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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Acquisition and Preparation | 22% | - Data extraction, transformation, and loading (ETL/ELT) - Data collection methods and sources - Data cleaning, validation, and quality improvement - Data integration and normalization |
| Topic 2: Data Concepts and Environments | 20% | - Foundational concepts of AI and machine learning in data - Cloud and distributed data environments - Data types, structures, and formats - Database systems and architectures |
| Topic 3: Visualization and Reporting | 20% | - Communicating insights to stakeholders - Dashboard and report development - Validation and accuracy of reports - Design principles for effective visualizations |
| 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: Data Governance, Quality, and Management | 14% | - Data governance frameworks and policies - Data quality standards and monitoring - Data lifecycle management and documentation - Data privacy, security, and compliance |
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NEW QUESTION # 36
A data analyst is helping a company achieve its strategic goals. Which of the following types of analytics should the analyst incorporate into the dashboards and tools delivered to the company?
Answer: A
Explanation:
Prescriptive analytics provides recommended actions based on data insights, directly supporting decision-making and helping organizations achieve their strategic goals.
NEW QUESTION # 37
A data analyst wants to use the following tables to find all the customers who have not placed an order:
Which of the following SQL statements is the best way to accomplish this task?
Answer: D
NEW QUESTION # 38
A data analyst is creating a report that provides information to parents and residents about the school distinct and its individual schools. Which of the following statistical methods is best suited for reporting this information?
Answer: C
Explanation:
Descriptive statistics are used to summarize and present existing data in a meaningful way. Since the report is intended to provide information about the school district and its individual schools, descriptive methods are best suited to present current facts, metrics, and characteristics without making predictions or drawing conclusions about a larger population.
NEW QUESTION # 39
A data analyst creates a report that identifies the middle 50% of the collected data. Which of the following best describes the analyst's findings?
Answer: A
Explanation:
This question pertains to theData Analysisdomain, focusing on statistical measures. The middle 50% of a dataset refers to a specific statistical concept related to data distribution.
* Interquartile range (Option A): The interquartile range (IQR) is the range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile), representing the middle 50% of the data, which matches the description.
* The difference between mode and median (Option B): This measures the spread between two central tendency metrics but doesn't represent the middle 50% of the data.
* Mean variance (Option C): Variance measures data dispersion around the mean, not the middle 50%.
* Skewness from the slope (Option D): Skewness measures data asymmetry, and "slope" is irrelevant here.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the IQR is the standard measure for the middle 50% of a dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 40
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 # 41
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