CompTIA DA0-001 Exam Topics
CompTIA DA0-001 Exam Overview :
| Exam Name: | CompTIA Data+ Certification Exam |
| Exam Code: | DA0-001 |
| Certifications: | CompTIA Data+ Certification |
| See Expected Questions: | CompTIA DA0-001 Expected Questions in Actual Exam |
CompTIA DA0-001 Exam Objectives :
| Section | Weight | Objectives |
|---|---|---|
| 1.0 Data Concepts and Environments | 15% | 1.1 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 1.2 Compare and contrast different data types. • Date • Numeric • Alphanumeric • Currency • Text • Discrete vs. continuous • Categorical/dimension • Images • Audio • Video 1.3 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) |
| 2.0 Data Mining | 25% | 2.1 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 2.2 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 2.3 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 2.4 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 |
| 3.0 Data Analysis | 23% | 3.1 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 3.2 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 3.3 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 3.4 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 |
| 4.0 Visualization | 23% | 4.1 Given a scenario, translate business requirements to form a report. • Data content • Filtering • Views • Date range • Frequency • Audience for report - Distribution list 4.2 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 4.3 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 4.4 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 4.5 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 | 14% | 5.1 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 5.2 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 5.3 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 |
| Official Information | https://www.comptia.org/training/books/data-da0-001-study-guide |
Updates in the CompTIA DA0-001 Exam Topics:
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