Exam Name: CompTIA Data+ Certification Exam (DA0-001日本語版)
Certification Provider: CompTIA
Corresponding Certification: CompTIA Data+
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Passing Score, Duration & Questions For The CompTIA DA0-001 Exam
- Time Duration: 90 minutes
- Format: Multiple choice and performance-based
- Languages: English
- Exam Length: 90 questions
- The passing score: 675 (on scale of 100-900)
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CompTIA DA0-001 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Data Concepts and Environments - 15% | |
| Identify basic concepts of data schemas and dimensions. | - Databases
- Data mart/data warehousing/data lake
- Schema concepts
- Slowly changing dimensions
|
| 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
- Data file formats
|
Data Mining - 25% | |
| Explain data acquisition concepts. | - Integration
- Data collection methods
|
| 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
- Derived variables |
| Explain common techniques for data manipulation and query optimization. | - Data manipulation
- Query optimization
|
Data Analysis - 23% | |
| Given a scenario, apply the appropriate descriptive statistical methods. | - Measures of central tendency Mean Median Mode - Measures of dispersion
- Frequencies/percentages |
| Explain the purpose of inferential statistical methods. | - t-tests - Z-score - p-values - Chi-squared - Hypothesis testing
- Simple linear regression |
| Summarize types of analysis and key analysis techniques. | - Process to determine type of analysis
- Type of analysis
|
| 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
|
| Given a scenario, use appropriate design components for reports and dashboards. | - Report cover page
- Design elements
- Documentation elements
|
| Given a scenario, use appropriate methods for dashboard development. | - Dashboard considerations
- Development process
Delivery considerations
|
| 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
- Ad-hoc/one-time report
- Tactical/research report |
Data Governance, Quality, and Controls - 14% | |
| Summarize important data governance concepts. | - Access requirements
- Security requirements
- Storage environment requirements
- Use requirements
- Entity relationship requirements
- Data classification
- Jurisdiction requirements
- Data breach reporting
|
| Given a scenario, apply data quality control concepts. | - Circumstances to check for quality
- Automated validation
- Data quality dimensions
- Data quality rule and metrics
- Methods to validate quality
|
| Explain master data management (MDM) concepts. | - Processes
- Circumstances for MDM
|
Reference: https://www.comptia.org/training/books/data-da0-001-study-guide
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CompTIA DA0-001日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Visualization and Reporting | 23% | - Communicating insights and recommendations - Selecting appropriate visualizations and charts - Building dashboards and reports - Design principles and best practices |
| Data Analysis | 23% | - Descriptive and inferential statistics - Analytical techniques: trend, performance, exploratory, comparative - Interpreting results and identifying patterns/anomalies - Statistical methods: correlation, regression, hypothesis testing |
| Data Governance, Quality and Controls | 14% | - Data lifecycle management and controls - Data privacy, security, and compliance - Data quality standards and measurement - Data governance frameworks and policies |
| Data Mining | 25% | - Querying and retrieving data - Data profiling, cleansing, and validation - Data transformation, manipulation, and enrichment - Data acquisition and integration methods |
| Data Concepts and Environments | 15% | - Data types, structures, and formats - Structured, semi-structured, and unstructured data - Data schemas, dimensions, and attributes - Data storage systems: databases, data marts, warehouses, data lakes |
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