal relationships between multiple variables using observed and latent variables. Structural equation modeling is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.

Sequential analysis is not correct, because it is a method of analyzing the order and timing of events or behaviors in a data set. Sequential analysis is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.

Sampling is not correct, because it is the process of selecting a subset of data from a larger population for analysis. Sampling is not a necessary step on the way to interpretation, but rather a preliminary step that can be done before collecting or analyzing the data.

Explanation

The correct answer is A: SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5; String manipulation (or string handling) is the process of changing, parsing, splicing, pasting, or analyzing strings. SQL is used for managing data in a relational database. The CONCAT () function adds two or more strings together. Syntax CONCAT(stringl, string2,... string_n) Parameter Values Parameter Description stringl, string2, string_n Required. The strings to add together.

The best format to deliver this report is D. A dashboard with filters at the top that the user can toggle.

A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance1 A dashboard with filters at the top that the user can toggle would allow the user to easily and quickly access the information they need about various regions, products, and time periods, without having to navigate through multiple tabs, pages, or emails. A dashboard with filters would also enable the user to compare and contrast different views of the data and see how they change over time. A dashboard with filters would also be more interactive and engaging than a static or email report2

A workbook with multiple tabs for each region would not be an efficient way to deliver this report, because it would require the user to switch between different tabs to see the information they need. This would make it harder to compare and contrast different regions, products, and time periods, and also increase the risk of errors or confusion. A workbook with multiple tabs would also be less visually appealing and more cluttered than a dashboard3

A daily email with snapshots of regional summaries would not be an efficient way to deliver this report, because it would limit the user’s ability to explore the data in depth and customize their view. A daily email would also be dependent on the frequency and timing of the email delivery, which might not match the user’s needs or preferences. A daily email would also be more likely to be ignored or deleted than a dashboard that is always accessible.

A static report with a different page for every filtered view would not be an efficient way to deliver this report, because it would create a very long and cumbersome report that would be difficult to read and understand. A static report would also not allow the user to change or update the filters as they wish, or see how the data changes over time. A static report would also be less interactive and engaging than a dashboard.

The data is presented in a CSV (comma-separated values) file format, which is a plain text format that stores tabular data. Each line of the file is a data record, and each record consists of one or more fields separated by commas. The first line of the file usually contains the names of the fields, also known as the header. In this case, the data has four fields: Name, Age, Gender, and Occupation. Therefore, the correct answer is B. References: CSV File (What It Is & How to Open One), Comma-separated values - Wikipedia

The most efficient function for an analyst to execute a data pull for the two prior months would be the Date function. This function allows for the manipulation and formatting of date values within a database. Using Date functions, an analyst can dynamically calculate the start and end dates for the previous two months, ensuring that the data pull is accurate and automated without manual intervention.

For example, SQL functions like DATEADD and DATEDIFF can be used to determine the exact range of dates needed for the data pull. These functions can calculate the first and last day of the previous months relative to the current date, which is essential for monthly reporting and analysis.

References:

• Discussions on Stack Overflow suggest using SQL date functions like DATEADD and DATEDIFF to dynamically extract data for previous months, which supports the use of Date functions12.
• The use of Date functions is also recommended for ensuring that the data pull is not only efficient but also accurate, as it avoids potential errors associated with manual date entry3.

Parameterization in SQL queries is a technique used to prevent SQL injection, which is a common security vulnerability that allows an attacker to interfere with the queries that an application makes to its database. By using parameterized queries, the database can distinguish between code and data, regardless of the input received. This method ensures that an attacker cannot change the intent of a query, even if SQL commands are inserted by the attacker. While parameterization can also affect performance by enabling consistent query execution plans, its primary purpose is to enhance security.

References:

• Medium article on SQL Query Optimization1.
• MSSQLTips on SQL Query Performance2.
• Blog post on SQL Performance Optimization3.
• SQL Easy guide on improving SQL Query Performance4.
• LearnSQL.com on SQL for Data Analysis5.

The best reporting enhancement that the accounting department could implement to reduce the number of calls on current accounts is C. Increase the frequency of report generation.

By increasing the frequency of report generation, the accounting department could provide the collections manager with more up-to-date information on the customers who are past due on their accounts. This would help to avoid calling customers who have made payments in the time since the last report was printed, and thus reduce the number of calls on current accounts. Increasing the frequency of report generation would also improve the accuracy and timeliness of the data, and enhance the efficiency and effectiveness of the collections process.

Modifying the date range on the report, including a time stamp on the report, or adding a report run date to the report would not be sufficient to reduce the number of calls on current accounts. These enhancements would only provide information on when the report was generated or what period it covers, but they would not change the fact that the report could be outdated by the time it reaches the collections manager. Therefore, these enhancements would not solve the problem of calling customers who have already paid their accounts.

The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

The best delivery method to support the client’s requirement is C. Subscription.

Short explanation: A subscription is a delivery method that allows the client to access the dashboard on-demand, whenever they need it. A subscription can be set up by the data analyst or the client themselves, and it can be configured to send an email notification when the dashboard is updated or refreshed. A subscription also allows the client to view the dashboard online or download it as a file format of their choice12

A. Email is not the best delivery method because it does not allow the client to access the dashboard on-demand. Email deliveries are sent at a fixed time or frequency, and they may not reflect the latest data or changes in the dashboard. Email deliveries also have limitations on the file size and format of the dashboard attachments1

B. Scheduled is not the best delivery method because it does not allow the client to access the dashboard on-demand. Scheduled deliveries are similar to email deliveries, except that they are triggered by a specific event or condition, such as a data update or a threshold value. Scheduled deliveries also have the same limitations as email deliveries on the file size and format of the dashboard attachments1

D. Static is not the best delivery method because it does not allow the client to access the dashboard on-demand. Static deliveries are one-time deliveries that are manually generated by the data analyst or the client. Static deliveries do not update or refresh automatically, and they may become outdated or irrelevant over time. Static deliveries also have limitations on the file size and format of the dashboard files3

The action that must be done to the Genre column before this task can be completed is delimit. Delimit is a process of separating or splitting a string of text into multiple parts based on a delimiter, which is a character or a sequence of characters that marks the boundary between the parts. For example, a comma (,) or a semicolon (;) can be used as a delimiter. In this case, the Genre column contains multiple genres for each movie, separated by commas. To determine the most popular movie genre, the data analyst needs to delimit the Genre column by commas, so that each genre can be counted and compared separately. The other options are not relevant for this task, as they are related to combining or joining strings or tables, not separating them. Append is a process of adding or attaching one string or table to the end of another string or table. Merge is a process of combining or joining two or more tables into one table based on a common column or key. Concatenate is a process of joining or linking two or more strings together into one string. Reference: [How to Split Text in Excel - Exceljet]

A JSON (JavaScript Object Notation) file is a text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). JSON files are human-readable and can be interpreted by various programming languages, making them ideal for data interchange123.

JSON files typically contain an array of objects, with each object representing a record with a series of name-value pairs. This structured format is both easy to understand and write by humans and easy for machines to parse and generate4.

References:

• JSON’s official definition and syntax rules1.
• A beginner’s guide to JSON and its data types2.
• Understanding the JSON file format3.
• Detailed explanation of JSON as a structured data format4.

Answer: C. Coding

Coding is a technique that is used to quantify data, especially qualitative data that are not expressed numerically. Coding involves assigning codes, such as numbers, letters, symbols, or colors, to different categories or themes that emerge from the data. For example, if you have a set of survey responses that ask about the satisfaction level of customers, you can code them as follows:

• Very satisfied = 5
• Satisfied = 4
• Neutral = 3
• Dissatisfied = 2
• Very dissatisfied = 1

By coding the data, you can convert them into quantitative data that can be analyzed using statistical methods, such as calculating the mean, median, mode, frequency, or percentage of each category12.

Option A is incorrect, as decoding is not a technique that is used to quantify data, but rather a process of interpreting or translating data from one form to another. For example, decoding can involve converting binary codes into text or images, or decrypting ciphertext into plaintext3.

Option B is incorrect, as enumeration is not a technique that is used to quantify data, but rather a process of listing or naming data in a specific order. For example, enumeration can involve listing the names of the states in alphabetical order, or naming the planets in order of their distance from the sun4.

Option D is incorrect, as structure is not a technique that is used to quantify data, but rather a property or characteristic of data that describes how they are organized or arranged. For example, structure can refer to the format, type, or schema of data, such as structured, semi-structured, or unstructured data.

Explanation

1. Duplicates

2. Misspellings

The most common data quality issues are difficult to resolve in Excel because of their rigidity. It forces analysts to do a ton of manual work, which results in a high probability of an error being introduced to the data set. Those common issues include:

- Blanks

- Nulls

- Outliers

- Duplicates

- Extra spaces

- Misspellings

- Abbreviations and domain-specific variations

- Formula error codes

When introduced, these errors can skew or even invalidate the resulting analysis. A smart tool would minimize the possibility of error by automating the manual work. In Excel, you might look for data quality issues in one of two ways. First, you might use auto filters on specific columns to scan for anomalies and blanks or you might use a pivot table to find gaps and discrepancies.

In either case, you're scanning for the anomalies yourself. Suffice it to say that's not a very efficient process. It also means accuracy is only as good as the analyst's eye, so the probability of error varies throughout the day.

The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a successful data pull of addresses.

References: This answer is based on general data analytics practices and does not reference a specific document.

An operational report is the most suitable type of report for information that needs to be sent out on a regular, scheduled basis, such as weekly. Operational reports are designed to provide ongoing insights into the performance of an organization’s operations and are typically automated to be distributed at set intervals. This automation can include scheduling the reports to be emailed to a specific list of recipients, making it an efficient solution for the analyst’s requirement.

Operational reports are often generated from data that is continuously updated, ensuring that the recipients receive the most current information at the time of the report’s distribution. This contrasts with ad hoc reports ©, which are usually created as needed and are not scheduled. Self-service reports (A) require users to generate the report themselves, which is not the requirement here. Research reports (B) are generally more detailed and are not typically used for regular operational updates.

References:

• The guidelines on writing email reports suggest that for regular, scheduled information dissemination, structured reports like operational reports are preferred1.
• Best practices in reporting also recommend automated and scheduled reports for consistent and timely updates, which operational reports provide2.

Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. References: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples

The correct answer is .html.

Short explanation: Web scraping is the process of extracting data from websites by parsing the HTML code of the web pages. HTML stands for HyperText Markup Language and it is the standard markup language for creating web pages and web applications. HTML files have the extension .html and they contain tags, elements, attributes, and content that define the structure and appearance of a web page. Web scraping tools need to parse the HTML files to extract the relevant data from the web pages12

This is because Microsoft Excel is a type of software application that allows users to create, edit, and analyze data in spreadsheets, which are composed of rows and columns of cells that can store various types of data, such as numbers, text, or formulas. Microsoft Excel can be used for calculations and pivot tables, which are two common features or functions in data analysis. Calculations are mathematical operations or expressions that can be performed on the data in the cells, such as addition, subtraction, multiplication, division, average, sum, etc. Pivot tables are interactive tables that can summarize and display the data in different ways, such as by grouping, filtering, sorting, or aggregating the data based on various criteria or categories. The other software applications are not used for calculations and pivot tables. Here is why:

IBM SPSS is a type of software application that allows users to perform statistical analysis and modeling on data sets, such as regression, correlation, ANOVA, etc. IBM SPSS does not use spreadsheets or cells to store or manipulate data, but rather uses data views or variable views to display the data in rows and columns. IBM SPSS does not have pivot tables as a feature or function, but rather has output views or charts to display the results of the analysis.

SAS is a type of software application that allows users to perform data management and analysis using a programming language that consists of statements and commands. SAS does not use spreadsheets or cells to store or manipulate data, but rather uses data sets or tables that are stored in libraries or folders. SAS does not have pivot tables as a feature or function, but rather has procedures or macros that can produce summary tables or reports based on the data.

Domo is a type of software application that allows users to create and share dashboards and visualizations that display data from various sources and systems, such as databases, cloud services, or web applications. Domo does not use spreadsheets or cells to store or manipulate data, but rather uses connectors or APIs to access and integrate the data from different sources. Domo does not have pivot tables as a feature or function, but rather has cards or widgets that can show different aspects or metrics of the data.

The standard deviation is a measure of the amount of variation or dispersion in a set of values. It is calculated as the square root of the variance. Given that the variance for the data set is 36 gallons, the standard deviation can be found by taking the square root of 36, which is 6. Therefore, the standard deviation for the number of gallons of oil produced each day is 6 gallons.

References:

• The concept of standard deviation and its calculation is a fundamental aspect of statistics, which is well-documented in statistical textbooks and resources.
• The calculation performed to arrive at the answer is based on the mathematical operation of taking the square root of the variance value.

Explanation

While this scenario describes a system integration challenge that can be solved with ETL or ELT, Angela is facing a Record linkage issue.

Customer segmentation is a type of cluster analysis, which is a method of grouping data points based on their similarities or differences. Cluster analysis can help identify patterns and trends in the data, as well as target specific groups of customers for marketing purposes. One common technique for customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value. This technique assigns a score to each customer based on how recently they bought the product, how often they buy the product, and how much they spend on the product. These scores can then be used to create clusters of customers with different characteristics and preferences. Therefore, the correct answer is D. References: Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate Guide for Customer Segmentation

The report for the CEO should include YTD total sales, as this will provide a high-level overview of the sales performance of the company and show how it is meeting its annual goals. The other options are not appropriate for the CEO, as they are either too detailed or irrelevant for the report. The sales representatives’ home addresses, line-item SKU numbers, and customers’ first and last names are not related to the sales performance and might compromise the privacy and security of the data. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

  A Pie chart is the most suitable for representing the average spending of active customers versus inactive customers. Pie charts are effective for comparing parts of a whole, which makes them ideal for visually displaying the proportion of spend between two distinct groups. They are widely used to depict percentage distributions and are straightforward, allowing immediate analysis of the active vs. inactive customer spending distribution at a glance.

 A dashboard is a graphical display that summarizes and presents key performance indicators (KPIs) and metrics for a business or a project. A dashboard should be clear, concise, and easy to understand. To develop a dashboard, one of the best practices is to create a wireframe or a mockup first. A wireframe or a mockup is a low-fidelity sketch or prototype of the dashboard layout and design, which helps to define the scope, requirements, and functionality of the dashboard. Creating a wireframe or a mockup can help to save time and resources, as well as to get feedback from stakeholders and users before deploying the dashboard to production. Therefore, the correct answer is A. References: [Dashboard Design Best Practices: 4 Key Principles | Toptal], [How to Create an Effective Dashboard (with Examples) | Tableau]

The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula:

Celsius = (Fahrenheit - 32) * 5/9

This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by 5/9. For example, if the temperature is 68°F, the rescaled value in Celsius is:

Celsius = (68 - 32) * 5/9 Celsius = 20°C

Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.

Strategy 4 provides the best sales in comparison to other strategies. This is because the total sales for Strategy 4 are the highest among all the strategies, as shown by the black line. The other statements are not accurate or do not uphold integrity in data reporting. Here is why:

Statement A is false because sales are not approximately equal for Product A and Product B across all strategies. For example, in Strategy 1, Product A has more sales than Product B, while in Strategy 3, Product B has more sales than Product A.

Statement C is misleading because it does not account for the difference in scale between the products. While Strategy 2 has the highest total sales among all products, it does not necessarily mean that it is the most effective for each product. For instance, Product D has very low sales in Strategy 2 compared to other strategies.

Statement D is biased because it does not provide any evidence or justification for why Product D should be promoted more than the other products in all strategies. It also ignores the fact that Product D has the lowest sales among all products in most of the strategies.

When creating a dashboard to identify trends in data sets, the most appropriate consideration is the data sources and attributes. This is because the quality, reliability, and structure of the data sources directly influence the dashboard’s ability to accurately reflect trends. Attributes, such as the type of data and the time frame it covers, are crucial for trend analysis. A well-designed dashboard should provide a clear and intuitive representation of the data, allowing for easy identification of trends and patterns.

Frequently asked questions (B) can inform the design of the dashboard but are not a direct consideration for the development process itself. A report from the data source © might be an output of the dashboard but does not guide its development. A comparison of data sets (D) could be a feature of the dashboard, but the underlying data sources and attributes must be considered first to ensure accurate and meaningful comparisons.

References:

• Best practices in dashboard design emphasize the importance of understanding and consolidating different data sources and creating a mix of useful metrics, which aligns with the choice of data sources and attributes1.
• Fundamental dashboard design principles include the clear and efficient display of information, which is dependent on the proper selection and use of data sources and attributes2.
• Effective dashboard communication is achieved by using colors, shapes, sizes, labels, and legends meaningfully, all of which rely on the underlying data sources and attributes3.

The first step in the development process of revamping a company-wide dashboard should be to talk to the group that made the request to determine the desired goal. This would help to understand the needs, expectations, and preferences of the stakeholders, as well as the scope, purpose, and objectives of the project. Talking to the group that made the request would also help to establish a clear communication channel, build rapport and trust, and solicit feedback and suggestions.

Machine data is the most likely type of data to be streamed live, as it refers to data generated by machines or devices, such as sensors, web servers, network devices, etc. Machine data is often produced continuously and in large volumes, requiring real-time processing and analysis. Other types of data, such as key-value pairs, delimited rows, and flat files, are more likely to be stored in databases or files and processed in batches.

A flat file is a type of data structure that stores data in a plain text format, such as CSV, TSV, or TXT. A flat file consists of one or more records, each containing one or more fields, separated by a delimiter, such as a comma, tab, or space. A flat file does not have any hierarchical or relational structure, and does not support any complex queries or operations1.

A flat file may be the best data structure for the analyst to use in this scenario, because:

• The analyst collects ten data points from 1,000 specimens, which means the data is relatively small and simple, and can be easily stored and processed in a flat file.
• The analyst will not need any additional data to complete the analysis, which means the data is static and does not require any updates or modifications.
• The analyst will not need to retrieve information by specifier, which means the data does not require any indexing or searching by key or value.

Filter on any of the responses that do not say “January” and update them to “January”. This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say “January” and updating them to “January”, the analyst can make sure that all the responses for the month of January are written in the same way. The other steps are not appropriate for ensuring data consistency. Here is why:

Deleting any of the responses that do not have “January” written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.

Replacing any of the responses that have “01” would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “Jan” and “January”. This could cause confusion and errors in the analysis.

Sorting any of the responses that say “Jan” and updating them to “01” would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “01” and “January”. This could also cause confusion and errors in the analysis.

An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.

To perform an INNER JOIN, we can use the following SQL query:

SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 9 rows of data, as shown below:

customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09

To perform a LEFT JOIN, we can use the following SQL query:

SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 15 rows of data, as shown below:

customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL

As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.

Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

The best sampling method for the data analyst’s need is C. Stratified sampling.

Stratified sampling is a type of probability sampling that involves dividing the population into homogeneous groups or strata based on some characteristic, such as state, and then randomly selecting a proportional number of individuals from each stratum. Stratified sampling ensures that every group is adequately represented in the sample, and reduces the sampling error and variability12

Systematic sampling is not correct, because it involves selecting every nth individual from the population, starting from a random point. Systematic sampling does not guarantee that every state will have a similar proportion of data in the sample, and may introduce bias or error if there is a hidden pattern or order in the population12

Convenience sampling is not correct, because it involves selecting individuals who are easily accessible or available to the researcher. Convenience sampling is a type of non-probability sampling that does not involve random selection, and may result in a biased or unrepresentative sample12

Random sampling is not correct, because it involves selecting individuals from the population at random, without any grouping or stratification. Random sampling may not produce a sample that has a similar proportion of data from every state, especially if the population is large or heterogeneous. Random sampling may also have a higher sampling error and variability than stratified sampling12

Data encryption and data masking are two actions that can be taken when transmitting data to mitigate the chance of a data leak occurring. Data encryption means transforming data into an unreadable format that can only be decrypted with a key. Data masking means hiding or replacing sensitive data with fictitious or anonymized data. Both methods protect the confidentiality and integrity of the data in transit. References: CompTIA Data+ Certification Exam Objectives, page 13

Explanation

Correct answer C. Snowflake.

Since the dimension links to a dimension that isn't connected to the fact table, it must be a Snowflake, with a Star, all dimensions link directly to the fact table, Sun and Comet are not data warehouse design patterns.

The most likely cause of the issue is that the databases are recording the event in different time zones. For example, if one database is in New York and the other database is in Los Angeles, there is a three-hour difference between them. Therefore, an event that occurs at 12:00 PM in New York would be recorded as 9:00 AM in Los Angeles. To avoid this issue, the databases should either use a common time zone or convert the timestamps to a standard format. Therefore, option C is correct.

Option A is incorrect because the data analyst is not querying the databases incorrectly, but rather observing a discrepancy in the timestamps.

Option B is incorrect because the databases are recording the same event, but with different timestamps.

Option D is incorrect because the second database is not logging incorrectly, but rather using a different time zone.

Integrity and D. Consistency. This is because integrity and consistency are two of the best reasons to validate data for quality control purposes, which means to check and ensure that the data is accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for integrity and consistency, the analyst can prevent or correct any errors or issues in the data that could affect the validity or reliability of the analysis or the results. Here is what integrity and consistency mean in terms of data quality:

• Integrity refers to the completeness and validity of the data, which means that the data has no missing, incomplete, or invalid values that could compromise its meaning or usefulness. For example, validating data for integrity could involve checking for null values, outliers, or incorrect data types in the data set.
• Consistency refers to the uniformity and standardization of the data, which means that the data follows a common format, structure, or rule across different sources or systems. For example, validating data for consistency could involve checking for spelling, punctuation, or capitalization errors in the data set.

The other reasons are not the best reasons to validate data for quality control purposes. Here is why:

• Retention refers to the storage and preservation of the data, which means that the data is kept and maintained in a secure and accessible way for future use or reference. Retention does not need to be validated for quality control purposes, because it does not affect the accuracy or reliability of the data itself.
• Transmission refers to the transfer and exchange of the data, which means that the data is moved or shared between different sources or systems in a fast and efficient way. Transmission does not need to be validated for quality control purposes, because it does not affect the completeness or validity of the data itself.
• Encryption refers to the protection and security of the data, which means that the data is encoded or scrambled in a way that prevents unauthorized access or use. Encryption does not need to be validated for quality control purposes, because it does not affect the uniformity or standardization of the data itself.
• Deletion refers to the removal and disposal of the data, which means that the data is erased or destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for quality control purposes, because it does not affect the meaning or usefulness of the data itself.

To compare the students’ scores, we need to standardize them by using the z-score formula, which is:

z = (x - μ) / σ

where x is the raw score, μ is the mean, and σ is the standard deviation. The z-score tells us how many standard deviations a score is above or below the mean. A higher z-score means a better score relative to the average.

Using the table, we can calculate the z-scores for each student as follows:

Randy: z = (76 - 70) / 2 = 3 Katie: z = (86 - 80) / 3 = 2 Ralph: z = (80 - 75) / 2 = 2.5 Jean: z = (80 - 90) / 1 = -10

The student with the highest z-score is Randy, with a z-score of 3. This means that Randy scored 3 standard deviations above the mean in math, which is the best performance among the four students. Therefore, the correct answer is A.

References: Comparing with z-scores (video) | Z-scores | Khan Academy, 17 Important Data Visualization Techniques | HBS Online

The best answer is B. As a sample size grows, its mean gets closer to the average of the whole population.

The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. This is due to the sample being more representative of the population as it increases in size. The law of large numbers guarantees stable long-term results for the averages of some random events1

A. As a sample size decreases, its standard deviation gets closer to the average of the whole population is not correct, because it confuses the concepts of standard deviation and mean. Standard deviation is a measure of how much the values in a data set vary from the mean, not how close the mean is to the population average. Also, as a sample size decreases, its standard deviation tends to increase, not decrease, because the sample becomes less representative of the population.

C. As a sample size decreases, its mean gets closer to the average of the whole population is not correct, because it contradicts the law of large numbers. As a sample size decreases, its mean tends to deviate from the average of the whole population, because the sample becomes less representative of the population.

D. When a sample size doubles, the sample is indicative of the whole population is not correct, because it does not specify how close the sample mean is to the population average. Doubling the sample size does not necessarily make the sample indicative of the whole population, unless the sample size is large enough to begin with. The law of large numbers does not state a specific number or proportion of samples that are indicative of the whole population, but rather describes how the sample mean approaches the population average as the sample size increases indefinitely.

Two reasons to conduct data cleansing are:

• To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable. This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12
• To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the data. This can help to identify and measure the patterns, changes, or relationships in the data over time13

This is because a data dictionary is a type of document that defines and describes the data elements, attributes, and relationships in a database or a data set. A data dictionary can be used to facilitate the MDM (Master Data Management) process, which is a process that aims to ensure the quality, consistency, and accuracy of the data across different sources and systems. By creating a data dictionary first, the analyst can establish a common understanding and standardization of the data field names, types, formats, and meanings, as well as identify any potential issues or conflicts in the data, such as missing values, duplicate values, or inconsistent values. The other MDM processes can take place after creating a data dictionary. Here is why:

Compliance with regulations is a type of MDM process that ensures that the data meets the legal and ethical requirements and standards of the industry or the organization. Compliance with regulations can take place after creating a data dictionary, because the data dictionary can help the analyst to identify and apply the relevant rules and policies to the data, such as data privacy, security, or retention.

Standardization of data field names is a type of MDM process that ensures that the data field names are consistent and uniform across different sources and systems. Standardization of data field names can take place after creating a data dictionary, because the data dictionary can provide a reference and a guideline for naming and labeling the data fields, as well as resolving any discrepancies or ambiguities in the data field names.

Consolidation of multiple data fields is a type of MDM process that combines or merges the data fields from different sources or systems into a single source or system. Consolidation of multiple data fields can take place after creating a data dictionary because the data dictionary can help the analyst to map and match the data fields from different sources or systems based on their definitions and descriptions, as well as eliminating any redundant or duplicate data fields.

When consolidating data from different sources, it is crucial to standardize field names to ensure consistency across datasets. This process involves aligning the field names so that they are the same in both tables, which simplifies the merging of data and subsequent analysis. Standardizing field names helps in maintaining data integrity and avoids confusion that may arise from having different names for the same data point.

Recode the data values (B) would not be necessary unless the data values themselves are inconsistent or in different formats. Overwriting the field names in one of the tables © could lead to loss of information or confusion. Editing the field names in the data dictionary (D) is helpful, but it does not address the immediate need to harmonize the field names in the actual datasets.

References:

• Best practices in data management.
• Principles of data integration and consolidation.

The best type of chart to display the data is A. Include a bar chart using the site and the percentage of new customers data.

A bar chart is a good choice for comparing categorical data with numerical data, such as the site and the percentage of new customers. A bar chart can show the relative differences between the sites and highlight the site with the highest percentage of new customers. A bar chart can also be easily labeled and formatted to make the data clear and understandable.

A line chart is not suitable for this data, because it is used to show trends or changes over time, which is not relevant for the site and the percentage of new customers data. A line chart would also be confusing and misleading, as it would imply a connection or correlation between the sites that does not exist.

A pie chart is also not a good choice for this data, because it is used to show the proportion of a whole, not the comparison of different categories. A pie chart would also be difficult to read and interpret, as it would require labels or legends to identify the sites and their percentages. A pie chart would also not be able to show the exact values of the percentages, only their relative sizes.

A scatter chart is another inappropriate option for this data, because it is used to show the relationship or correlation between two numerical variables, not between a categorical and a numerical variable. A scatter chart would also be cluttered and unclear, as it would plot each site as a point on a coordinate plane, without any labels or axes. A scatter chart would also not be able to show the differences or rankings between the sites and their percentages.

 In technical documentation, a glossary is the designated section where definitions for technical terms are provided. It serves as a reference point for readers to understand specialized or uncommon words used within the document. Including descriptions of technical terms in a glossary ensures that readers have a consistent resource to refer to, which can improve comprehension and reduce misunderstandings12.

A system diagram (Option B) is a visual representation of the system’s components and their interactions, not a place for defining terms. User requirements (Option C) outline what end-users expect from the system, and an index (Option D) is an alphabetical list of topics covered in the document, usually with page numbers, but not definitions.

References:

• Creating effective technical documentation1.
• Best practices when writing technical descriptions3.

A CSV file is a type of flat file that stores data as plain text in a table-like structure with rows and columns. Each row represents a single record, while columns represent fields or attributes of the data. A CSV file uses commas or other delimiters to separate the values in each row. A CSV file can be easily imported or exported by various applications and programs12

A measure of the amount of dispersion of a set of values. This is because standard deviation is a type of statistical measure that quantifies how much the values in a data set vary or deviate from the mean or the average of the data set. Standard deviation can be used to describe the spread or the distribution of the data, as well as to identify any outliers or extreme values in the data. For example, a low standard deviation indicates that the values are close to the mean, while a high standard deviation indicates that the values are far from the mean. The other options are not correct descriptions of standard deviation. Here is why:

• A measure that is used to establish a relationship between two variables is not a correct description of standard deviation, but rather a description of correlation or regression, which are types of statistical measures that quantify how two variables are related or associated with each other. Correlation or regression can be used to test or model the dependence or the influence of one variable on another variable, as well as to predict or estimate the value of one variable based on the value of another variable.
• A measure of how data is distributed is not a correct description of standard deviation, but rather a description of frequency or probability, which are types of statistical measures that quantify how often or how likely a value or an event occurs in a data set. Frequency or probability can be used to describe the occurrence or the chance of the data, as well as to compare or contrast different categories or groups of the data.
• A measure that is used to find the significant difference between variables is not a correct description of standard deviation, but rather a description of hypothesis testing or inferential statistics, which are types of statistical methods that use sample data to make generalizations or conclusions about a population or a parameter. Hypothesis testing or inferential statistics can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation.

After writing the report, the most appropriate next step for the analyst is to complete a check for quality in the report. This involves reviewing the report for accuracy, clarity, completeness, consistency, and relevance. The analyst should ensure that the report addresses the director’s business questions and objectives, that the data and analysis are correct and reliable, that the tables and graphs are well-designed and easy to understand, that the descriptions and summary are concise and informative, and that there are no errors or inconsistencies in the report. A quality check will help the analyst to improve the presentation and communication of the report, as well as to avoid any misunderstandings or misinterpretations by the director1.

A static report is a type of report that shows a snapshot of data at a specific point in time. A static report does not change or update automatically, unless the data source is refreshed or the report is regenerated. A static report is suitable for situations where the data does not change frequently or where historical data is needed for comparison or analysis. In this case, the data analyst is asked to create a sales report for the second-quarter 2020 board meeting, which will include a review of the business’s performance through the second quarter. The board meeting will be held on July 15, 2020, after the numbers are finalized. This means that the data analyst does not need to show real-time or dynamic data, but rather a fixed and accurate view of the sales data for the second quarter. Therefore, a static report would be the best way to meet this stakeholder requirement. Therefore, the correct answer is A. References: What are Static Reports? | Sisense, Static vs Dynamic Reports - What’s The Difference? | datapine

The table provided shows an inconsistency in the ‘Gender’ column, which lists three distinct values: Male, Female, and College. This is inconsistent because ‘College’ is not a gender category. The ‘Gender’ column should only have two distinct values, typically ‘Male’ and ‘Female’, to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection.

In data analysis, it’s crucial to ensure that categorical variables like gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.

References:

• Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender1.
• Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets2.
• The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results3.

A dynamic report is a type of report that shows data that changes or updates automatically based on certain criteria or parameters. A dynamic report can allow users to interact with the data, filter it, drill down into it, or visualize it in different ways. A dynamic report is suitable for situations where the data changes frequently or where real-time or near-real-time data is needed for decision making or analysis. In this case, the data analyst is asked to create a sales report for the second-quarter 2020 board meeting, which will include a review of the business’s performance through the second quarter. The board meeting will be held on July 15, 2020, after the numbers are finalized. This means that the data analyst does not need to show real-time or dynamic data, but rather a fixed and accurate view of the sales data for the second quarter. Therefore, a static report would be the best way to meet this stakeholder requirement. Therefore, the correct answer is A. References: [What are Dynamic Reports? | Sisense], Static vs Dynamic Reports - What’s The Difference? | datapine

Merging tables to create a master file that includes all transactions for both online and in-store sales is best described as data consolidation. This process involves combining data from various sources into a single, unified dataset. Data consolidation is essential for providing a comprehensive view of all transactions, which can be used for analysis, reporting, and decision-making purposes.

References: The answer is based on standard data management practices and the definition of data consolidation. No specific external documents were referenced for this response.

SQL (Structured Query Language) is a domain-specific language used in programming, specifically designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Unlike languages like Python or R, which are general-purpose programming languages, SQL is tailored specifically for database management and manipulation.

References:

• ResearchGate article on SQL1.
• SpringerLink chapter on Relational Databases and SQL Language2.
• DataCamp tutorial on SQL Server Installation3.
• Wikipedia page on SQL4.

A data-mining ETL tool is a software application that performs extract, transform, and load (ETL) operations on data for data mining purposes. Data mining is the process of discovering patterns, trends, and insights from large and complex data sets. ETL tools help to prepare the data for analysis by extracting data from various sources, transforming data into a consistent and suitable format, and loading data into a data warehouse or other destination. SSIS (SQL Server Integration Services) is an example of a data-mining ETL tool that is part of Microsoft SQL Server. SSIS provides graphical tools and wizards for building and debugging ETL packages that can work with various data sources and destinations. Therefore, the correct answer is A. References: [Data Mining - SQL Server Integration Services (SSIS) | Microsoft Docs], [What Is Data Mining? | Oracle]

A word cloud is an ideal choice for visualizing what employees are searching for on an intranet site. It represents the frequency of word occurrence in a visually impactful way, with more commonly searched terms appearing larger in the cloud. This allows for quick identification of the most popular queries and topics of interest among employees. Unlike histograms, pie charts, or scatter plots, word clouds can effectively display textual data, which is the nature of search queries. They are particularly useful for analyzing text data from surveys or feedback forms, which can be similar to search query data in an intranet environment1234.

References: 1: “What Are Word Clouds? Pros & Cons of Word Cloud Visualizations” - Alida 2: “Using Word Clouds for Powerful Data Visualization” - WordCloud.app blog 3: “Ultimate Google Data Studio Word Cloud Guide: Visualization 2024” - AtOnce 4: “How to Create Word Cloud in Power BI” - Zebra BI

A pie chart is the best choice to show the composition between the categories of the survey response data set. A pie chart represents the whole with a circle, divided by slices into parts. Each slice shows the relative size of each category as a percentage of the total. A pie chart is useful when the categories are mutually exclusive and add up to 100%. The table shows the favorite color and the number of responses for each color, which can be easily converted into percentages. A pie chart can show how each color contributes to the total number of responses.

Option A is incorrect because a histogram is used to show how data points are distributed along a numerical scale. The survey response data set is not numerical, but categorical.

Option C is incorrect because a line chart is used to show trends or changes over time. The survey response data set does not have a time dimension.

Option D is incorrect because a scatter plot is used to show the relationship between two numerical variables. The survey response data set does not have two numerical variables.

Option E is incorrect because a waterfall chart is used to show how an initial value is increased or decreased by a series of intermediate values. The survey response data set does not have an initial value or intermediate values.

References:

• How to Choose the Right Chart for Your Data - Infogram
• How to Choose the Right Data Visualization | Tutorial by Chartio
• Find the Best Visualizations for Your Metrics - The Data School
• How to choose the best chart or graph for your data

A telephone number formatted as XXX-XXX-XXXX would be considered a text data type, as it is composed of alphanumeric characters and symbols. A numeric data type is composed of only numbers, such as integers or decimals. A date data type is composed of values that represent dates or times, such as YYYY-MM-DD or HH:MM:SS. A float data type is composed of numbers with fractional parts, such as 3.14 or 0.5. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

Answer: C. While Strategy 2 does not result in the highest sales of Product D. over all products it appears to be the most effective.

A summary statement that upholds integrity in data reporting should be accurate, unbiased, and supported by evidence. Option C is the only statement that meets these criteria, as it reflects the data shown in the bar graph without exaggerating or distorting it. Option C also acknowledges the limitation of the statement by using the word “appears”, which indicates that there may be other factors or variables that affect the sales performance.

Option A is inaccurate, as sales are not approximately equal for Product A and Product B across all strategies. Product A has higher sales than Product B in strategies 1, 3, and 5, while Product B has higher sales than Product A in strategies 2 and 4.

Option B is biased, as it does not consider the sales of different products in each strategy. Strategy 4 provides the best sales for Product B, but not for the other products. Strategy 5 has the highest total sales across all products, as shown by the black line graph.

Option D is unsupported by evidence, as it does not explain why Product D should be promoted more than the other products in all strategies. Product D has the lowest sales among all products in strategies 1, 3, and 4, and only slightly higher sales than Product C in strategies 2 and 5.

Surveys sent to 100 randomly selected homes that are reflective of the population. This is because a random sample is a type of sample that is selected by using a random method, such as a lottery or a computer-generated number, which ensures that every element in the population has an equal chance of being selected. A random sample can result in a representative sample, which means that the sample reflects the characteristics and diversity of the population. By sending surveys to 100 randomly selected homes that are reflective of the population, the analyst can ensure that the sample is representative of the county’s households and their income levels. The other sampling methods are not likely to result in a representative sample. Here is why:

A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m. would result in a biased sample, which means that the sample favors or excludes certain groups or elements in the population. By conducting the survey only between 2:00 p.m. and 3:00 p.m., the analyst would miss out on people who are not available or reachable at that time, such as those who are working or sleeping. This could affect the representativeness and generalizability of the sample.

A systematic survey that is sent to 100 single-family homes in the county would result in an unrepresentative sample, which means that the sample does not reflect the characteristics and diversity of the population. By sending surveys only to single-family homes, the analyst would ignore other types of households, such as apartments, condos, or mobile homes. This could affect the accuracy and reliability of the sample.

Surveys sent to ten randomly selected homes within 5mi (8km) of the county’s office would result in a small sample, which means that the sample size is too low to capture the variability and diversity of the population. By sending surveys only to ten homes within a limited area, the analyst would miss out on many households that are located in different parts of the county. This could affect the precision and confidence of the sample.

The term ‘4Ac1’ is a combination of numbers and letters, which fits the definition of an alphanumeric string. Alphanumeric refers to a character set that contains both letters and numbers. In data analytics and programming, such a value is typically treated as a string, which is a sequence of characters. Strings can include letters, digits, and various other symbols.

A numeric data type would only include numbers, and a float is a specific kind of numeric data type that includes decimal points, neither of which applies to ‘4Ac1’. A boolean data type represents one of two values: true or false. Since ‘4Ac1’ does not represent a true or false value, it cannot be classified as boolean. Lastly, symbolic is not a standard data type in the context of programming and data analytics.

References:

• Understanding Python 3 data types1.
• Basic Data Types in Python2.
• Java Data Types3.

Explanation

The correct answer is B: Data blending.

Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization and can then be processed or analyzed. Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question. Data append is incorrect. Data append is a process that involves adding new data elements to an existing database. An example of a common data append would be the enhancement of a company's customer files. A data append takes the information they have, matches it against a larger database of business data, allowing the desired missing data fields to be added. Normalize data is incorrect.

Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set.

This is because sales year to date refers to the sales that have occurred from the beginning of the current year until the current date. By creating a sales report that identifies sales year to date, the analyst can measure and compare the sales performance and progress of the current year. Since the analyst is asked to create the sales report on the morning of April 9, 2020, and the daily sales data is current through the end of the day, the date range that should be on the report is January 1, 2020 to April 9, 2020. The other date ranges are not correct for identifying sales year to date. Here is why:

• January 1, 2020 to April 1, 2020 would not include the sales that occurred in the first eight days of April, which would underestimate the sales year to date.
• January 1, 2020 to April 7, 2020 would not include the sales that occurred in the last two days of April, which would also underestimate the sales year to date.
• January 1, 2020 to April 8, 2020 would not include the sales that occurred on April 9, which would also underestimate the sales year to date.