Get CompTIA DA0-002 Exam Practice Questions - Real and Updated

Answer : C

This question falls under the Data Analysis domain, focusing on analytical methods for dashboards. The analyst wants to identify patterns in historical data for further study, which points to a specific type of analytics.

Predictive (Option A): Predictive analytics forecasts future outcomes, not focused on identifying patterns for further study.

Prescriptive (Option B): Prescriptive analytics provides recommendations, which goes beyond identifying patterns.

Diagnostic (Option C): Diagnostic analytics examines historical data to identify patterns, trends, and correlations, enabling further investigation, which fits the scenario.

Descriptive (Option D): Descriptive analytics summarizes what happened but doesn't focus on identifying patterns for deeper study.

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods,' and diagnostic analytics is best for pattern identification in historical data.

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Answer : B

This question pertains to the Data Governance domain, focusing on data quality validation techniques. The task is to validate that the number of records matches expectations, which requires a specific type of assessment.

Source control (Option A): Source control (e.g., Git) manages code versions, not dataset validation.

Unit test (Option B): A unit test checks a specific component of a process, such as verifying that the number of records in a dataset matches the expected count, making it the best fit.

Stress test (Option C): Stress tests evaluate system performance under load, not record counts.

Health check (Option D): A health check monitors system status but isn't specific to validating record counts.

The DA0-002 Data Governance domain includes 'data quality control concepts,' and unit tests are a standard method for validating specific data outcomes like record counts.

Answer : B

This question pertains to the Data Acquisition and Preparation domain, focusing on combining and deduplicating data using SQL. The task is to remove duplicates between two tables, meaning the analyst needs a unique set of records from both.

SELECT * FROM Employees UNION ALL SELECT * FROM Managers (Option A): UNION ALL combines all rows from both tables, including duplicates, which doesn't meet the requirement.

SELECT * FROM Employees UNION SELECT * FROM Managers (Option B): UNION combines rows from both tables and automatically removes duplicates, providing a unique set of records, which fits the task.

SELECT * FROM Employees JOIN SELECT * FROM Managers (Option C): This syntax is incorrect; a JOIN requires an ON clause, and it wouldn't remove duplicates.

SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers (Option D): A CROSS JOIN creates a Cartesian product, resulting in all possible combinations, not removing duplicates.

The DA0-002 Data Acquisition and Preparation domain includes 'executing data manipulation,' and UNION is the correct SQL operation for combining tables while removing duplicates.

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Answer : A

This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., 'Full Time 1') redundantly includes the Type value ('Full Time') with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn't applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and redundancy is a common data quality issue in schema design.

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Answer : D

This question pertains to the Data Concepts and Environments domain, focusing on the content of metadata. Metadata describes data attributes, and the task is to identify what it typically includes.

Transformations (Option A): Transformations (e.g., data cleaning steps) are part of data lineage, not metadata.

Data lineage (Option B): Data lineage tracks data flow and transformations, which is related to metadata but not a direct component.

Syntax (Option C): Syntax refers to code structure, not a metadata component.

Variable types (Option D): Metadata includes information about data fields, such as variable types (e.g., integer, string), which is a standard component.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and metadata typically contains details like variable types to describe the dataset.