PDF Version DemoCompTIA DA0-001日本語 Exam Overview:
| Certification Vendor: | CompTIA |
|---|---|
| Exam Name: | CompTIA Data+ Certification Exam |
| Exam Number: | DA0-001 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple Choice, Performance-Based Questions |
| Available Languages: | English, Thai, Japanese |
| Exam Price: | $255 USD |
| Passing Score: | 675 (on a scale of 100–900) |
| Real Exam Qty: | Maximum of 90 |
| Exam Duration: | 90 minutes |
| Related Certifications: | CompTIA Data+ |
| Sample Questions: | CompTIA DA0-001日本語 Sample Questions |
| Exam Way: | Online or at a Pearson VUE testing center |
| Pre Condition: | CompTIA recommends 18–24 months of experience in a report/business analyst job role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience. |
| Official Syllabus URL: | https://www.comptia.org/certifications/data |
Understanding CompTIA DA0-001 Exam Topics
Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
Visualizing and reporting data
Mining data
Manipulating data
Applying basic statistical methods
Reference: https://www.comptia.org/training/books/data-da0-001-study-guide
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CompTIA Data+ Exam Certification Details:
| Schedule Exam | Pearson VUE |
| Duration | 90 mins |
| Number of Questions | 90 |
| Exam Price | $239 (USD) |
| Exam Name | CompTIA Data+ |
| Sample Questions | CompTIA Data+ Sample Questions |
| Passing Score | 675 / 900 |
| Books / Training | CompTIA Data+ Certification Training |
| Exam Code | DA0-001 |
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
|
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