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| Section | Weight | Objectives |
|---|
| Data Governance, Quality, and Controls | 16% | - Data governance frameworks
- 1. Compliance requirements (GDPR, CCPA, etc.)
- 2. Policies, standards, and procedures
- 3. Data stewardship and ownership
- Security and privacy
- 1. Data classification and protection
- 2. Foundational AI considerations in data lifecycle
- 3. Audit trails and logging
- 4. Access controls and encryption
- Data quality management
- 1. Quality assessment and monitoring
- 2. Quality dimensions (accuracy, completeness, consistency)
- 3. Remediation strategies
|
| Data Concepts and Environments | 20% | - Data structures and formats
- 1. File types and their use cases
- 2. Structured, semi-structured, and unstructured data
- 3. Common data formats (JSON, XML, CSV)
- Database fundamentals
- 1. Relational vs. non-relational databases
- 2. Cloud and containerized data environments
- 3. Schemas and data models
- Data types and metadata
- 1. Metadata and its role in data management
- 2. Data types (string, numeric, Boolean, date)
|
| Visualization | 15% | - Storytelling with data
- 1. Executive summaries and key insights
- 2. Narrative techniques for data communication
- 3. Audience-appropriate visuals
- Dashboard and report design
- 1. Visualization best practices
- 2. Dashboard components and layout
- 3. Chart types and their applications
|
| Data Mining | 25% | - Data preparation and cleansing
- 1. Data cleaning techniques
- 2. Handling missing values and duplicates
- 3. Data normalization and transformation
- Data enrichment and integration
- 1. Derived columns and calculated fields
- 2. Data mapping and lineage
- 3. Joining and merging datasets
- Data acquisition and collection
- 1. APIs and connectors
- 2. Data sources and ingestion methods
- 3. ETL/ELT processes
|
| Data Analysis | 24% | - Descriptive statistics
- 1. Measures of dispersion
- 2. Data distributions
- 3. Measures of central tendency
- Analysis tools and techniques
- 1. SQL for data analysis
- 2. BI platforms and notebooks
- 3. Libraries and functions for analysis
- Inferential statistics
- 1. Confidence intervals
- 2. Correlation and regression basics
- 3. Hypothesis testing
|
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CompTIA Data+ Exam Sample Questions (Q175-Q180):
NEW QUESTION # 175
A data analyst receives a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees' birth months. Which of the following inconsistencies should the analyst identify?
- A. Equivalence
- B. Parallel
- C. Duplication
- D. Redundancy
Answer: D
NEW QUESTION # 176
A project manager requests an unscheduled report that provides a list of clients. Which of the following frequencies is best for this report?
- A. Annual
- B. Weekly
- C. Ad hoc
- D. Daily
Answer: C
Explanation:
An ad hoc report is created on demand to satisfy a specific, unscheduled request. Since the project manager requested a report outside the regular reporting schedule, an ad hoc report is the most appropriate choice.
NEW QUESTION # 177
A data analyst receives a request for the current employee head count and runs the following SQL statement:
SELECT COUNT(EMPLOYEE_ID) FROM JOBS
The returned head count is higher than expected because employees can have multiple jobs.
Which of the following should return an accurate employee head count?
- A. SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS
- B. SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS
- C. SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
- D. SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
Answer: C
Explanation:
Counting distinct employee IDs returns each employee only once, even if the employee appears in multiple job records, which produces an accurate head count.
NEW QUESTION # 178
A developer builds an online survey that requires all questions to have an answer. Which of the following inconsistencies does this setting prevent?
- A. Data corruption
- B. Completeness
- C. Missing values
- D. Duplication
Answer: C
Explanation:
This question pertains to theData Governancedomain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.
* Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.
* Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.
* Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.
* Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.
The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 179
Before distributing a report, a marketing analyst notices that the total distinct promotional email messages is less than the combined total of emails sent. Which of the following is the most likely reason for this difference?
- A. A recipient received duplicate emails.
- B. Some emails were not delivered.
- C. The aggregation did not include all emails.
- D. The report failed to run properly.
Answer: A
NEW QUESTION # 180
......
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