CompTIA DA0-002 Exam With Confidence Using Practice Dumps

This question falls under the Data Concepts and Environments domain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.

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.

This question falls under the Data Analysis domain, focusing on statistical methods for forecasting. The task is to anticipate (predict) the number of daily calls, which involves looking into the future.

Predictive (Option A): Predictive analytics uses historical data to forecast future outcomes (e.g., number of calls), which matches the requirement.

Diagnostic (Option B): Diagnostic analytics identifies causes and patterns in historical data, not future predictions.

Inferential (Option C): Inferential statistics make generalizations about a population, not specific forecasts.

Descriptive (Option D): Descriptive analytics summarizes past data, not suitable for anticipating future values.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and predictive analytics is the best method for forecasting future call volumes.