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CompTIA · COMPTIA-DATA · Intermediate

CompTIA Data+ — Practice Questions and Mock Exam

Prepare for COMPTIA-DATA with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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90Mock exam questions
90minTime limit
675/ 900Passing score

Checked against CompTIA · September 2026 · Current exam version

Overview

About the exam

CompTIA Data+ (DA0-002) is an early-career, vendor-neutral certification that validates the skills needed to analyze, interpret, and communicate data insights for informed business decision-making. It covers the full data analytics lifecycle from acquisition and preparation through analysis, visualization, and governance.

Version 2 expands coverage to include AI concepts, cloud data environments, and emerging compliance frameworks. Data+ is ideal for aspiring data analysts, business intelligence specialists, and professionals transitioning into analytics roles. It maps to the NICE Framework Data Analyst work role (IO-WRL-001).

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Try five COMPTIA-DATA questions

Try five practice questions from the app’s current CompTIA Data+ question bank, with answers and explanations.

Data Analysis1 / 5

A data analyst receives a large dataset with 200 features (columns) for building a classification model. Many features may be redundant or irrelevant. Which technique should the analyst use to reduce the number of features while retaining the most important information?

AlexFull explanation from Alex

With 200 features, the analyst faces the "curse of dimensionality"—too many variables cause overfitting, longer training, and poor generalization. Dimensionality reduction addresses this via feature selection (removing irrelevant features using correlation or importance scores) or feature extraction like PCA, which creates new components capturing maximum variance in fewer dimensions (CompTIA Data+ Obj 3.2; NIST SP 800-188). Why not the others? Imputation fills missing values—it doesn't reduce feature count. Deduplication removes duplicate rows, not columns. Encryption secures data but doesn't change dimensionality. Exam tip: "too many features" = dimensionality reduction; "missing values" = imputation; "duplicate records" = deduplication.

Sourcescikit-learn.org

Data Acquisition and Preparation2 / 5

A data engineer is loading data from a CSV file into a database table. The CSV has 100,000 rows, but after loading, the table shows only 95,000 rows. Which data quality issue is MOST likely responsible?

AlexFull explanation from Alex

Database tables enforce constraints that CSV files do not, so rows violating those rules are rejected during loading. Common violations include: NOT NULL (missing required values), data type mismatches (text in numeric columns), UNIQUE/PRIMARY KEY (duplicate identifiers), CHECK (out-of-range values), and FOREIGN KEY (orphaned references). The 5,000 missing rows appear in an error/rejection log (CompTIA Data+ Obj 4.1; DAMA DMBOK Ch. 13). Why not the others? Databases don't auto-compress rows during loading—compression is a storage optimization. File size doesn't limit loading (data is streamed in batches). Licensing doesn't cap table sizes at arbitrary row counts. Exam tip: source rows > loaded rows = constraint violations; always reconcile counts and check rejection logs.

Sourcepostgresql.org

Data Governance3 / 5

A company wants to monitor data quality in its ETL pipeline in real-time. Which approach is MOST effective for automated data quality monitoring?

AlexFull explanation from Alex

Automated validation rules embedded in the ETL pipeline catch quality issues at ingestion, before bad data reaches reports. These include null checks, type validation, range checks, uniqueness constraints, statistical anomaly detection (row count shifts, distribution drift), and business rule enforcement. Tools like Great Expectations, dbt tests, and Monte Carlo automate checks and alert on threshold breaches (CompTIA Data+ Obj 4.1; DAMA DMBOK Ch. 13). Why not the others? Full backups protect against data loss, not quality issues—they preserve bad data too. Reviewing only when users report problems is reactive—damage is already done. Monthly manual spot checks miss issues between reviews and don't scale. Exam tip: proactive = automated rules + real-time alerts; reactive = user complaints + infrequent manual reviews.

Sourcedocs.aws.amazon.com

Visualization and Reporting4 / 5

An organization is building a self-service analytics platform. Non-technical business users will create their own reports and dashboards. Which capability is MOST critical for enabling self-service analytics?

AlexFull explanation from Alex

A semantic layer abstracts the physical data model into business-friendly terms, enabling non-technical users to build reports without SQL. Instead of querying "tbl_txn.amt_usd" with complex JOINs, users see "Revenue" as a governed, pre-defined metric. Key capabilities: business-friendly naming, pre-built calculations (e.g., gross margin), standardized definitions ensuring consistency, and simplified relationships hiding JOIN complexity. Tools like Looker LookML, dbt semantic layer, and Tableau data models provide this (CompTIA Data+ Obj 5.3; Gartner Augmented Analytics). Why not the others? ETL code repositories require developer skills—the opposite of self-service. Command-line SQL demands technical expertise non-technical users lack. Administrative database access is for DBAs, not report builders, and poses security risks. Exam tip: self-service = semantic layer + drag-and-drop; technical analytics = SQL + code.

Sourcedocs.getdbt.com

Question pool

362 practice questions

The Pass-IT question pool gives you material to practice for COMPTIA-DATA. A Pass-IT mock exam uses 90 questions and a 90-minute time limit; these are practice settings.

Pool details: COMPTIA-DATA

Exam details checked against CompTIASeptember 5, 2026

date of the last check against the official CompTIA source

Passing score675 / 900

as published by CompTIA

Objectives in the guide18 objectives listed in the official guide

across 5 domains in the official exam guide

Pool size362 questions

= The pool size is equivalent to 4 sets of 90 questions; this does not mean that each mock exam uses a separate set.

Blueprint domains5 domains in the exam blueprint

Data Concepts and Environments 74 · Data Acquisition and Preparation 74 · Data Analysis 94 · Visualization and Reporting 68 · Data Governance 52

Recorded as checked against sources344 of 362

questions recorded as having their answer, options, and explanation checked against official CompTIA documentation

How questions are made →

Exam domains

What's on the exam

Data Analysis carries the most weight at 24%, testing which statistical method or function fits a given question rather than raw calculation. Data Acquisition and Preparation follows at 22%, covering querying, ETL/ELT workflows, and cleansing techniques like parsing and imputation. Data Concepts and Environments and Visualization and Reporting tie at 20% apiece (one covering database types and BI tooling, the other chart selection and dashboard delivery), while Data Governance is the lightest domain at 14%, testing documentation, lineage, and compliance concepts like GDPR.

The four largest domains sit within four points of each other, so no single area can be skipped without meaningful risk to the pass score. Data Governance is the outlier on weight but not on difficulty: its questions on data lineage, retention, and privacy compliance appear throughout the rest of the exam even when they're not the primary subject of a question.

Exam blueprint: COMPTIA-DATA

Data Concepts and Environments20%

Explain core data concepts including database types, data structures, and file formats, identify common data sources and storage infrastructure, and survey the coding environments, BI tools, and AI concepts used in data analysis.

≈ 12 h
Data Acquisition and Preparation22%

Acquire data through querying, integration, and ETL/ELT processes, identify data quality issues such as missing values and duplication, and apply transformation and cleansing techniques including parsing, standardization, and imputation.

≈ 13 h
Data Analysis24%

Match the audience and format to the analysis being delivered, pick the statistical method or function that actually answers the question at hand, and work through basic connectivity, data, and query troubleshooting when something breaks.

≈ 14 h
Visualization and Reporting20%

Choose appropriate visual elements and design conventions for charts and dashboards, select delivery methods such as executive summaries and self-service portals, and troubleshoot reporting issues like stale data and slow refresh rates.

≈ 12 h
Data Governance14%

Apply data management concepts such as documentation, lineage, and versioning, summarize compliance requirements like GDPR and data retention, and compare privacy protection practices and data quality assurance methods.

≈ 8 h

Format

Exam format and question types

The exam consists of a maximum of 90 questions to be completed in 90 minutes. Question types include multiple-choice and performance-based questions (PBQs) that drop you into a data analysis scenario: interpreting a dataset, choosing an appropriate visualization, or identifying a data quality problem. The passing score is 675 on a scale of 100 to 900. A simulation PBQ can be skipped and revisited, so decide early how much time you are willing to spend on one.

Question types: COMPTIA-DATA

Multiple Choice74%

Select the single answer that best meets the question’s requirements.

Multiple Response20%

Select multiple answers. Follow the question’s instructions on how many to choose.

Performance-based6%

Complete a practical task in a simulated environment, following the stated requirements.

See CompTIA for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for COMPTIA-DATA

CompTIA Data+ is delivered at Pearson VUE testing centers or online with a proctor, in English and Japanese. The certification holds for three years and renews with 20 CEUs, by earning a higher-level CompTIA certification, or by passing the current version of the exam.

Preparation and logistics: COMPTIA-DATA

Preparation

Illustrative study time35–90 h

illustrative planning range: 35 h with relevant experience to 90 h when starting out; your needs may fall outside this range

LevelIntermediate
Recommended backgroundNo formal prerequisites. 18-24 months of data analytics experience recommended.

Taking and maintaining the certification

DeliveryPearson VUE testing centers or online proctored
Retake policyNo wait for 2nd attempt; 14-day wait for 3rd and subsequent attempts
Certification validity3 years

Complete CE activities within 3-year renewal cycle. Options include CertMaster CE, earning a higher CompTIA certification, non-CompTIA IT certifications, training, publishing, or work experience. CE fee required.

Continuing education credits20
Official guideView on provider site

Study notes

Common pitfalls

Topics to review: COMPTIA-DATA

  1. 01Statistical Concepts

    Must understand basic statistics: mean, median, mode, standard deviation, correlation, regression

  2. 02Data Visualization

    Knowing which chart type (bar, line, scatter, pie, heat map) is appropriate for each data scenario

  3. 03Data Quality

    Understanding data cleansing, deduplication, normalization, and validation concepts

  4. 04SQL Basics

    Basic SQL queries appear — SELECT, JOIN, WHERE, GROUP BY, ORDER BY

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the CompTIA Data+ exam?

The CompTIA Data+ exam has 90 questions and a 90-minute time limit.

What is the passing score for CompTIA Data+?

The passing score for the CompTIA Data+ exam is 675 / 900.

Which pitfalls should I review when preparing for CompTIA Data+?

Topics to review include Statistical Concepts, Data Visualization, Data Quality, SQL Basics. Work through examples to check that you understand the distinctions and can explain your answer.

How is the Data+ exam weighted?

Data analysis is the largest domain at 24%, with data acquisition and preparation at 22% and data concepts and environments and visualization and reporting at 20% each. Data governance closes it at 14%. The weighting is flat by design, so Data+ rewards breadth rather than one deep specialism.

Do you need experience before taking Data+?

There is no formal prerequisite. CompTIA recommends 18 to 24 months of data analytics work, and the catalog budget is about 60 hours. The exam assumes you have cleaned a messy dataset at least once, because the preparation domain is written from that angle.

Data+ or a vendor analytics certification?

Data+ is vendor-neutral and tests the analytics workflow itself: acquisition, cleaning, analysis, visualization and governance. Microsoft PL-300 tests the same workflow through Power BI, and its answers are Power BI answers. Take Data+ if your tooling changes between jobs, and the vendor exam if your employer has already picked the tool.

How do you renew Data+?

Data+ runs on a three-year continuing-education cycle. Eligible activities include CertMaster CE, earning a higher CompTIA certification, other IT certifications, training, publishing or documented work experience. You have to log them before the cycle closes, not after.

How soon can you retake Data+?

There is no wait before the second attempt. From the third attempt onwards CompTIA requires 14 days between tries.

One certification, 12 months

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