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A line chart is the best visualization for displaying data trends over time. It clearly shows changes and trends in the data by connecting data points with lines, making it ideal for representing time-series data such as quarterly sales.

CompTIA Data+ Reference:

CompTIA Data+ Study Guide (Exam DA0-001), Chapter 7: Data Visualization and Reporting, Section “Choosing the Right Visualization for Time-Based Data”, Official CompTIA CertMaster Learn for Data+, Module 7.1 “Time-Series Visualization: Line Charts”.

This is because the number of datapoints increases each day by approximately 20% of the total number from the day before. Therefore, to find the number of datapoints on the next day, we can use the formula:

Plugging in the given values, we get:

Since we are dealing with whole numbers, we can round up the result to the nearest integer, which is 10,600.

When incorporating new customer information from a survey into an existing customer contacts database, the appropriate data manipulation technique is:

Option B:Append

Rationale:Appending involves adding new records to the end of an existing dataset without altering the existing data. In this scenario, the analyst adds the new customer information as additional records in the database.

Option A:Join

Rationale:Joining combines data from two or more tables based on a related column, creating a new dataset with combined information. This is not necessary when simply adding new records.

Option C:Transform

Rationale:Transformation involves changing the format, structure, or values of data. While important in data processing, it doesn't pertain to adding new records.

Option D:Blend

Rationale:Data blending combines data from different sources into a single dataset, often used in analysis rather than updating a database with new records.

This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:

=IF (condition, value_if_true, value_if_false)

The other data manipulation techniques are not examples of logical functions. Here is why:

WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:

AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:

BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is: