CompTIA Data+ Quick Facts (2025)

CompTIA Data+ Quick Facts

The CompTIA Data+ certification is a definitive step for professionals ready to strengthen their ability to interpret and communicate data-driven insights. This overview highlights the key areas of knowledge, helping you navigate each domain with clarity and purpose as you prepare to excel on your certification journey.

How does CompTIA Data+ strengthen your data skillset?

CompTIA Data+ validates essential skills in data analytics, bringing together concepts from data management, visualization, analysis, and governance. It is ideal for professionals who work with data to inform business decisions—whether creating dashboards, performing queries, or interpreting patterns for strategic planning. This certification demonstrates your ability to translate raw information into meaningful insights that guide organizational success.

Exam Domains Covered (Click to expand breakdown)

Exam Domain Breakdown

Domain 1: Data Concepts and Environments (20% of the exam)

Explain data concepts.

• Database types (relational and non-relational)
• Common file extensions such as CSV, XLSX, JSON, TXT, JPG, DAT
• Structured and unstructured data types and schemas
• Data types including string, numeric, boolean, datetime, and large objects

Section summary: This section introduces the essential building blocks of data and how they are represented in different databases and formats. You will learn to identify where structured, semi-structured, and unstructured data fit within analytical processes while understanding fact tables, schema structures, and data relationships that support business intelligence systems.

Additionally, you will become familiar with key data types and object storage methods to ensure correct data handling across tools and environments. This foundational understanding positions you to work effectively with any dataset by selecting proper formats and structures for analysis.

Identify types of data sources.

• Databases, APIs, websites, and logs
• Data lakes, lakehouses, marts, silos, and warehouses

Section summary: This part explores how data originates and is stored across multiple systems and repositories. You will compare traditional databases with modern data architectures such as data lakes and warehouses, learning when to apply each based on analytical goals.

It also highlights how a variety of sources like APIs and log files contribute to data pipelines, giving you a well-rounded view of where and how information is collected for processing.

Identify infrastructure concepts.

• Cloud providers (AWS, Azure, Google)
• Private, public, and hybrid environments
• Object, file, local, shared, and block storage
• Containerization fundamentals

Section summary: Here, you will connect data environments to the infrastructure that hosts them. This includes working with cloud and on-premises ecosystems, understanding differences in storage types, and recognizing deployment models.

The focus is on selecting the most effective infrastructure strategy for storing, accessing, and integrating data securely and efficiently across environments.

Identify common data analysis tools.

• Integrated development environments and text editors
• Business intelligence software and common data analysis libraries
• Popular database management tools for SQL and NoSQL systems

Section summary: This section provides practical exposure to widely used data tools. You will explore IDEs like RStudio and VS Code, BI tools including Tableau and Power BI, and essential programming libraries such as pandas and tidyverse.

Through these examples, you gain a clear perspective on which tools best support different analytic tasks from querying databases to building dashboards or transforming raw data for reporting.

Identify artificial intelligence (AI) concepts.

• Large language models, deep learning, NLP, foundational models, and RPA

Section summary: This part gives a high-level view of how AI enhances data analytics. You will study core AI applications relevant to data work, such as generative modeling, language processing, and automation.

Understanding these concepts helps you see how intelligent systems can advance analytics workflows and support innovation across industries through data-driven automation.

Domain 2: Data Acquisition and Preparation (22% of the exam)

Given a scenario, use data acquisition methods.

• Integration and querying techniques such as joins, filters, and aggregations
• ETL and ELT concepts
• Data collection through surveying and sampling

Section summary: In this section, you will develop the ability to gather data effectively from multiple systems using both manual and automated techniques. Learning to combine, filter, and aggregate results helps ensure that datasets are accurate and ready for deeper analysis.

You will also examine how ETL and ELT pipelines transform data from raw input to analytical output, strengthening your skills in building efficient data integration workflows.

Given a scenario, perform data exploration to identify possible inconsistencies with a data set.

• Common issues such as missing values, duplication, redundancy, outliers, and validation

Section summary: This section focuses on quality control through exploratory techniques that help detect irregularities. You will learn actionable methods for identifying potential problems during data review to maintain dataset integrity for analysis.

The emphasis is on validating completeness and consistency across sources, enabling you to proactively manage data quality during discovery and preparation phases.

Given a scenario, perform appropriate data transformation and cleansing techniques.

• String manipulation, conversions, clustering, scaling, and imputation
• Parsing, merging, appending, and creating derived variables

Section summary: Here, your attention shifts to cleaning and transforming data for optimized usability. You will apply methods such as scaling, imputation, and merging to standardize and enrich datasets for reporting and visualization.

This section ensures you can confidently manage transformation workflows that turn raw information into polished, structured datasets aligned with project objectives.

Domain 3: Data Analysis (24% of the exam)

Given a set of requirements, determine the appropriate communication approach for data analysis.

• Audience awareness, accessibility, and KPI alignment

Section summary: This section emphasizes clear data storytelling tailored to audience needs. You will learn how to present insights at the right level of detail for technical and executive stakeholders, ensuring your analysis drives actionable outcomes.

It also guides you in designing outputs that respect accessibility principles while highlighting KPIs that communicate progress and impact effectively.

Given a scenario, select the appropriate statistical method or function.

• Descriptive, predictive, prescriptive, and inferential statistics
• Logical, date, string, and mathematical functions

Section summary: This section helps you connect analytical outcomes to the right statistical techniques. You’ll explore common data functions and statistical measures like averages and variance to identify relationships and trends in datasets.

By mastering these methods, you will confidently select appropriate functions to draw meaningful conclusions that inform decision-making.

Given a scenario, troubleshoot basic issues using the appropriate tool or method.

• Problem-solving for connectivity, SQL errors, and data validation

Section summary: In this portion, you will refine troubleshooting skills to resolve issues that arise in data analysis workflows. You will learn how to use logs, check data sources, and leverage community resources effectively.

The goal is to ensure that analysis remains accurate, timely, and resilient across technical environments by developing a structured approach to issue resolution.

Domain 4: Visualization and Reporting (20% of the exam)

Given a scenario, use the appropriate visual elements.

• Charts, maps, infographics, and pivot tables with clear design principles

Section summary: Visualization lies at the heart of data communication, and this section focuses on designing visuals that highlight insights vividly. You’ll learn how to choose the right chart types, apply color theory, and include supportive elements like legends and labels.

Proper design ensures that data presentations are not only visually engaging but also convey meaning with clarity and precision to your target audience.

Given a scenario, use the appropriate delivery or consumption method.

• Dashboards, portals, reports, and real-time or snapshot data versions

Section summary: This section covers how to deliver insights through accessible and relevant channels. You’ll explore different formats such as self-service dashboards and recurring executive summaries, learning to match delivery methods to user needs.

The outcome is a strong understanding of both static and interactive reporting capabilities ensuring stakeholders can act on data insights efficiently.

Given a scenario, troubleshoot issues using report validation techniques.

• Identifying issues like slow performance, stale data, or filter errors

Section summary: In this topic, you’ll refine evaluation methods for verifying report accuracy and data consistency. You will learn to trace performance problems and use monitoring tools for report quality assurance.

It reinforces the habit of maintaining trustworthy, validated reporting systems that keep analytics sustainable and dependable over time.

Domain 5: Data Governance (14% of the exam)

Explain data management concepts.

• Documentation, lineage, versioning, and metadata

Section summary: This section details the principles behind effective data governance. You will learn to document information flow, maintain versioning, and establish a source of truth for all organizational datasets.

Understanding these principles helps teams ensure consistency, accountability, and transparency throughout every stage of the data lifecycle.

Summarize concepts related to data compliance.

• Regulatory requirements, retention, replication, audit, and ethical data practices

Section summary: This part focuses on meeting compliance and ethical standards that safeguard organizational and customer data. You will explore GDPR, PCI DSS, and other frameworks while recognizing the importance of documentation and incident tracking.

By mastering these concepts, you help ensure data management strategies align with legal and ethical expectations for secure, responsible analytics.

Compare and contrast data privacy and protection practices.

• Encryption, access control, anonymization, and masking

Section summary: Here you’ll gain expertise in maintaining privacy through controlled access and secure data-handling techniques. Encryption standards and anonymization methods are key areas of focus.

This section empowers you to implement protection strategies that minimize risk while maintaining usability for analytics and collaboration.

Compare and contrast data quality assurance practices.

• Testing methods, quality metrics, and continuous monitoring

Section summary: The final section emphasizes proactive quality assurance. You’ll learn to design tests, monitor metrics, and maintain control systems that continually measure data reliability.

By applying these practices, you ensure data maintains its integrity, accuracy, and consistency, forming a dependable foundation for decision-making.

Who Should Pursue the CompTIA Data+ Certification?

The CompTIA Data+ Certification is perfect for individuals who are eager to master data analytics fundamentals and transform raw numbers into meaningful business insights. This credential is ideal for:

• Early-career data professionals looking to validate their analytical skills
• Business analysts, data technicians, or reporting specialists seeking to advance
• Team members in finance, marketing, or operations working with data-driven decisions
• IT professionals aiming to bridge the gap between technology and business intelligence

Whether you’re just entering the analytics field or already performing analytical tasks, the CompTIA Data+ proves your ability to use data confidently in real-world business environments.

What Career Opportunities Can CompTIA Data+ Open Up?

Earning the CompTIA Data+ credential helps you stand out in fields that rely on analytics to inform strategic decisions. It’s a smart investment if you’re pursuing roles such as:

• Data Analyst or Junior Data Analyst
• Business Intelligence Analyst
• Reporting Analyst
• Marketing Data Specialist
• Operations Data Coordinator

As data analytics continues to shape every industry, this certification can also serve as a stepping stone toward more advanced certifications and roles in data science, business intelligence, and cloud analytics.

What Version of the Exam Is Current?

The latest version of the CompTIA Data+ Certification exam is DA0-002 V2. This version modernizes analytics content and aligns with emerging practices in AI, governance, and data visualization. Be sure your study materials reference DA0-002 V2 to match the latest objectives and technology environments.

How Much Does the CompTIA Data+ Exam Cost?

The CompTIA Data+ Certification exam costs $255 USD. Price may vary based on your country and test delivery method (online or in-person). You can purchase exam vouchers directly from CompTIA or authorized training partners. It's a worthwhile investment that demonstrates real data literacy to employers around the world.

How Many Questions Are on the Exam?

The exam features a maximum of 90 questions. These include both multiple-choice and performance-based questions that simulate real-world analytics scenarios. Each question is designed to test not just your knowledge, but your ability to apply it practically across the data lifecycle—from acquisition and preparation to visualization and governance.

How Long Is the CompTIA Data+ DA0-002 V2 Exam?

You’ll have 90 minutes to complete the exam. Time management is key, but the exam structure allows you to focus strategically—start with the questions that come most naturally, then move on to those requiring deeper analysis. Most test-takers find the pacing fair with practice using sample questions and simulated environments.

What’s the Passing Score for CompTIA Data+?

You’ll need a scaled score of 720 out of 900 to pass the CompTIA Data+ exam. The scaled system ensures consistency between different test versions, meaning every candidate is evaluated fairly. A 720 score reflects a solid grasp of all key data competencies, including analysis, visualization, and governance concepts crucial to professional performance.

What Languages Is the Exam Offered In?

The CompTIA Data+ Certification exam is available in English. CompTIA periodically expands language options to reflect global demand, but at this time English remains the primary exam delivery language. If English is not your first language, you can request additional time accommodations through CompTIA’s official testing channels.

What Experience Do You Need Before Taking the Exam?

CompTIA recommends 1.5 to 2 years of hands-on experience in a data-related or analytical role. This includes exposure to databases, basic statistics, and data visualization tools. While not mandatory, practical experience helps you contextualize exam questions and boosts confidence when tackling performance-based scenarios.

How Difficult Is the CompTIA Data+ Certification?

The CompTIA Data+ is a foundational-yet-professional certification designed for emerging data professionals. It blends business context with analytical practice. Success comes from understanding how data flows through systems, how to interpret results, and how to communicate insights effectively. With steady study and real-world application, many learners achieve certification on their first attempt.

To prepare thoroughly, explore top-rated CompTIA Data+ Practice Exams that simulate the actual test format and include fully explained answers to reinforce your understanding.

What Domains Are Covered on the Exam?

The CompTIA Data+ DA0-002 V2 exam assesses your analytics knowledge across five key domains:

1. Data Concepts and Environments (20%)
   Understand different data structures, storage models, and analytical environments.
2. Data Acquisition and Preparation (22%)
   Learn to collect, clean, and transform data for accurate analysis.
3. Data Analysis (24%)
   Apply statistical reasoning and communicate analytical results clearly.
4. Visualization and Reporting (20%)
   Create meaningful reports and dashboards that support business decisions.
5. Data Governance (14%)
   Maintain data quality, ensure compliance, and uphold security and privacy standards.

Each domain builds a comprehensive understanding of the data life cycle—from collection to communication.

How Is the Exam Structured?

The exam questions vary between multiple-choice and performance-based formats. Multiple-choice items test conceptual understanding, while performance-based tasks measure applied skill in realistic scenarios (for instance, interpreting data visualizations or choosing the right statistical approach). This ensures candidates can move seamlessly from theory to real-world execution.

How Is the CompTIA Data+ Certification Scored?

CompTIA uses a scaled scoring model ranging from 100 to 900 points, with 720 as the passing threshold. This approach normalizes results across different exam forms to maintain fairness and consistency. Only your highest achieved score counts, so if you decide to retake the exam, you’ll always keep your best performance.

How Long Is the CompTIA Data+ Certification Valid?

Your CompTIA Data+ certification remains valid for three years from the date you pass the exam. To maintain your credential, you can renew through CompTIA’s Continuing Education (CE) program, earning CEUs (Continuing Education Units) via related learning or by retaking the most current Data+ exam version.

What Skills Will You Learn from the CompTIA Data+ Exam?

The CompTIA Data+ equips you with practical, industry-aligned data analytics skills, including:

• Collecting and preparing reliable datasets
• Using analytical tools such as Power BI, Tableau, SQL, and Python-based libraries
• Applying descriptive and predictive statistics for trend discovery
• Creating visual narratives from complex data outputs
• Maintaining compliance with privacy and governance frameworks

By the end of your study journey, you’ll be able to transform data into decisions that drive meaningful business change.

What Tools Should You Know Before Taking the Exam?

Success on the exam often comes naturally to candidates familiar with tools like:

• SQL and Database Management Systems (e.g., SSMS, MySQL Workbench)
• Visualization software such as Tableau or Power BI
• Programming environments like Python, R, or SAS
• Jupyter Notebooks or VS Code for analysis and documentation

CompTIA lists these as recommended, not required, but even light practice with them will enhance your readiness and comfort during the exam.

What Are Common Mistakes to Avoid When Preparing?

Candidates occasionally underestimate the importance of practice and review. Avoid these pitfalls by:

• Practicing with real datasets rather than memorized examples
• Ensuring you understand both concepts and their practical applications
• Reviewing all five exam domains equally to avoid topic imbalance
• Validating your understanding with peers or practice tests for clarity

A confident study routine that mixes reading, hands-on exercises, and practice exams leads to outstanding success.

How Can You Study Effectively for CompTIA Data+?

Create a structured plan that balances reading, experimentation, and review. Combine these learning methods:

1. Official CompTIA Learning Resources with domain-by-domain review
2. Practical labs and simulation exercises to apply what you learn
3. Community discussions to clarify confusing topics or share insights
4. Timed mock exams to improve pacing and readiness

Set consistent study goals and celebrate small wins along the way. With the right plan, you’ll feel ready and confident on exam day.

What Topics Are Most Important to Focus On?

Key areas to emphasize in your preparation include:

• Understanding data architectures and infrastructure (databases, APIs, cloud systems)
• Mastering ETL processes and data cleansing techniques
• Applying statistical analyses like descriptive, predictive, and inferential methods
• Building effective visualizations and dashboards
• Knowing data governance frameworks and best practices in compliance

Deep comprehension of these areas ensures you’re ready for the variety of questions you’ll encounter on the exam.

How Do You Register for the CompTIA Data+ Exam?

Scheduling your exam is easy. Simply sign in to your CompTIA certification account, purchase a voucher if necessary, and choose your preferred delivery method with Pearson VUE. You can test either online with remote proctoring or in person at an authorized testing center. Select your date, confirm details, and you’re ready to start your journey toward certification success.

Where Can You Take the CompTIA Data+ DA0-002 V2 Exam?

You can complete the certification either:

1. Remotely, from your home or office using online proctoring
2. In-person, at a Pearson VUE testing center nearest to you

Both options maintain the same integrity standards, so you can choose whichever environment supports your best performance.

What Comes After Earning Your CompTIA Data+?

After passing your CompTIA Data+, you can pursue other certifications to deepen your expertise, such as:

• CompTIA DataSys+ for database administration
• CompTIA Security+ or CySA+ for analytics in cybersecurity
• Vendor certifications like Microsoft Power BI Data Analyst (PL-300) or AWS Data Analytics Specialty for specialization

Each next step builds on the analytical foundation you’ve gained, helping advance your data career with confidence.

Why is CompTIA Data+ a Great Step for Your Future?

Data literacy is rapidly becoming one of the most valuable skills across every industry. Earning the CompTIA Data+ shows that you know how to harness, interpret, and communicate data in ways that impact business decisions. This certification not only strengthens your technical abilities but also enhances your communication, problem-solving, and storytelling skills through data.

Where Can You Learn More About the CompTIA Data+ Certification?

To get full exam details, objectives, and official resources, visit the official CompTIA Data+ certification page. There, you can explore exam vouchers, study materials, and updates as you prepare to become a certified data professional ready to transform information into impact.