CompTIA Data+ Exam (2025) DA0-002 Practice Questions

Answer : D

This question falls under the Data Concepts and Environments domain, focusing on understanding file formats and their structures. The task is to identify a file type that uses delimiters to separate data.

XML (Option A): XML uses tags to structure data, not delimiters.

HTML (Option B): HTML is a markup language for web pages, not a data file format using delimiters.

JSON (Option C): JSON uses key-value pairs and nested structures, not delimiters like commas.

CSV (Option D): CSV (Comma-Separated Values) uses delimiters (typically commas) to separate data fields, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' such as file formats like CSV that use delimiters.

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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 : C

This question pertains to the Data Concepts and Environments domain, focusing on the purpose of data lineage. Data lineage involves tracking the lifecycle of data.

To see the steps and path of data flow through different systems (Option A): This describes a data flow diagram, not data lineage, which focuses on transformations rather than just flow.

To better understand the granularity of data variable relationships (Option B): This relates to data modeling, not the purpose of data lineage.

To track data transformations from acquisition through reporting (Option C): Data lineage tracks the journey of data, including transformations (e.g., cleaning, aggregation) from its source to its final use in reporting, which is its primary purpose.

To look up data definitions, ensuring consistent use across business units (Option D): This describes a data dictionary, not data lineage.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and data lineage specifically tracks transformations across the data lifecycle.

Answer : D

This question pertains to the Data Analysis domain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.

Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.

Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.

Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.

Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods,' and binning is a standard technique for categorizing data for analysis.

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Answer : A

This question falls under the Data Governance domain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.

Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.

Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn't address the root cause of stale data.

Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.

Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.

The DA0-002 Data Governance domain includes 'data quality control concepts,' such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.

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Answer : A

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.

Answer : B

This question falls under the Data Governance domain, which in DA0-002 includes understanding data privacy and compliance with regulations like GDPR. The question asks which piece of information, if made public, constitutes a privacy violation, meaning it must be personally identifiable information (PII).

Gender (Option A): Gender is not typically considered PII on its own, as it's not uniquely identifiable.

Driver's license (Option B): A driver's license number is PII because it uniquely identifies an individual and can be linked to other personal information, such as name and address. Making it public violates privacy regulations.

Age (Option C): Age alone isn't PII, as it's not uniquely identifiable.

Employment status (Option D): Employment status (e.g., employed, unemployed) isn't PII, as it doesn't uniquely identify an individual.

The DA0-002 Data Governance domain includes 'identifying PII and data privacy concepts,' and a driver's license is a clear example of PII that, if exposed, results in a privacy violation.

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