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CompTIA DA0-002 Exam Syllabus Topics:

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

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CompTIA Data+ Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
Given the following tables:
Individual table
ID
FirstName
LastName
1
John
Doe
Output
ID
FullName
1
JohnDoe
Which of the following is the best option to display output from FirstName and LastName as FullName?

Answer: A

Explanation:
This question falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").
* Concatenate (Option A): Concatenation combines two or more strings into one (e.g., usingCONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.
* Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.
* Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.
* Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.


NEW QUESTION # 88
Which of the following programming languages are the most efficient at performing data mining and statistical analysis? (Choose two.)

Answer: A,C

Explanation:
R is specifically designed for statistical analysis and data mining, offering extensive statistical libraries. Python provides powerful data processing and machine learning libraries, making it highly efficient for data mining and analysis tasks.


NEW QUESTION # 89
A data analyst needs to join together a table data source and web API data source using Python. Which of the following is the best way to accomplish this task?

Answer: A


NEW QUESTION # 90
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 # 91
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: D

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 # 92
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