How hard is the CompTIA Data+ exam?

Data+ (DA0-001) is considered an easy to moderately easy exam. It has a maximum of 90 questions (multiple-choice and performance-based) with a 90-minute time limit. The passing score is 675 out of 900. Most candidates with basic data experience can prepare in 2-4 weeks. No coding or SQL is tested — it focuses on concepts, analysis techniques, visualization best practices, and data governance.

Is CompTIA Data+ worth it in 2026?

Yes — data analytics is one of the fastest-growing career fields. Data+ is the only vendor-neutral, entry-level data analytics certification, making it ideal for business analysts, marketing analysts, data analysts, and anyone working with data. It complements tools like Excel, SQL, Tableau, and Python. The US Bureau of Labor Statistics projects 23% job growth for data analysts through 2032.

Do I need coding or SQL skills for Data+?

No — Data+ does not test coding, SQL, Python, R, or any specific tool. It focuses entirely on concepts: data types, statistical analysis, visualization best practices, data cleaning techniques, governance, and regulations. You need to understand WHAT these tools do, not HOW to write code. This makes Data+ accessible to non-technical professionals and career changers.

Does CompTIA Data+ expire?

Yes — Data+ is valid for 3 years. To renew, earn 20 CEUs through continuing education activities, pass a higher-level CompTIA certification, or retake the exam. CompTIA also offers CertMaster CE for automatic renewal upon completion.

CompTIA Data+ MCQs with Answers 2026 -

40+

MCQs Covered

Domains Covered

675

Pass Score

2026

Updated For

The CompTIA Data+ (DA0-001) is the industry-standard entry-level data analytics certification. It validates your ability to mine, manipulate, visualize, and report on data to drive business decisions. Data+ covers the entire data lifecycle — from data concepts and environments to data mining, manipulation, visualization, governance, and quality. Whether you’re an aspiring data analyst, business analyst, marketing analyst, or anyone working with data — Data+ proves you can transform raw data into actionable insights. These fully solved MCQs cover all five exam domains.

🗄️ Data Concepts
(15-25%)
⛏️ Data Mining
(25-35%)
🔧 Manipulation
(15-25%)
📊 Visualization
(20-30%)
🔒 Governance
(5-15%)

Table of Contents

Data Concepts & Environments

Domain 1 — 15-25% of Exam

Question 01

Which type of data is organized into rows and columns with a predefined schema, typically stored in relational databases?

AStructured data ✅

BUnstructured data

CSemi-structured data

DQualitative data

💡 Explanation: Structured data follows a rigid, predefined schema with rows and columns — like SQL databases, spreadsheets, and CSV files. Unstructured data has no predefined format (images, videos, emails, social media posts). Semi-structured data has some organization but no rigid schema (JSON, XML, logs). This three-type classification is one of the most tested Data+ concepts.

Question 02

A company uses a centralized repository that collects data from multiple sources, transforms it, and stores it for reporting and analytics. What is this called?

AData warehouse ✅

BData lake

CData mart

DData pipeline

💡 Explanation: A data warehouse is a centralized repository of structured, transformed data optimized for reporting and analytics. It uses ETL (Extract, Transform, Load) to clean and organize data from multiple sources. A data lake stores raw data in any format (structured + unstructured) without transformation. A data mart is a subset of a warehouse focused on one department. A data pipeline is the automated flow of data between systems.

Question 03

Which data type represents categories or labels without a natural numerical order?

ANominal ✅

BOrdinal

CInterval

DRatio

💡 Explanation: Nominal data represents categories with no inherent order — like colors (red, blue), gender (male, female), or country names. Ordinal has a meaningful order but no consistent interval (satisfaction: low, medium, high). Interval has equal spacing but no true zero (temperature in °C). Ratio has equal spacing AND a true zero (weight, height, income). Memorize all four measurement levels for Data+.

Data Mining

Domain 2 — 25-35% of Exam (Highest Weight!)

Question 04

Which statistical measure represents the value that appears MOST frequently in a dataset?

AMean

BMedian

CMode ✅

DStandard deviation

💡 Explanation: Mode = most frequent value. Mean = average (sum/count). Median = middle value when sorted. Standard deviation measures how spread out values are from the mean. These are measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). A dataset can have no mode, one mode, or multiple modes (bimodal, multimodal).

Question 05

A data analyst wants to measure the strength and direction of the linear relationship between two numeric variables. Which statistical technique should they use?

ACorrelation analysis ✅

BRegression analysis

CChi-square test

DT-test

💡 Explanation: Correlation measures the strength and direction of a linear relationship between two variables using the correlation coefficient (r). Values range from -1 (perfect negative) to +1 (perfect positive). r=0 means no linear relationship. Important: correlation does NOT imply causation. Regression predicts one variable from another. Chi-square tests relationships between categorical variables. T-test compares means between groups.

Question 06

Which type of analysis answers the question “What is likely to happen in the future?” based on historical data patterns?

ADescriptive analysis

BDiagnostic analysis

CPredictive analysis ✅

DPrescriptive analysis

💡 Explanation: The four types of analytics: Descriptive = “What happened?” (summaries, dashboards). Diagnostic = “Why did it happen?” (root cause analysis). Predictive = “What will happen?” (forecasting, machine learning). Prescriptive = “What should we do?” (recommendations, optimization). This hierarchy is heavily tested on Data+ — memorize all four types with examples.

Data Manipulation

Domain 3 — 15-25% of Exam

Question 07

A dataset contains several records with missing values in key fields. Which data cleaning technique involves replacing missing values with the mean, median, or a calculated value?

AImputation ✅

BDeduplication

CNormalization

DParsing

💡 Explanation: Imputation replaces missing values with substitutes — mean, median, mode, or predicted values. This preserves the dataset size without losing records. Deduplication removes duplicate records. Normalization scales numeric data to a standard range. Parsing extracts meaningful components from data (like splitting a full name into first and last). Data cleaning techniques are heavily tested on Data+.

Question 08

What does the ETL process stand for in data management?

AExtract, Transform, Load ✅

BEvaluate, Test, Launch

CExport, Transfer, Log

DEncrypt, Tokenize, Load

💡 Explanation: ETL (Extract, Transform, Load) is the process of: (1) Extracting data from source systems (databases, APIs, files), (2) Transforming data (cleaning, formatting, aggregating, joining), and (3) Loading it into a target system (data warehouse). ELT (Extract, Load, Transform) is the modern alternative — data is loaded first, then transformed in the target system. Know the difference between ETL and ELT for the exam.

Question 09

A data analyst notices extreme values in a dataset that are significantly higher or lower than the rest of the data. What are these values called?

AOutliers ✅

BNull values

CDuplicates

DAnomalies

💡 Explanation: Outliers are data points that differ significantly from other observations. They can be detected using box plots (IQR method), z-scores (> 3 standard deviations), or scatter plots. Outliers can result from data entry errors, measurement issues, or genuine extreme values. The analyst must investigate before removing — sometimes outliers contain valuable insights (fraud detection, rare events). Use the median instead of mean when outliers are present.

Visualization & Reporting

Domain 4 — 20-30% of Exam

Question 10

Which type of chart is BEST for showing the distribution of a single continuous variable and identifying its shape (normal, skewed)?

ABar chart

BHistogram ✅

CPie chart

DScatter plot

💡 Explanation: A histogram shows the frequency distribution of a continuous variable using bins (ranges). It reveals the shape of the distribution — normal, left-skewed, right-skewed, bimodal, or uniform. Bar charts compare categories (discrete data). Pie charts show parts of a whole (proportions). Scatter plots show relationships between two continuous variables. Choosing the right chart type is heavily tested on Data+.

Question 11

A manager needs a real-time, interactive visual display that shows key performance indicators (KPIs) at a glance. What should the data analyst create?

ADashboard ✅

BStatic report

CData dictionary

DPivot table

💡 Explanation: A dashboard is an interactive visual display that presents key metrics and KPIs at a glance, often with real-time or near-real-time data. Dashboards allow users to filter, drill down, and explore data interactively. Static reports are fixed-point-in-time documents (PDFs, printed reports). A data dictionary describes the structure and metadata of a dataset. Pivot tables summarize and aggregate data in spreadsheets.

Question 12

Which chart type is BEST for showing the relationship between two continuous numerical variables?

ALine chart

BScatter plot ✅

CStacked bar chart

DTreemap

💡 Explanation: A scatter plot displays individual data points on an X-Y axis, revealing the relationship, correlation, and clusters between two numerical variables. Adding a trend line shows the direction and strength of the relationship. Line charts show trends over time (time-series data). Stacked bar charts show part-to-whole comparisons across categories. Treemaps show hierarchical data using nested rectangles sized by value.

Data Governance, Quality & Controls

Domain 5 — 5-15% of Exam

Question 13

Which data privacy regulation requires organizations to protect the personal data and privacy of individuals in the European Union?

AGDPR (General Data Protection Regulation) ✅

BHIPAA

CPCI DSS

DSOX

💡 Explanation: GDPR protects EU citizens’ personal data with strict rules on collection, storage, processing, and deletion. Key rights include right to access, right to erasure (“right to be forgotten”), data portability, and consent requirements. HIPAA protects health information in the US. PCI DSS protects credit card data. SOX (Sarbanes-Oxley) governs financial reporting. Know all four regulations for Data+.

Question 14

Which data quality dimension refers to whether data values fall within an expected range and conform to business rules?

AAccuracy

BCompleteness

CValidity ✅

DTimeliness

💡 Explanation: Data quality dimensions: Accuracy = data correctly represents real-world values. Completeness = no missing values in required fields. Validity = values conform to defined rules, formats, and ranges (e.g., age 0-120, email format). Consistency = data matches across systems. Timeliness = data is available when needed. Uniqueness = no duplicates. Know all six dimensions and examples of each for the exam.

📊 4 Types of Analytics

📋

Descriptive

What happened?
Summaries & KPIs

🔍

Diagnostic

Why did it happen?
Root cause analysis

🔮

Predictive

What will happen?
Forecasting & ML

🧭

Prescriptive

What should we do?
Recommendations

📈 Chart Selection Quick Guide

📊

Bar Chart

Compare categories

📈

Line Chart

Trends over time

🔵

Scatter Plot

Relationship between 2 variables

📉

Histogram

Distribution of one variable

🥧

Pie Chart

Parts of a whole (proportions)

📦

Box Plot

Distribution & outliers

✅ 6 Data Quality Dimensions

Accuracy

Correct real-world values

Completeness

No missing required data

Validity

Conforms to rules/ranges

Consistency

Matches across systems

Timeliness

Available when needed

Uniqueness

No duplicate records

💡 CompTIA Data+ Exam Tips

Master Chart Selection — It’s #1 on the Exam

Know which chart to use for every scenario. Bar = compare categories, Line = time trends, Scatter = two-variable relationships, Histogram = distribution, Pie = proportions, Box plot = distribution + outliers. Many questions give a scenario and ask which chart is most appropriate.

Know the 4 Analytics Types Cold

Descriptive (what happened), Diagnostic (why), Predictive (what will happen), Prescriptive (what to do). Questions give a business scenario and ask which analytics type applies. Also know statistical concepts: mean, median, mode, correlation, regression, and p-values.

Data Cleaning Is Heavily Tested

Know all cleaning techniques: imputation (fill missing values), deduplication (remove duplicates), normalization (scale data), parsing (extract components), and recoding (change categories). Also understand ETL vs ELT, data warehouse vs data lake, and data governance regulations (GDPR, HIPAA, PCI DSS).