CompTIA Data+ (DA0-001) Practice Exam

CompTIA Data+ (DA0-001) Practice Exam

 

CompTIA Data+ serves as an entry-level certification in data analytics aimed at professionals entrusted with fostering data-informed business decision-making. The exam will:

• Enhance your ability to interpret data effectively. 
• Refine your data mining skills.
• Master the art of critical insights through compelling reports that facilitate informed decision-making.
• Position yourself as an indispensable asset within your team.

The CompTIA Data+ examination validates the candidate's proficiency in translating business needs into actionable insights by leveraging data mining techniques, applying foundational statistical methodologies, and navigating complex datasets while upholding governance and quality standards throughout the data lifecycle.

 

Recommended Prerequisites:

• CompTIA suggests 18-24 months of experience in roles such as reporting or business analysis, exposure to database systems and analytical tools, a basic comprehension of statistical concepts, and familiarity with data visualization techniques.

 

Who should take the exam?

The CompTIA Data+ (DA0-001) exam is for:

• Professionals interested in building foundational skills for data analytics careers. 
• Database administrators
• Data Analysts
• Business Analysts
• Marketing Analysts
• Operations Analysts
• Entry-level Data Scientists
• Recent graduates

 

Exam Details 

• Exam Code: DA0-001
• Exam Name: CompTIA Data+
• Exam Languages: English, Japanese, Thai
• Exam Questions: 90 Questions
• Time: 90 minutes
• Passing Score: 675 (on scale of 100–900)

 

Course Outline 

The Exam covers the given topics  - 

Topic 1: Understand Data Concepts and Environments 15%

• Identify basic concepts of data schemas and dimensions.
• Compare and contrast different data types.
• Compare and contrast common data structures and file formats.

 

Topic 2: Learn about Data Mining 25%

• Explain data acquisition concepts.
• Identify common reasons for cleansing and profiling datasets.
• Given a scenario, execute data manipulation techniques.
• Explain common techniques for data manipulation and query optimization.

 

Topic 3: Explore Data Analysis 23%

• Given a scenario, apply the appropriate descriptive statistical methods.
• Explain the purpose of inferential statistical methods.
• Summarize types of analysis and key analysis techniques.
• Identify common data analytics tools.

 

Topic 4: Understand Visualization 23%

• Given a scenario, translate business requirements to form a report.
• Given a scenario, use appropriate design components for reports and dashboards.
• Given a scenario, use appropriate methods for dashboard development.
• Given a scenario, apply the appropriate type of visualization.
• Compare and contrast types of reports.

 

Topic 5: Data Governance, Quality, and Controls 14%

• Summarize important data governance concepts.
• Given a scenario, apply data quality control concepts.
• Explain master data management (MDM) concepts.