CompTIA • DA0-001
CompTIA Data+ (DA0-001) Practice Test
CompTIA Data+ validates the skills required to transform business requirements into data-driven decisions by mining, manipulating, and analyzing complex datasets. It covers the full data lifecycle including statistical methods, visualization, and data governance and quality standards.
Exam Details
Questions
700
Duration
90 minutes
Passing Score
675/900
Difficulty
AssociateLast Updated
Mar 2026
Topics Covered
Data Concepts and EnvironmentsData MiningData AnalysisVisualizationData Governance, Quality, and Controls
Exam Domain Breakdown
Data Concepts and Environments15%
Data Mining25%
Data Analysis23%
Visualization23%
Data Governance, Quality, and Controls14%
Exam Overview
CompTIA Data+ (DA0-001) is an entry-level, vendor-neutral certification that validates the ability to analyze, interpret, and communicate business data effectively. The certification proves competency in transforming business requirements into data-driven decisions through data mining, manipulation, and analysis of complex datasets. It covers the full data lifecycle, including statistical methods, data visualization, and governance and quality standards. This certification is uniquely positioned as the only entry-level, hands-on analytics certification that does not focus on a single vendor's tools, making it accessible to professionals transitioning into data analytics roles.
Who Should Take This Exam
CompTIA Data+ targets early-career professionals seeking to advance into data analytics roles. Ideal candidates include business analysts, report developers, and professionals with 18-24 months of experience in roles involving data analysis, reporting, or business analytics. The certification is also suitable for career changers with basic analytics exposure, IT professionals transitioning to data roles, and individuals with fundamental understanding of statistics and data visualization who want formal credential validation.
Prerequisites
While CompTIA Data+ has no formal prerequisites, the certification is designed for candidates with 18-24 months of practical experience in a report, business analyst, or similar analytical role. Recommended prerequisite knowledge includes exposure to databases and analytical tools, a basic understanding of statistics and probability, and hands-on experience with data visualization tools. Familiarity with spreadsheet applications (Excel) and SQL concepts is beneficial, though not mandatory. Candidates should have some exposure to business analytics workflows and report creation.
Exam Format
The CompTIA Data+ exam (DA0-001) consists of a maximum of 90 scored questions, combining multiple-choice (single and multiple response) and performance-based question (PBQ) types. The exam is delivered online or in-person at Pearson VUE testing centers and must be completed within 90 minutes. Performance-based questions require hands-on skills such as dataset manipulation, drag-and-drop interactions, and scenario analysis. The exam uses scaled scoring on a scale of 100-900 points, with a passing score of 675. Non-native English speakers may be eligible for extra testing time, and ADA accommodations are available with proper documentation.
Skills Measured
- 1.Domain 1: Data Concepts and Environments (15%) — Understanding data types, database architectures, data storage technologies, cloud platforms, and the fundamentals of data ecosystems
- 2.Domain 2: Data Mining (25%) — Techniques for extracting, transforming, and preparing data; performing data integration; and applying appropriate mining methods to identify patterns
- 3.Domain 3: Data Analysis (23%) — Applying statistical methods, descriptive and inferential analysis, hypothesis testing, and translating business requirements into analytical approaches
- 4.Domain 4: Visualization (23%) — Selecting appropriate visualization types, developing dashboards, creating reports, and communicating data insights effectively to stakeholders
- 5.Domain 5: Data Governance, Quality, and Controls (14%) — Understanding data governance frameworks, master data management (MDM), data quality principles, and implementing controls throughout the data lifecycle
Study Tips
- Focus on hands-on practice with actual datasets and tools like Excel, Python, or Tableau mentioned in CompTIA study materials. Performance-based questions require practical skills, not just theory.
- Master statistical fundamentals including descriptive statistics, probability, hypothesis testing, and correlation—these concepts are core to Domain 3 and frequently tested through scenario-based questions.
- Study real-world business scenarios and learn to translate requirements into data analysis approaches. CompTIA emphasizes the practical application of analyzing data to solve actual business problems.
- Practice identifying the appropriate visualization type for different data scenarios. Review best practices for dashboard design, report creation, and communicating data insights to non-technical stakeholders.
- Understand the data lifecycle from collection through governance, including data quality control, data validation, and master data management concepts. Study the regulations and standards relevant to data handling in your industry.
- Use CompTIA's official study materials and take multiple practice exams. Treat every practice question as if it counts—some exam questions are unscored beta questions, so you won't know which ones don't count during the test.
- Review CompTIA's published exam objectives document thoroughly. The five domains with their specific weightings guide your study plan and help you allocate time effectively.
Career Benefits
CompTIA Data+ certification significantly enhances employability in the rapidly growing data analytics field. Certified professionals qualify for roles including Data Analyst, Business Analyst, Business Intelligence Specialist, Marketing Analyst, and reporting specialists. The current job market shows over 193,000 U.S. job postings seeking data analysis, data mining, and data visualization skills, with many entry-level positions available. Salary expectations range from $45,600 for junior professionals to $95,200+ for experienced analysts, with experienced senior data professionals earning $110,000 or more. As a vendor-neutral credential, Data+ is recognized across industries and demonstrates data literacy—a critical skill for career advancement and mobility in today's data-driven economy. The certification differentiates candidates in competitive job markets and serves as a foundation for advancing to more specialized certifications or senior analytics roles.
Sample Questions
Preview — answers shown5 sample questions with correct answers and explanations. Start a practice session to test yourself across all 700 questions.
1. Northwind's data team wants to track a critical business data entity — specifically, customer records — across six internal source systems (CRM, billing, ERP, support, marketing, and loyalty). Each system stores slightly different versions of the same customer's name, address, and contact details. The team needs a single, authoritative record that reconciles all six versions. Which MDM concept describes this reconciled output record? (Select one!)
AData catalog asset
BGolden record
CData dictionary entry
DCanonical schema
Explanation
A golden record is the single, authoritative, and most accurate version of a master data entity created by merging and reconciling data from multiple source systems. When six systems each hold different versions of the same customer, MDM processes deduplicate, standardize, and merge those records into one golden record that becomes the system of reference. A data dictionary entry documents field definitions and metadata but does not represent a reconciled data record. A data catalog asset is an inventory item for data discovery across the organization, not a merged entity record. A canonical schema defines a standard data model for integration but is not the output reconciled record itself.
2. A healthcare organization is establishing its data governance framework. The Chief Medical Officer is ultimately accountable for patient record integrity and determines who may access sensitive health data. A business analyst maintains data definitions, enforces data quality standards, and serves as the liaison between clinical staff and the IT department. A database administrator implements encryption protocols, manages backup schedules, and provisions system access for approved users. Which assignment correctly maps each person to their data governance role? (Select one!)
AChief Medical Officer = Data Steward; Business Analyst = Data Owner; DBA = Data Custodian
BChief Medical Officer = Data Owner; Business Analyst = Data Steward; DBA = Data Custodian
CChief Medical Officer = Data Owner; Business Analyst = Data Custodian; DBA = Data Steward
DChief Medical Officer = Data Custodian; Business Analyst = Data Owner; DBA = Data Steward
Explanation
The Data Owner is a senior executive who holds ultimate accountability for a data domain. This includes the authority to classify data sensitivity and define who may access it. The Chief Medical Officer, as the executive accountable for patient record integrity and access decisions, fits this role. The Data Steward operates at the business or operational level, maintaining data definitions, enforcing quality rules, and bridging the gap between business stakeholders and IT teams. The business analyst, who maintains definitions and enforces quality standards while liaising between clinical staff and IT, is the Data Steward. The Data Custodian is a technical role responsible for implementing the security controls and operational procedures that owners and stewards define. This includes encryption, backups, database management, and provisioning access. The DBA who implements encryption and manages backups is the Data Custodian. Data Owners are accountable and set policy. Data Stewards are responsible for quality and definitions. Data Custodians implement the technical safeguards.
3. Litware's data team is analyzing various data sources. Which of the following are examples of unstructured data? (Select two!)
Multiple correct answersACustomer transaction records in a relational database
BProduct images stored as JPEG files
CSocial media posts and comments
DSales data in a CSV file
EInventory quantities in an Excel spreadsheet
Explanation
Product images (JPEG files) and social media posts are unstructured data because they lack a predefined data model or schema. Transaction records, CSV files, and Excel spreadsheets contain structured or semi-structured data with defined formats and schemas.
4. Fabrikam's customer data warehouse tracks customer address changes over time for marketing analysis. When a customer moves, the business needs to retain both the old and new addresses with effective date ranges to analyze regional purchasing patterns historically. The current implementation overwrites addresses, losing historical data. What slowly changing dimension strategy should they implement? (Select one!)
AType 1 SCD with audit log table for tracking changes
BType 0 SCD with immutable dimension records
CType 3 SCD with previous and current address columns
DType 2 SCD with effective start/end dates and current flag
Explanation
Type 2 SCD is the correct approach for preserving full historical records with date ranges. It creates a new row for each change with EffectiveStartDate, EffectiveEndDate, and typically a CurrentFlag to identify the active record. This allows analysts to join facts to the appropriate dimension record based on transaction dates, enabling accurate historical analysis of regional patterns. Type 1 overwrites without history, Type 3 only tracks one previous value (insufficient for multiple moves), and Type 0 is for dimensions that never change. Type 2 is the most common SCD implementation for tracking historical changes.
5. Tailspin Hospital implements a new data access system for their electronic health records. The IT director wants physicians, nurses, and administrative staff to automatically receive appropriate data access permissions based solely on their job function. For example, all cardiologists should have identical access to cardiology patient records, all nurses should have standardized access to patient vitals and medication records, and all billing staff should have access only to insurance and payment information. Which access control model best fits these requirements? (Select one!)
ARole-Based Access Control, where permissions are assigned to job roles and users inherit permissions through role membership
BDiscretionary Access Control, where individual data owners decide who can access specific patient records
CAttribute-Based Access Control, where access decisions are made dynamically based on multiple user attributes, resource attributes, and environmental conditions
DMandatory Access Control, where a central authority assigns security clearance levels and data classification labels
Explanation
Role-Based Access Control assigns permissions to predefined roles based on job functions, and users automatically inherit those permissions when assigned to roles. This matches the hospital requirement where employees receive standardized access based solely on their position. Discretionary Access Control allows individual data owners to grant access on a case-by-case basis, which would not provide the automated standardization needed. Mandatory Access Control uses centralized security labels and clearances typically found in military or government settings. Attribute-Based Access Control evaluates multiple dynamic attributes to make access decisions, which is more complex than the straightforward role-based approach needed here.
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