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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance, Quality, and Management | 14% | - Data quality standards and monitoring - Data lifecycle management and documentation - Data privacy, security, and compliance - Data governance frameworks and policies |
| Topic 2: Data Acquisition and Preparation | 22% | - Data extraction, transformation, and loading (ETL/ELT) - Data integration and normalization - Data cleaning, validation, and quality improvement - Data collection methods and sources |
| Topic 3: Data Concepts and Environments | 20% | - Data types, structures, and formats - Cloud and distributed data environments - Database systems and architectures - Foundational concepts of AI and machine learning in data |
| Topic 4: Visualization and Reporting | 20% | - Dashboard and report development - Design principles for effective visualizations - Communicating insights to stakeholders - Validation and accuracy of reports |
| Topic 5: Data Analysis | 24% | - Data modeling and pattern recognition - Exploratory and confirmatory data analysis - Statistical methods and techniques - Analytical tools and querying languages |
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NEW QUESTION # 112
Which of the following best describes an assessment a data analyst would use to validate that the number of records in a dataset matches the expected results?
Answer: B
Explanation:
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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 113
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: D
NEW QUESTION # 114
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?
Answer: C
Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 115
A data analyst wants to understand several datasets at the variable level. Which of the following should the analyst consult to find this information?
Answer: B
Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on documentation for understanding datasets. The analyst needs variable-level details (e.g., field definitions), which points to a specific type of documentation.
* Data lineage (Option A): Data lineage tracks data flow and transformations, not variable definitions.
* Data dictionary (Option B): A data dictionary provides detailed information about variables (e.g., name, type, description), which is exactly what the analyst needs.
* Data flow diagram (Option C): A data flow diagram shows data movement, not variable-level details.
* Data versioning (Option D): Data versioning tracks changes to datasets, not variable definitions.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is the primary tool for variable-level information.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 116
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 # 117
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