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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance, Quality, and Management | 14% | - Data quality standards and monitoring - Data privacy, security, and compliance - Data governance frameworks and policies - Data lifecycle management and documentation |
| Topic 2: Data Acquisition and Preparation | 22% | - Data cleaning, validation, and quality improvement - Data extraction, transformation, and loading (ETL/ELT) - Data collection methods and sources - Data integration and normalization |
| Topic 3: 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 |
| Topic 4: Data Analysis | 24% | - Statistical methods and techniques - Exploratory and confirmatory data analysis - Analytical tools and querying languages - Data modeling and pattern recognition |
| Topic 5: Visualization and Reporting | 20% | - Communicating insights to stakeholders - Validation and accuracy of reports - Design principles for effective visualizations - Dashboard and report development |
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NEW QUESTION # 155
Software end users are happy with the quality of product support provided. However, they frequently raise concerns about the long wait time for resolutions. An IT manager wants to improve the current support process. Which of the following should the manager use for this review?
Answer: A
NEW QUESTION # 156
Which of the following elements is the most important to include in a dashboard for internal technical audiences?
Answer: B
Explanation:
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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 157
A company's entire server environment is located at the company's headquarters. Which of the following describes this type of environment?
Answer: B
Explanation:
An on-premises environment means that the organization's servers, hardware, and supporting infrastructure are physically located and managed at the company's own facilities. Since the entire server environment is housed at the company's headquarters, it is an on-premises deployment.
NEW QUESTION # 158
A data analyst is creating a high-level report for the Chief Executive Officer. Which of the following data would most likely be included in the report?
Answer: C
Explanation:
Year-to-date sales numbers provide high-level performance insights appropriate for executive decision-making.
NEW QUESTION # 159
A data analyst calculated the average score per student without making any changes to the following table:
Student
Subject
Score
123
Math
100
123
Biology
80
234
Math
96
123
Biology
80
345
Biology
88
234
Math
96
Which of the following exploration techniques should the analyst have considered before calculating the average?
Answer: D
Explanation:
This question pertains to theData Governancedomain, focusing on data quality issues that affect analysis.
The table contains duplicate rows, which would skew the average score calculation if not addressed.
* Student 123: Math (100), Biology (80), Biology (80)- Duplicate Biology score.
* Student 234: Math (96), Math (96)- Duplicate Math score.
* Student 345: Biology (88)- No duplicates.
* Duplication (Option A): The table has duplicate rows (e.g., Student 123's Biology score of 80 appears twice), which would inflate the average if not removed. The analyst should have checked for duplicates before calculating the average.
* Redundancy (Option B): Redundancy refers to unnecessary fields (e.g., storing the same data in multiple columns), not duplicate rows.
* Binning (Option C): Binning groups data into categories, not relevant for addressing duplicates in averaging.
* Grouping (Option D): Grouping (e.g., GROUP BY in SQL) might be part of the solution,but the issue to identify is duplication.
The DA0-002 Data Governance domain includes "data quality control concepts," and checking for duplication is critical to ensure accurate calculations like averages.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 160
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