CompTIA DA0-001 Exam Topics
CompTIA DA0-001 Exam
CompTIA Data+ Certification Exam
Total Questions: 262What is Included in the CompTIA DA0-001 Exam?
Authentic information about the syllabus is essential to go through the CompTIA DA0-001 exam in the first attempt. Study4Exam provides you with comprehensive information about CompTIA DA0-001 exam topics listed in the official syllabus. You should get this information at the start of your preparation because it helps you make an effective study plan. We have designed this CompTIA Data+ certification exam preparation guide to give the exam overview, practice questions, practice test, prerequisites, and information about exam topics that help to go through the CompTIA Data+ Certification Exam exam. We recommend you use our preparation material to cover the entire CompTIA DA0-001 exam syllabus. Study4Exam offers 3 formats of CompTIA DA0-001 exam preparation material. Each format provides new practice questions in PDF format, web-based and desktop practice exams to get passing marks in the first attempt.
CompTIA DA0-001 Exam Overview :
| Exam Name | CompTIA Data+ Certification Exam |
| Exam Code | DA0-001 |
| Official Information | https://www.comptia.org/training/books/data-da0-001-study-guide |
| See Expected Questions | CompTIA DA0-001 Expected Questions in Actual Exam |
| Take Self-Assessment | Use CompTIA DA0-001 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
CompTIA DA0-001 Exam Topics :
| 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 |
Updates in the CompTIA DA0-001 Exam Topics:
CompTIA DA0-001 exam questions and practice test are the best ways to get fully prepared. Study4exam's trusted preparation material consists of both practice questions and practice test. To pass the actual CompTIA Data+ DA0-001 exam on the first attempt, you need to put in hard work on these questions as they cover all updated CompTIA DA0-001 exam topics included in the official syllabus. Besides studying actual questions, you should take the CompTIA DA0-001 practice test for self-assessment and actual exam simulation. Revise actual exam questions and remove your mistakes with the CompTIA Data+ Certification Exam DA0-001 exam practice test. Online and Windows-based formats of the DA0-001 exam practice test are available for self-assessment.
Our Features
- 50000+ Customers feedbacks involved in Products
- Customize your exam based on your objectives
- User-Friendly interface
- Exam History and Progress reports
- Self-Assessment Features
- Various Learning Modes