About the exam
Data+ Exam structure
CompTIA Data+ DA0-002 prep with 601 original practice questions, domain-weighted mocks, analytics drills, flashcards, and topic recovery.
Issuer and path
CompTIA Data+ Exam Prep is administered through CompTIA. Check official resources before booking, retesting, or relying on a stale requirement.
Data Concepts and Environments
20 scored + 0 pretest
Data types, structures, formats, sources, storage systems, cloud and container infrastructure, analytical tools, AI concepts, and database environments.
Data Acquisition and Preparation
22 scored + 0 pretest
Data integration, queries, extraction, exploration, profiling, cleaning, transformation, validation, joining, and preparation workflow.
Data Analysis
24 scored + 0 pretest
Descriptive statistics, distributions, correlation, hypothesis testing, outliers, trend analysis, segmentation, and business interpretation.
Visualization and Reporting
20 scored + 0 pretest
Chart selection, dashboard design, report layout, audience needs, accessibility, storytelling, filters, KPIs, and presentation quality.
Data Governance, Quality and Controls
14 scored + 0 pretest
Data quality, stewardship, privacy, security, lineage, retention, access control, ethics, compliance, and governance controls.
Before you schedule
Confirm the Data+ exam code, voucher dates, testing option, ID requirements, system test for online delivery, and retake policy before booking.
Official Outline Coverage Map
Coverage is mapped to official outline item counts so content depth can be checked without hard-coding a single exam.
| Topic | Official outline items | Your questions | Your flashcards | Confidence |
|---|---|---|---|---|
| Data Types, Structures and Sources | 8 | 60 | 10 | Priority |
| Databases, Storage and Basic Statistics | 7 | 60 | 10 | Strong |
| Data Acquisition, Profiling and Extraction | 13 | 66 | 10 | Priority |
| Cleaning, Transformation and Validation | 12 | 66 | 10 | Strong |
| Statistics, Trends and Segmentation | 12 | 72 | 10 | Priority |
| Hypothesis, Metrics and Business Interpretation | 11 | 72 | 10 | Strong |
| Chart Selection and Dashboard Design | 12 | 60 | 10 | Priority |
| Reporting, Storytelling and Audience Needs | 11 | 60 | 10 | Strong |
| Data Quality, Lineage and Stewardship | 7 | 43 | 10 | Priority |
| Privacy, Security, Ethics and Controls | 7 | 42 | 10 | Strong |
How to use this guide
How to study for CompTIA Data+
Treat each Data+ item as an analytics decision: identify the source, quality issue, transformation, statistic, visual, audience, and control.
1. Identify source and structure
Name the data type, source, schema, metadata, grain, and collection context.
2. Profile and prepare
Check quality, missing values, duplicates, joins, transformations, validation rules, and lineage.
3. Analyze and visualize
Choose statistics, segments, trends, KPIs, charts, filters, and dashboard layout based on the question.
4. Communicate and govern
Match the audience, state limits, protect sensitive data, and document quality or control decisions.
Data Types, Structures and Sources
Data+ starts with recognizing data types, structured and unstructured data, file formats, source systems, schemas, metadata, and collection context.
Key rules
Rule 1
Data type and structure determine which operations, validations, and analysis methods are appropriate.
Exam cue: Identify type, structure, source, schema, and metadata before analysis.
Rule 2
Source context matters because operational, transactional, survey, sensor, and third-party data have different limitations.
Exam cue: Match file format to use case and tool compatibility.
Rule 3
Metadata and schema information explain the meaning, lineage, constraints, and usability of data.
Exam cue: Check collection context before trusting a field.
Common traps
Treating text categories as numeric measurements.
Prevention: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Assuming a flat file has the same constraints as a relational table.
Prevention: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Ignoring metadata when interpreting ambiguous field names.
Prevention: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Memory anchors
Structured Data
Structured data follows a defined model such as rows, columns, keys, and constraints.
Semi-Structured Data
Semi-structured data has flexible organization such as JSON, XML, or nested event records.
Unstructured Data
Unstructured data such as free text, images, audio, and documents needs extra processing before analysis.
Categorical Data
Categorical data represents labels or groups rather than measured quantities.
Numerical Data
Numerical data supports arithmetic and can be discrete or continuous.
Schema
A schema describes fields, data types, relationships, constraints, and expected structure.
Metadata
Metadata describes data meaning, source, owner, format, lineage, and quality context.
CSV
CSV is a delimited text format that is easy to exchange but weak on data types and constraints.
JSON
JSON supports nested structured data commonly used by APIs and event systems.
AI and NLP
AI models and natural language processing can classify, summarize, or extract patterns, but their outputs require validation and governance.
Next best moves
Quick check-up
Use a short quiz to confirm the rule pattern is actually sticking.
Check-up Questions
Northstar Health receives a table with fixed columns, declared data types, a primary key, and validation constraints. Which action is MOST appropriate? Use only the facts stated.
An API returns nested JSON in which optional objects and arrays differ across permits records. Which recommendation is BEST? Use only the facts stated.
Answer all questions to submit.
Next step personalized recommendations
Open another topic next
Official resources
Verify the details with the official sources
Use these links for eligibility, scheduling, handbook rules, and issuer updates. Our guide helps you study; official sources tell you what the testing partner currently requires.
FAQ
Common Data+ questions
Is this the official CompTIA Data+ exam?
No. These are original practice questions aligned to public Data+ DA0-002 domains. They are not copied from secure exam material.
What should I study first?
Start with data types, schemas, sources, profiling, cleaning, and validation before moving into statistics, dashboards, and governance.
Do I need advanced math for Data+?
No. Focus on practical descriptive statistics, trends, distributions, correlation, confidence, and business interpretation.
Why are there 601 questions?
The larger bank supports repeated domain and topic drills without memorizing a small set of prompts.
How should I use the 601 questions?
Use prep and quality drills first, then analysis and visualization drills, then full mocks to practice audience-focused decisions.