CompTIA Data+ DA0-001
1.0 Data Concepts and Environments
2.0 Data Mining
3.0 Data Analysis
4.0 Visualization
5.0 Data Governance, Quality, and Controls
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CompTIA Data+ Certification Exam Objectives
EXAM NUMBER: DA0-001
About the Exam
Candidates are encouraged to use this document to help prepare for the CompTIA Data+ (DA0- 001) certification exam. This exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions by:
• Mining data
• Manipulating data
• Applying basic statistical methods
• Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
This is equivalent to 18–24 months of hands-on experience working in a business intelligence report/data analyst job role. These content examples are meant to clarify the test objectives and should not be construed as a comprehensive listing of all the content of this examination.
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PLEASE NOTE
The lists of examples provided in bulleted format are not exhaustive lists. Other examples of technologies, processes, or tasks pertaining to each objective may also be included on the exam although not listed or covered in this objectives document. CompTIA is constantly reviewing the content of our exams and updating test questions to be sure our exams are current and the security of the questions is protected. When necessary, we will publish updated exams based on testing exam objectives. Please know that all related exam preparation materials will still be valid.
TEST DETAILS
Required exam DA0-001
Number of questions 90
Types of questions Multiple choice and performance-based Length of test 90 minutes
Recommended experience • 18–24 months of experience in a report/business
analyst job role
• Exposure to databases and analytical tools
• Basic understanding of statistics
• Data visualization experience
Passing score 675 (on scale of 100–900)
EXAM OBJECTIVES (DOMAINS)
The table below lists the domains measured by this examination and the extent to which they are represented:
1.0 Data Concepts and Environments 15%
2.0 Data Mining 25%
3.0 Data Analysis 23%
4.0 Visualization 23%
5.0 Data Governance, Quality, and Controls 14%
Total 100%
1.0 Data Concepts and Environments
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)
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
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.
(The intent of this objective is NOT to test specific vendor feature sets nor the purposes of the 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
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
4.0 Visualization
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
5.0 Data Governance, Quality, and Controls
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
CompTIA Data+ (DA0-001) Acronym List
The following is a list of acronyms that appear on the CompTIA Data+ exam. Candidates are encouraged to review the complete list and attain a working knowledge of all listed acronyms as a part of a comprehensive exam preparation program.
ACRONYM DEFINITION
API Application Programming Interface
AWS Amazon Web Services
BI Business Intelligence
ELT Extract, Load, Transform
ETL Extract, Transform, Load
FAQs Frequently Asked Questions
GDPR General Data Protection Regulation HTML Hypertext Markup Language
JSON JavaScript Object Notation
KPI Key Performance Indicator
MDM Master Data Management
OLAP Online Analytical Processing
OLTP Online Transaction Processing
P&L Profit and Loss
PCI Payment Card Industry
PHI Personal Health Information
PII Personally Identifiable Information RDBMS Relational Database Management System SDLC Software Development Life Cycle
SQL Structured Query Language
XML Extensible Markup Language
CompTIA Data+ Proposed Hardware and Software List
CompTIA has included this sample list of hardware and software to assist candidates as they prepare for the Data+ exam. This list may also be helpful for training companies that wish to create a lab component for their training
offering. The bulleted lists below each topic are samples and are not exhaustive.
HARDWARE
• Desktop/laptop
- High processing power for large volume analyses
- Lower processing power for smaller volume analyses
• Internet access
• Cloud environment
SOFTWARE
• SQL environment to run scripts (SQL Lite, Management Studio, etc.)
• Eclipse
• Anaconda
• R Studio
• Database modeling tool
• Microsoft Office Suite
• Visualization tools (Tableau, Power BI, etc.)
• Reporting tools
• Sample datasets (Kaggle)
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