CompTIA Data+ (DA0-001) Practice Exam
DA0-001 is the retiring V1 edition of CompTIA Data+ — superseded by Data+ V2 (DA0-002). This page documents the V1 blueprint and sample questions for reference; new candidates should prepare for V2. If you are still finishing V1 study, our practice engine covers all five V1 domains.
Start 24-hour free trial →Data+ V1 (DA0-001) exam at a glance
- Vendor
- CompTIA
- Exam code
- DA0-001 (Data+ V1)
- Certification
- CompTIA Data+
- Level
- Early-career data analytics (associate)
- Status
- Retired — English April 14, 2026; Japanese/Thai July 16, 2026. Superseded by Data+ V2 (DA0-002)
- Format
- Maximum of 90 questions; multiple choice and performance-based
- Duration
- 90 minutes
- Passing score
- 675 on a scale of 100–900
- Delivery
- Pearson VUE test center or online proctored (while available)
- Recommended experience
- 18–24 months in a report/business analyst role; exposure to databases and analytical tools; basic statistics; data-visualization experience
- Prerequisites
- None required (experience recommended, not enforced)
- Successor
- Data+ V2 (DA0-002), launched October 14, 2025
Source: CompTIA — Data+ certification. The Data+ V1 (DA0-001) edition documented here is retired; CompTIA’s Data+ page now serves the current V2 (DA0-002) exam. Verify current details with CompTIA before scheduling.
About CompTIA Data+ (DA0-001)
CompTIA Data+ is a vendor-neutral, early-career data-analytics credential. It validates that you can take a business requirement and turn it into a data-driven answer — acquiring and cleaning data, mining and analysing it, visualising the result, and governing it responsibly. Unlike tool-specific certifications, Data+ deliberately avoids locking you into a single platform, so the concepts transfer across whatever BI or query tool your employer uses.
DA0-001 is the first version (V1) of that exam. CompTIA has retired it in favour of Data+ V2 (DA0-002), which launched on October 14, 2025 and modernised the blueprint — V2 reorganises the domains and folds in explicit AI concepts (AI models, natural language processing, robotic automation), containerization, and a stronger data-acquisition and preparation emphasis. Anyone certifying now should study for V2; DA0-001 material is preserved here for candidates who were mid-way through V1 study and for reference on how the exam evolved.
Every PowerKram question on this page maps to one of the five V1 domains and cites CompTIA’s objectives, so weak spots become a focused reading list. For where a data credential sits among the other CompTIA certifications, see our guide to how IT certifications fit together.
Data+ V1 (DA0-001) exam domains and weights
The V1 exam splits across five weighted domains, summing to 100%. Data Mining is the single heaviest area at 25%, with Data Analysis and Visualization close behind at 23% each — the three together make up over 70% of the exam. (Note: V2 reweights and renames these domains; the figures below are for the retiring V1 exam.)
Acquiring and combining data from multiple sources; exploring data to find missing values, duplication and outliers; and transforming data through cleansing, merging, parsing and formatting so it is fit for analysis.
Applying appropriate statistical methods and analytical techniques to a dataset; interpreting descriptive and inferential results; and communicating those results to the right audience.
Selecting the correct chart or report type for the message; building dashboards; and applying data-storytelling and design principles so an insight is communicated clearly to technical and non-technical stakeholders.
Data schemas and dimensions; data types; common data structures and file formats; and the environments where data lives — databases, data marts, data warehouses and data lakes.
Applying data governance and compliance requirements; controlling access to and protecting sensitive data; and using quality-control techniques such as data validation, profiling and master-data management.
Source: CompTIA — Data+ V1 (DA0-001) exam objectives. Weights are CompTIA’s published V1 figures and total 100%.
Who Data+ is for
CompTIA positions Data+ for early-career data practitioners — people who work with data day to day but are not necessarily statisticians or data scientists. The recommended profile is 18–24 months in a reporting or business-analyst role:
- Business and reporting analysts formalising the analytics skills they already use to build reports and dashboards.
- Data analysts and junior data professionals proving a vendor-neutral foundation before specialising in a particular BI or database stack.
- Operations, finance and marketing staff who work with data-driven decisions and want to demonstrate that competence formally.
- Career changers entering analytics who need a credential that is broad rather than tool-locked.
On the CompTIA data pathway, Data+ is the foundation; the advanced step is DataX (DY0-001) for deeper data-science work. If your interest is the analyst role itself — responsibilities, adjacent skills and pay — review the business analyst career track in our Career Hub.
What this Data+ practice exam delivers
Learn mode
Correct answer, the reasoning, why each distractor fails, and a link to CompTIA’s Data+ objectives — immediately after each question. Best for the Data Mining and Analysis areas, where the right choice turns on a precise technique rather than a keyword.
Exam mode
Up to 90 questions on a 90-minute timer against the 675/900 standard — the real DA0-001 format, including performance-based items. Builds the pacing the timed exam demands.
Source-linked explanations
Every answer cites CompTIA’s official Data+ objectives, so you can verify the concept against the vendor’s own wording and read further rather than memorise.
Score by domain
Results break down across the five V1 domains, so practice tells you whether the next session goes on mining, analysis, visualization, or governance rather than “more questions.”
Sample Data+ (DA0-001) practice questions
Ten free questions spread across the five V1 domains, each with a full explanation and a link to CompTIA’s Data+ objectives. The complete bank comes with the 24-hour trial. Many of these concepts carry directly into Data+ V2.
An organisation stores large volumes of raw, unstructured and semi-structured data in its native format for future, undefined analysis. Which environment best describes this?
- A normalised relational database
- A data mart serving one department’s reporting
- A data lake
- An OLAP cube
Show answer & explanation
Correct: C — a data lake. A data lake stores raw data of any structure in its native format, deferring schema definition until the data is read for a specific use. That “store now, define later” pattern is exactly what the scenario describes.
Why not the others: A relational database (A) requires a defined schema up front and expects structured data. A data mart (B) is a narrow, purpose-built subset for one team’s reporting, not an open store of raw data. An OLAP cube (D) is a pre-aggregated structure for fast multidimensional queries, the opposite of raw and undefined.
Source: CompTIA — Data+ objectives, data concepts and environments →A dataset combines records from three regional systems and contains the same customer listed multiple times with slight spelling differences. Which data-preparation step addresses this?
- Deduplication as part of data cleansing
- Encrypting the customer field
- Archiving the older records
- Adding a new calculated column
Show answer & explanation
Correct: A — deduplication during cleansing. Identifying and resolving duplicate records — including near-duplicates from spelling variations — is a core data-cleansing task in the mining domain. It is the step that makes the combined dataset trustworthy for analysis.
Why not the others: Encryption (B) protects the field but does nothing about duplicates. Archiving older records (C) removes history without resolving which duplicate is correct. A calculated column (D) derives a new value; it does not merge duplicate customers.
Source: CompTIA — Data+ objectives, data mining → Further reading: PowerKram — Data preparation and feature engineering →Before analysis, an analyst finds that 8% of the values in a key numeric column are missing. What is the best first step?
- Delete the entire dataset and request a fresh extract
- Identify and quantify the missing values, then decide on an imputation or exclusion strategy
- Replace every missing value with zero immediately
- Ignore the gaps and proceed to visualisation
Show answer & explanation
Correct: B — quantify first, then choose a strategy. Data exploration in the mining domain calls for assessing the extent and pattern of missing values before acting, so the chosen remedy (impute, drop, or flag) fits the data. Understanding the gap precedes fixing it.
Why not the others: Deleting the dataset (A) is a drastic overreaction to a recoverable problem. Replacing with zero (C) silently distorts every downstream statistic and assumes zero is a valid value. Ignoring the gaps (D) carries incomplete data into analysis and produces misleading results.
Source: CompTIA — Data+ objectives, data exploration → Further reading: PowerKram — Data preparation and feature engineering →A dataset of household incomes contains a few extreme high earners that pull the average upward. Which measure of central tendency best represents the typical household?
- The mean
- The range
- The median
- The standard deviation
Show answer & explanation
Correct: C — the median. The median is the middle value and is resistant to outliers, so it represents the typical household better than the mean when a few extreme values skew the distribution. Choosing the right statistic for skewed data is an analysis-domain skill.
Why not the others: The mean (A) is exactly what the outliers distort. The range (B) measures spread, not a typical value, and is itself highly sensitive to extremes. Standard deviation (D) also measures spread, not central tendency.
Source: CompTIA — Data+ objectives, statistical methods →An analyst wants to quantify the strength and direction of the linear relationship between advertising spend and sales. Which technique fits?
- Data masking
- Deduplication
- Schema normalisation
- Correlation analysis
Show answer & explanation
Correct: D — correlation analysis. Correlation measures the strength and direction of the linear relationship between two variables, which is exactly what “how strongly does spend move with sales” asks for. It is a standard analysis-domain technique.
Why not the others: Data masking (A) obscures sensitive values — a governance control, not an analysis. Deduplication (B) is a cleansing step. Schema normalisation (C) is a database-design activity. None measure a relationship between variables.
Source: CompTIA — Data+ objectives, analysis techniques →An analyst must present monthly revenue for the last three years to show the trend over time to executives. Which visualization is most appropriate?
- A pie chart
- A line chart
- A treemap
- A scatter plot
Show answer & explanation
Correct: B — a line chart. Line charts are the standard choice for showing a continuous measure changing over time, so a 36-month revenue trend reads clearly. Matching chart type to message is the core visualization skill.
Why not the others: A pie chart (A) shows parts of a single whole, not change over time. A treemap (C) shows hierarchy and proportion. A scatter plot (D) shows the relationship between two variables, not a time series.
Source: CompTIA — Data+ objectives, visualization → Further reading: PowerKram — How IT certifications fit together →A dashboard for operations managers is cluttered with every available metric, and users say they cannot find what matters. Which design principle should the analyst apply first?
- Add more charts so nothing is missing
- Switch every chart to 3-D for visual interest
- Use as many colours as possible to separate the metrics
- Prioritise the few metrics tied to the audience’s decisions and remove the rest
Show answer & explanation
Correct: D — prioritise the decision-relevant metrics. Effective dashboards are built around the audience’s decisions; tailoring content to the viewer and removing noise is a named visualization principle. Less, focused, is more usable.
Why not the others: Adding charts (A) worsens the clutter that caused the complaint. 3-D charts (B) distort perception and are widely discouraged. Maximising colours (C) reduces readability and can mislead. All three add noise rather than clarity.
Source: CompTIA — Data+ objectives, reporting and dashboards →A company must restrict a sensitive payroll table so that only authorised HR staff can query it. Which control best enforces this?
- Role-based access control (RBAC)
- Adding more columns to the table
- Defragmenting the database
- Renaming the sensitive columns
Show answer & explanation
Correct: A — role-based access control. RBAC grants data access according to a user’s role, so only authorised HR staff can reach the payroll table. Access control is a core governance-domain protection strategy.
Why not the others: Adding columns (B) changes structure, not who can read the data. Defragmenting (C) is a performance-maintenance task with no security effect. Renaming columns (D) is trivial obscurity that any authorised or unauthorised user can see through.
Source: CompTIA — Data+ objectives, data governance and controls → Further reading: PowerKram — Enterprise security practices →An analytics team must share a customer dataset with an external partner but cannot reveal real names or account numbers, while keeping the data usable for analysis. Which technique fits?
- Increasing storage capacity
- Data masking
- Deleting the customer table
- Building an additional dashboard
Show answer & explanation
Correct: B — data masking. Masking replaces sensitive values with realistic but non-identifying substitutes, so the dataset stays analytically useful without exposing real identities. Protecting sensitive data while preserving usability is a governance-domain objective.
Why not the others: More storage (A) is unrelated to protecting identities. Deleting the table (C) destroys the very data the partner needs. Another dashboard (D) is a visualization task and does nothing to conceal the sensitive fields.
Source: CompTIA — Data+ objectives, protecting sensitive data → Further reading: PowerKram — Enterprise security practices →A manager needs a single analysis-ready table built from a CRM export, a billing system, and a web-analytics feed. Which process combines these sources?
- Data archiving
- Data encryption
- Data integration
- Data retention scheduling
Show answer & explanation
Correct: C — data integration. Integration merges data from multiple sources into a unified, analysis-ready dataset, which is precisely the CRM-plus-billing-plus-web-analytics case. Acquiring and combining data is a data-mining objective.
Why not the others: Archiving (A) moves inactive data to long-term storage. Encryption (B) protects data confidentiality. Retention scheduling (D) governs how long data is kept. None of the three combine sources into one table.
Source: CompTIA — Data+ objectives, data acquisition and integration →Keep going: study guides and career paths
Whether you are finishing V1 or planning for V2, the underlying analytics skills are the same — and they lead somewhere. Two PowerKram hubs back this exam.
Deep dive: V1 vs V2, format and scoring, study path, and where Data+ leads
DA0-001 (V1) vs DA0-002 (V2) — what changed
DA0-001 is retired; DA0-002 (V2) launched October 14, 2025 and is the current exam. V2 keeps the same overall shape — up to 90 questions in 90 minutes, multiple-choice and performance-based — but reorganises and renames the domains: Data concepts and environments (20%), Data acquisition and preparation (22%), Data analysis (24%), Visualization and reporting (20%), and Data governance (14%). V2 also adds explicit AI concepts (AI models, natural language processing, robotic automation) and containerization, and strengthens the data-acquisition emphasis. If you are certifying now, study for V2. Read the IT certifications guide →
Format and scoring (V1)
The V1 exam delivered up to 90 questions in 90 minutes, mixing multiple-choice and performance-based items, and was scored on a 100–900 scale with a passing score of 675. Performance-based questions asked candidates to work through a data task rather than recall a definition, which is why hands-on practice mattered more than memorisation. Read the data preparation guide →
Where most candidates lose marks
On the V1 exam, the three largest domains — Data Mining, Data Analysis and Visualization — together carried over 70% of the questions, so a candidate strong on governance concepts but weak on preparing and analysing real data would still struggle. The recurring trap was treating analysis as recall: knowing that a median resists outliers is different from recognising the skewed scenario that calls for it. Practise choosing the right technique from a described situation, not defining terms in isolation. Read the enterprise security practices guide →
A realistic study path
Data+ candidates typically prepared over eight to twelve weeks, weighted toward the mining, analysis and visualization domains. A practical plan: build a foundation in data concepts, then spend the bulk of your time on hands-on cleansing, transformation and analysis of real datasets, and finish with dashboard-building and timed practice. SQL fluency is the single biggest differentiator for newcomers and carries directly into V2. Governance concepts reward a focused final pass rather than sustained effort. Read the data preparation guide →
Where Data+ leads
Data+ is the foundation of CompTIA’s data pathway. The advanced step is DataX for deeper data-science work; Data+ also pairs naturally with security credentials for data-protection and compliance roles, and with cloud credentials for data-engineering work. As a career asset it is most valuable as vendor-neutral proof of analytics competence for early-career analysts moving toward specialised data roles. See the business analyst career track →
Data+ (DA0-001) exam FAQ
Is the DA0-001 exam still available?
What are the Data+ V1 (DA0-001) domains and weights?
What is the difference between DA0-001 and DA0-002?
What was the Data+ V1 passing score?
Does Data+ have prerequisites?
Do these V1 sample questions still help for V2?
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