Free Practice Questions for CompTIA DA0-002 Exam

This question pertains to theVisualization and Reportingdomain, focusing on dashboard design for specific audiences. Internal technical audiences (e.g., data analysts, IT staff) need actionable, data-driven insights.

Methodology section (Option A): Methodology is important for research reports, not dashboards, especially for technical audiences who prioritize data.

Dynamic features (Option B): Dynamic features (e.g., interactivity) are useful but not the most critical element for technical audiences.

Key performance indicators (Option C): KPIs provide critical metrics (e.g., system uptime, error rates) that technical audiences need to monitor and act on, making this the most important element.

Company branding (Option D): Branding is more relevant for external audiences, not internal technical ones.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and KPIs are essential for technical dashboards.

This question pertains to theData Governancedomain, 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 theData Acquisition and Preparationdomain, focusing on combining data from multiple tables. The analyst needs specific columns from each table, suggesting a method to combine data horizontally based on relationships.

Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining columns from tables.

Union (Option B): Union stacks tables vertically, requiring identical structures, but the analyst needs specific columns, likely based on relationships, not a vertical stack.

Nest (Option C): Nesting is used for hierarchical data (e.g., JSON), not for combining relational tables.

Join (Option D): A join (e.g., INNER JOIN) combines tables horizontally based on a common key, allowing the analyst to select specific columns from each table, which fits the task.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and joining tables is the best method for combining specific columns from multiple tables.

This question is part of theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.

Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.

Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.

Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.

Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.

This question falls under theData Acquisition and Preparationdomain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.

Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.

Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.

Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.

Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.

This question falls under theData Governancedomain 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 pertains to theData Governancedomain, focusing on ensuring data quality and reliability in dashboards over time. Daily troubleshooting indicates a recurring issue, and the task is to validate data moving forward.

Inquiring about structure changes (Option A): This might identify past issues but doesn’t provide ongoing validation.

Setting up monitoring alerts (Option B): Monitoring alerts can automatically notify the analyst of data issues (e.g., missing updates, errors), providing a proactive way to validate data continuously.

Reaching out to users daily (Option C): This is inefficient and reactive, not a sustainable validation method.

Rebuilding the dashboard (Option D): Rebuilding might fix current issues but doesn’t ensure future validation.

The DA0-002 Data Governance domain includes "data quality control concepts," such as implementing monitoring to ensure data reliability in dashboards.

This question pertains to theVisualization and Reportingdomain, focusing on the initial steps in dashboard creation. The analyst is starting a project for multiple teams, so understanding the audience is critical.

Evaluate the user persona type for the dashboards (Option A): User personas define the needs, roles, and preferences of the audience (e.g., executives vs. analysts), which is the firststep to ensure the dashboards meet user requirements.

Determine the number of team members who need to access the dashboards (Option B): This is important for access control but not the first step in design.

Determine the delivery method of the dashboards (Option C): Delivery method (e.g., self-service) is decided later, after understanding user needs.

Evaluate the KPIs for the dashboards (Option D): KPIs are defined after understanding the user personas and their goals.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and evaluating user personas is the first step in this process.

This question falls under theData Governancedomain, focusing on data access and security troubleshooting. The analyst reset their password, but the recurring analysis failed to log in, indicating a mismatch.

The company changed its database authentication method (Option A): This would affect all users, not just the analyst, and there’s no indication of a broader change.

The password expiration process locked the account (Option B): The analyst reset the password as required, so the account isn’t likely locked due to expiration.

The analyst did not change the password used to launch the report (Option C): Recurring analyses often use stored credentials. If the analyst updated their password but didn’t update the stored credentials for the analysis, the login would fail, making this the most likely reason.

The company is experiencing issues with password replication (Option D): This is possible but less likely without evidence of broader system issues.

The DA0-002 Data Governance domain includes "data privacy concepts," and ensuring stored credentials match updated passwords is a common issue in recurring analyses.

This question falls under theData Governancedomain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.

PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn’t specified.

GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It’s the most relevant for a company-wide data audit.

ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.

PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.

The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.