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| Section | Weight | Objectives |
|---|---|---|
| Data Governance, Quality, and Management | 14% | - Data quality standards and monitoring - Data lifecycle management and documentation - Data governance frameworks and policies - Data privacy, security, and compliance |
| Data Analysis | 24% | - Analytical tools and querying languages - Exploratory and confirmatory data analysis - Data modeling and pattern recognition - Statistical methods and techniques |
| Data Concepts and Environments | 20% | - Database systems and architectures - Foundational concepts of AI and machine learning in data - Cloud and distributed data environments - Data types, structures, and formats |
| Visualization and Reporting | 20% | - Dashboard and report development - Communicating insights to stakeholders - Design principles for effective visualizations - Validation and accuracy of reports |
| Data Acquisition and Preparation | 22% | - Data collection methods and sources - Data extraction, transformation, and loading (ETL/ELT) - Data cleaning, validation, and quality improvement - Data integration and normalization |
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NEW QUESTION # 21
Which of the following best explains the purpose of data lineage?
Answer: B
Explanation:
Data lineage documents the lifecycle of data, showing how it moves and changes as it passes through systems, processes, and transformations--from initial acquisition to final reporting and consumption. It helps organizations understand where data came from, how it was modified, and whether it can be trusted for decision-making.
NEW QUESTION # 22
A data analyst is following up on a recent, company-wide data audit of customer invoice data. Which of the following is the best option for the analyst to use?
Answer: B
Explanation:
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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 23
A data analyst pulls a table similar to the following one:
ID
Type
TypeID
Phone
1
Full Time
Full Time 1
Mobile
2
Part Time
Part Time 2
Work
3
Full Time
Full Time 3
Mobile
Which of the following best explains the data issue with TypeID?
Answer: B
Explanation:
This question is part of theData Concepts and Environmentsdomain, 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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 24
A data analyst receives a notification that a customized report is taking too long to load. After reviewing the system, the analyst does not find technical or operational issues. Which of the following should the analyst try next?
Answer: B
Explanation:
This question pertains to theData Governancedomain, 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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 25
A data analyst is designing a report for the business review team. The team lists the following requirements for the report:
* Specific data points
* Color branding
* Labels and terminology
* Suggested charts and tables
Which of the following components is missing from the requirements?
Answer: B
Explanation:
This question falls under theVisualization and Reportingdomain of CompTIA Data+ DA0-002, which involves understanding the components necessary for designing a report. The given requirements cover data, visuals, and design, but a key aspect of report planning is missing.
* Source validation (Option A): Source validation ensures data accuracy, but it's typically part of the data preparation phase, not a report design requirement.
* Design elements (Option B): Color branding, labels, and terminology are design elements, so this is already included.
* Delivery method (Option C): The delivery method (e.g., recurring, ad hoc, self-service) specifies how the report will be distributed or accessed, which is a critical requirement missing from the list.
* Report type (Option D): Suggested charts and tables imply the report type (e.g., summary, dashboard), so this is indirectly covered.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and the delivery method is a key component of report planning that's missing here.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 26
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