CompTIA Data+ Certification : DA0-001 Exam Questions

  • Exam Code: DA0-001
  • Exam Name: CompTIA Data+ Certification Exam
  • Updated: Aug 03, 2026
  • Q&As: 398 Questions and Answers

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About CompTIA Data+ Certification Exam Braindumps

CompTIA DA0-001 Exam Syllabus Topics:

TopicDetails

Data Concepts and Environments - 15%

Identify basic concepts of data schemas and dimensions.- Databases
  • Relational
  • Non-relational

- Data mart/data warehousing/data lake

  • Online transactional processing (OLTP)
  • Online analytical processing (OLAP)

- Schema concepts

  • Snowflake
  • Star

- Slowly changing dimensions

  • Keep current information
  • Keep historical and current information
Compare and contrast different data types.- Date
- Numeric
- Alphanumeric
- Currency
- Text
- Discrete vs. continuous
- Categorical/dimension
- Images
- Audio
- Video
Compare and contrast common data structures and file formats.- Structures
  • Structured
    - Defined rows/columns
    - Key value pairs
  • Unstructured
    - Undefined fields
    - Machine data

- Data file formats

  • Text/Flat file
    - Tab delimited
    - Comma delimited
  • JavaScript Object Notation (JSON)
  • Extensible Markup Language (XML)
  • Hypertext Markup Language (HTML)

Data Mining - 25%

Explain data acquisition concepts.- Integration
  • Extract, transform, load (ETL)
  • Extract, load, transform (ELT)
  • Delta load
  • Application programming interfaces (APIs)

- Data collection methods

  • Web scraping
  • Public databases
  • Application programming interface (API)/web services
  • Survey
  • Sampling
  • Observation
Identify common reasons for cleansing and profiling datasets.- Duplicate data
- Redundant data
- Missing values
- Invalid data
- Non-parametric data
- Data outliers
- Specification mismatch
- Data type validation
Given a scenario, execute data manipulation techniques.- Recoding data
  • Numeric
  • Categorical

- Derived variables
- Data merge
- Data blending
- Concatenation
- Data append
- Imputation
- Reduction/aggregation
- Transpose
- Normalize data
- Parsing/string manipulation

Explain common techniques for data manipulation and query optimization.- Data manipulation
  • Filtering
  • Sorting
  • Date functions
  • Logical functions
  • Aggregate functions
  • System functions

- Query optimization

  • Parametrization
  • Indexing
  • Temporary table in the query set
  • Subset of records
  • Execution plan

Data Analysis - 23%

Given a scenario, apply the appropriate descriptive statistical methods.- Measures of central tendency
Mean
Median
Mode
- Measures of dispersion
  • Range
    Max
    Min
  • Distribution
  • Variance
  • Standard deviation

- Frequencies/percentages
- Percent change
- Percent difference
- Confidence intervals

Explain the purpose of inferential statistical methods.- t-tests
- Z-score
- p-values
- Chi-squared
- Hypothesis testing
  • Type I error
  • Type II error

- Simple linear regression
- Correlation

Summarize types of analysis and key analysis techniques.- Process to determine type of analysis
  • Review/refine business questions
  • Determine data needs and sources to perform analysis
  • Scoping/gap analysis

- Type of analysis

  • Trend analysis
    - Comparison of data over time
  • Performance analysis
    - Tracking measurements against defined goals
    - Basic projections to achieve goals
  • Exploratory data analysis
    - Use of descriptive statistics to determine observations
  • Link analysis
    - Connection of data points or pathway
Identify common data analytics tools.- Structured Query Language (SQL)
- Python
- Microsoft Excel
- R
- Rapid mining
- IBM Cognos
- IBM SPSS Modeler
- IBM SPSS
- SAS
- Tableau
- Power BI
- Qlik
- MicroStrategy
- BusinessObjects
- Apex
- Dataroma
- Domo
- AWS QuickSight
- Stata
- Minitab

Visualization - 23%

Given a scenario, translate business requirements to form a report.- Data content
- Filtering
- Views
- Date range
- Frequency
- Audience for report
  • Distribution list
Given a scenario, use appropriate design components for reports and dashboards.- Report cover page
  • Instructions
  • Summary
    - Observations and insights

- Design elements

  • Color schemes
  • Layout
  • Font size and style
  • Key chart elements
    - Titles
    - Labels
    - Legends
  • Corporate reporting standards/style guide
    - Branding
    - Color codes
    - Logos/trademarks
    - Watermark

- Documentation elements

  • Version number
  • Reference data sources
  • Reference dates
    - Report run date
    - Data refresh date
    - Frequently asked questions (FAQs)
    - Appendix
Given a scenario, use appropriate methods for dashboard development.- Dashboard considerations
  • Data sources and attributes
    - Field definitions
    - Dimensions
    - Measures
  • Continuous/live data feed vs. static data
  • Consumer types
    - C-level executives
    - Management
    - External vendors/stakeholders
    - General public
    - Technical experts

- Development process

  • Mockup/wireframe
    - Layout/presentation
    - Flow/navigation
    - Data story planning
  • Approval granted
  • Develop dashboard
  • Deploy to production

Delivery considerations

  • Subscription
  • Scheduled delivery
  • Interactive (drill down/roll up)
    - Saved searches
    - Filtering
    - Static
    - Web interface
    - Dashboard optimization
    - Access permissions
Given a scenario, apply the appropriate type of visualization.- Line chart
- Pie chart
- Bubble chart
- Scatter plot
- Bar chart
- Histogram
- Waterfall
- Heat map
- Geographic map
- Tree map
- Stacked chart
- Infographic
- Word cloud
Compare and contrast types of reports.- Static vs. dynamic reports
  • Point-in-time
  • Real time

- Ad-hoc/one-time report
- Self-service/on demand
- Recurring reports

  • Compliance reports (e.g., financial, health, and safety)
  • Risk and regulatory reports
  • Operational reports [e.g., performance, key performance indicators (KPIs)]

- Tactical/research report

Data Governance, Quality, and Controls - 14%

Summarize important data governance concepts.- Access requirements
  • Role-based
  • User group-based
  • Data use agreements
  • Release approvals

- Security requirements

  • Data encryption
  • Data transmission
  • De-identify data/data masking

- Storage environment requirements

  • Shared drive vs. cloud based vs. local storage

- Use requirements

  • Acceptable use policy
  • Data processing
  • Data deletion
  • Data retention

- Entity relationship requirements

  • Record link restrictions
  • Data constraints
  • Cardinality

- Data classification

  • Personally identifiable information (PII)
  • Personal health information (PHI)
  • Payment card industry (PCI)

- Jurisdiction requirements

  • Impact of industry and governmental regulations

- Data breach reporting

  • Escalate to appropriate authority
Given a scenario, apply data quality control concepts.- Circumstances to check for quality
  • Data acquisition/data source
  • Data transformation/intrahops
    - Pass through
    - Conversion
  • Data manipulation
  • Final product (report/dashboard, etc.)

- Automated validation

  • Data field to data type validation
  • Number of data points

- Data quality dimensions

  • Data consistency
  • Data accuracy
  • Data completeness
  • Data integrity
  • Data attribute limitations

- Data quality rule and metrics

  • Conformity
  • Non-conformity
  • Rows passed
  • Rows failed

- Methods to validate quality

  • Cross-validation
  • Sample/spot check
  • Reasonable expectations
  • Data profiling
  • Data audits
Explain master data management (MDM) concepts.- Processes
  • Consolidation of multiple data fields
  • Standardization of data field names
  • Data dictionary

- Circumstances for MDM

  • Mergers and acquisitions
  • Compliance with policies and regulations
  • Streamline data access

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