Changed DA0-002 Exam Questions

This question falls under the Data Analysis domain, focusing on identifying data quality issues. The table shows counts over time, and the analyst needs to flag an issue in the data.

Completeness (Option A): Completeness refers to missing data, but all rows have values for DateTime and Count.

Outlier (Option B): The count of 245 on 2024-01-02 is significantly higher than other counts (12-13), indicating an outlier that should be investigated for accuracy.

Mismatch (Option C): Mismatch implies inconsistent data types or formats, but the DateTime and Count columns appear consistent except for the last row (addressed separately).

Duplication (Option D): Duplication refers to identical rows, but no rows are identical (same DateTime and Count).

The last row ("00:00:00", 12) has a formatting issue, but the most significant issue for analysis is the outlier (245), as it could skew results. The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as identifying outliers in datasets.

This question falls under the Data Analysis domain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.

Monday: 455

Tuesday: 346

Wednesday: 382

Thursday: 563

Friday: 887

Saturday: 934

Sunday: 346

The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.

Option A: 346 – Correct, as it’s the most frequent value.

Option B: 446 – Incorrect, as 446 doesn’t appear in the dataset.

Option C: 455 – Incorrect, as 455 appears only once.

Option D: 559 – Incorrect, as 559 doesn’t appear in the dataset.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, focusing on troubleshooting data quality issues in dashboards. The dashboard isn’t loading properly, indicating a potential issue with the data connection or configuration.

Review the data layer and data source (Option A): The data layer (e.g., queries, connections) and data source (e.g., database) are the foundation of a dashboard. If the information isn’t loading, the issue likely lies in the data connection or query configuration, making this the first step.

Validate that the database is up-to-date (Option B): While this might be a subsequent step, it assumes the connection is working, which should be confirmed first.

Check that the program is updated to the latest version (Option C): Software updates might fix bugs, but this isn’t the most immediate cause of data not loading.

Ensure the correct filters are displaying on the dashboard (Option D): Filters affect data display, but if the data isn’t loading at all, the issue is more fundamental.

The DA0-002 Data Governance domain includes "data quality control concepts," and reviewing the data layer and source is the primary step in troubleshooting dashboard loading issues.

This question falls under the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores data in its original, unaltered form.

Data lake (Option A): A data lake stores raw, unaltered data in its native format (structured, semi-structured, or unstructured), making it the correct choice.

Data warehouse (Option B): A data warehouse stores processed, structured data, often transformed for analysis, not unaltered.

Data table (Option C): A data table is a structure within a database, not a repository, and may contain altered data.

Data factory (Option D): A data factory (e.g., Azure Data Factory) is a data integration service, not a repository for storing data.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed to store unaltered data.