PDF Version DemoPassing Score, Duration & Questions For The CompTIA DA0-001 Exam
- The passing score: 675 (on scale of 100-900)
- Exam Length: 90 questions
- Languages: English
- Time Duration: 90 minutes
- Format: Multiple choice and performance-based
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CompTIA Data+ Exam Certification Details:
| Passing Score | 675 / 900 |
| Exam Price | $239 (USD) |
| Schedule Exam | Pearson VUE |
| Duration | 90 mins |
| Number of Questions | 90 |
| Books / Training | CompTIA Data+ Certification Training |
| Exam Name | CompTIA Data+ |
| Sample Questions | CompTIA Data+ Sample Questions |
| Exam Code | DA0-001 |
Reference: https://www.comptia.org/training/books/data-da0-001-study-guide
For more info read reference:
CompTIA DA0-001 Exam Reference
Is The CompTIA DA0-001 Exam Worth It
CompTIA Data+ exam is worth it, but only if you are looking for a career in IT. If you are just looking for a career, then it is not worth it. The CompTIA Data+ certification is not about learning how to use the computer, it is about understanding the system and how to use it effectively. It takes many years of training and experience to become proficient at using this type of software.
The CompTIA Data+ exam is designed to screen out candidates who have no concept of what they are doing when they work with computers. It requires that you have some knowledge of what the different parts of a computer do, how they work together and how they interact with each other. It also requires that you have some basic familiarity with networking systems and networks in general as well as knowledge about security issues related to computers and networks in general. CompTIA DA0-001 exam dumps are the key of success.
The CompTIA Data+ certification is an excellent starting point for anyone who wants to learn more about IT but does not have any formal training or education in this area yet. It can serve as a great entry point into the world of technology if you have already been exposed to computers but don't know much about them.
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CompTIA DA0-001 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Concepts and Environments | 15% | - Data schemas, dimensions, and attributes - Structured, semi-structured, and unstructured data - Data types, structures, and formats - Data storage systems: databases, data marts, warehouses, data lakes |
| Data Analysis | 23% | - Interpreting results and identifying patterns/anomalies - Statistical methods: correlation, regression, hypothesis testing - Analytical techniques: trend, performance, exploratory, comparative - Descriptive and inferential statistics |
| Visualization and Reporting | 23% | - Building dashboards and reports - Design principles and best practices - Communicating insights and recommendations - Selecting appropriate visualizations and charts |
| Data Mining | 25% | - Data profiling, cleansing, and validation - Data acquisition and integration methods - Querying and retrieving data - Data transformation, manipulation, and enrichment |
| Data Governance, Quality and Controls | 14% | - Data lifecycle management and controls - Data quality standards and measurement - Data governance frameworks and policies - Data privacy, security, and compliance |
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