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CompTIA DA0-002 Exam Syllabus Topics:

SectionWeightObjectives
Data Mining25%- Data enrichment and integration
  • 1. Derived columns and calculated fields
  • 2. Joining and merging datasets
  • 3. Data mapping and lineage
- Data acquisition and collection
  • 1. Data sources and ingestion methods
  • 2. ETL/ELT processes
  • 3. APIs and connectors
- Data preparation and cleansing
  • 1. Data normalization and transformation
  • 2. Data cleaning techniques
  • 3. Handling missing values and duplicates
Data Analysis24%- Analysis tools and techniques
  • 1. Libraries and functions for analysis
  • 2. SQL for data analysis
  • 3. BI platforms and notebooks
- Descriptive statistics
  • 1. Data distributions
  • 2. Measures of central tendency
  • 3. Measures of dispersion
- Inferential statistics
  • 1. Correlation and regression basics
  • 2. Hypothesis testing
  • 3. Confidence intervals
Data Concepts and Environments20%- Data structures and formats
  • 1. Structured, semi-structured, and unstructured data
  • 2. File types and their use cases
  • 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. Data types (string, numeric, Boolean, date)
  • 2. Metadata and its role in data management
Visualization15%- Dashboard and report design
  • 1. Visualization best practices
  • 2. Chart types and their applications
  • 3. Dashboard components and layout
- Storytelling with data
  • 1. Audience-appropriate visuals
  • 2. Narrative techniques for data communication
  • 3. Executive summaries and key insights
Data Governance, Quality, and Controls16%- Data quality management
  • 1. Quality assessment and monitoring
  • 2. Remediation strategies
  • 3. Quality dimensions (accuracy, completeness, consistency)
- Security and privacy
  • 1. Foundational AI considerations in data lifecycle
  • 2. Access controls and encryption
  • 3. Audit trails and logging
  • 4. Data classification and protection
- Data governance frameworks
  • 1. Compliance requirements (GDPR, CCPA, etc.)
  • 2. Policies, standards, and procedures
  • 3. Data stewardship and ownership

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CompTIA Data+ Exam DA0-002 Prüfungsfragen mit Lösungen (Q190-Q195):

190. Frage
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?

Antwort: D


191. Frage
A data analyst wants to understand several datasets at the variable level. Which of the following should the analyst consult to find this information?

Antwort: D

Begründung:
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.


192. Frage
A data analyst is creating a report about cars sold within the last twelve months. The current data set only contains information from the last ten months. Which of the following is the most appropriate way to accomplish this task?

Antwort: A


193. Frage
Which of the following pieces of information, if made public, results in a data privacy violation?

Antwort: B

Begründung:
This question falls under theData Governancedomain, which in DA0-002 includes understanding data privacy and compliance with regulations like GDPR. The question asks which piece of information, if made public, constitutes a privacy violation, meaning it must be personally identifiable information (PII).
* Gender (Option A): Gender is not typically considered PII on its own, as it's not uniquely identifiable.
* Driver's license (Option B): A driver's license number is PII because it uniquely identifies an individual and can be linked to other personal information, such as name and address. Making it public violates privacy regulations.
* Age (Option C): Age alone isn't PII, as it's not uniquely identifiable.
* Employment status (Option D): Employment status (e.g., employed, unemployed) isn't PII, as it doesn't uniquely identify an individual.
The DA0-002 Data Governance domain includes "identifying PII and data privacy concepts," and a driver's license is a clear example of PII that, if exposed, results in a privacy violation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


194. Frage
A reporting team needs to update communication decks that were shared internally so that the decks can be shared with external partners. Which of the following elements is the most important update?

Antwort: A

Begründung:
A watermark is the most important update when preparing internal communication decks for external distribution because it helps ensure that confidential, internal-use-only, or proprietary information is properly identified and managed. Removing or updating the watermark prevents confusion about the intended audience and the sensitivity of the content being shared externally.


195. Frage
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