CompTIA DA0-002 Exam Dumps

This question falls under the Data Governance domain 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.

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This question pertains to the Data Analysis domain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal, e.g., ratings), requiring appropriate data attributes.

Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.

Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.

Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.

Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods,' and numerical and ordinal attributes are key for forecasting sales and satisfaction.

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This question pertains to the Data Governance domain, focusing on data quality and report performance optimization. The report is slow despite no technical issues, suggesting a data-related inefficiency.

Check that the appropriate filters are applied (Option A): Applying filters reduces the dataset size by excluding irrelevant data, improving report performance. This is a logical next step after ruling out technical issues.

Check data source connections (Option B): The analyst already reviewed the system and found no operational issues, so connectivity is likely not the problem.

Check for data structure changes in the report (Option C): While possible, this is a deeper investigation step and less likely to be the immediate cause of slowness.

Check whether other peers have the same issue (Option D): This might confirm the issue's scope but doesn't directly address the performance problem.

The DA0-002 Data Governance domain emphasizes 'data quality control concepts,' including optimizing report performance through techniques like filtering.

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This question is part of the Visualization and Reporting domain, focusing on delivery methods for dashboards. The requirement for displaying financial transactions 'as they are updated' implies a need for real-time updates and interactivity, which narrows down the options.

Real-time (Option A): Real-time delivery ensures the dashboard reflects the latest data as transactions are updated, meeting the requirement.

Snapshot (Option B): A snapshot provides a static view at a specific point, not suitable for ongoing updates.

Dynamic (Option C): A dynamic dashboard allows for interactivity and can be updated as data changes, complementing real-time delivery.

Static (Option D): Static dashboards don't update automatically, making this incorrect.

Ad hoc (Option E): Ad hoc delivery is for one-time reports, not ongoing updates.

Time series (Option F): Time series refers to a data type or visualization, not a delivery method.

The DA0-002 Visualization and Reporting domain includes understanding 'the appropriate visualization in the form of a report or dashboard' with delivery methods Real-time and dynamic methods best support the need for updated financial transaction dashboards.

This question pertains to the Data Concepts and Environments domain, focusing on data sources that facilitate online data access. The task is to identify a source that simplifies online data consumption.

Data mart (Option A): A data mart stores structured data for specific business areas, typically accessed internally, not designed for online consumption.

Web scraping (Option B): Web scraping extracts data from websites but requires parsing and cleaning, which isn't necessarily 'easier.'

Database (Option C): Databases store data but aren't inherently designed for online consumption without an interface.

Application programming interface (Option D): An API provides a structured way to access data online, often in formats like JSON, making data consumption easier for applications and users.

The DA0-002 Data Concepts and Environments domain includes understanding 'data sources,' and APIs are specifically designed to simplify online data access.

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