DA0-002 CompTIA Data+ Exam (2025) Exam Topics and Questions

Data Concepts and Environments sets the foundation for everything else on this exam. Before you can acquire, analyse, or visualise data, you need to recognise the landscape it lives in: what types of databases exist, how data is structured and typed, where it comes from, and what tools are used to work with it. The traps here are definitional; candidates who blur the lines between similar concepts lose easy marks.

– Data structures, types, and storage formats

The exam expects you to distinguish between relational and non-relational databases and to know why that distinction matters when choosing a storage approach. Structured data lives in rows and columns; semi-structured data carries its own schema in formats like JSON or XML; unstructured data has no predefined model at all. File extensions are testable too: knowing that a .csv is flat and delimited, while a .parquet is columnar and compressed, is exactly the kind of detail that separates a right answer from a wrong one. Data types (integer, float, string, Boolean, date) appear in transformation and query questions as well, so anchor them early. Storage concepts, including on-premise servers, cloud object storage, and containerised environments, set the context for where all of this data actually sits and how accessible it is to downstream tools.

– Sources, tools, and emerging AI concepts

Data sources range from internal relational databases and flat files to external APIs, web scraping outputs, application logs, and version-controlled repositories. The exam tests whether you can match a source type to a use case, not just name the source. On the tooling side, you are expected to recognise categories: coding environments such as Jupyter notebooks, business intelligence platforms, and dedicated analysis tools each serve different purposes, and confusing them costs marks. AI concepts appear in this topic at an awareness level. You need to identify what a machine learning model does in a data pipeline, what natural language processing is used for, and where robotic process automation fits, without needing to implement any of them. The emphasis is on classification and appropriate use, not on building or tuning models.

How Data Concepts and Environments is tested

At 20% of the exam, this topic generates a meaningful share of items, and most of them are scenario-based recognition tasks. You are shown a situation and asked to identify the correct database type, storage model, or tool category for that context. Items reward precise vocabulary: candidates who treat "structured" and "semi-structured" as interchangeable, or who cannot place a given file format in the right category, will drop marks here consistently.

AI concept items are the most likely to catch candidates off guard because the content feels peripheral. CompTIA tests it at the identify-and-classify level, so the demand is knowing what each technology does in a data workflow, not how it works internally.

The most common mistake is over-generalising. Candidates who know the broad strokes of cloud versus on-premise storage but cannot distinguish between object storage and block storage, for example, will find the distractors convincing. Specificity is what the items reward.

Data acquisition and preparation carries the highest weight on the exam, and for good reason: every analysis depends on data that has been correctly gathered and cleaned. Where the previous topic asked you to recognise environments and tools, this one asks you to act on them. The practical nature of these tasks means items go deeper than definitions; expect to apply logic to messy, realistic data scenarios.

How Data acquisition and preparation is tested

At 22%, this is the single heaviest topic on the exam. Items here are almost always scenario-driven. You are given a data situation with a specific problem and asked what to do next, or asked to identify what went wrong.

For acquisition items, the demand is knowing which integration method or query approach suits the scenario. Candidates who have memorised method names without understanding when to apply them will find the distractors plausible.

Exploration items reward the ability to recognise data quality problems from a description: knowing that a column with 40% null values needs a different response than a column with a single outlier, for example.

Transformation items are where most candidates lose marks. Cleansing, merging, parsing, and formatting each have distinct purposes, and the exam tests whether you can choose the right one for a given problem. Confusing parsing with cleansing, or merging with joining, leads to wrong answers on a topic that makes up nearly a quarter of the exam.

Data Analysis is the largest single topic on this exam. After gathering and preparing data, the job is to draw conclusions from it, communicate those conclusions to the right audience, and do so using methods that are statistically defensible. This topic tests applied thinking: you are not just recalling facts but choosing an approach and justifying it, which is a different cognitive demand from what the earlier topics required.

How Data Analysis is tested

At 24%, this topic alone accounts for nearly a quarter of your score, so a weak performance here is difficult to recover from across the rest of the exam.

Communication items test audience awareness. Items reward choosing a method that matches the technical level and needs of a specific audience, so knowing that an executive summary differs from a technical appendix in more than length is essential.

Statistical method items are where candidates most often lose marks. The exam does not require deep mathematical derivation, but it does expect you to know when to use mean versus median, what standard deviation tells you about spread, and when a correlation does not imply causation. Items reward selecting the technique that fits the data and the question, not just the technique you are most familiar with.

Troubleshooting items present a broken or misleading analysis and ask you to identify the source of the problem. The demand is diagnostic: candidates who can name tools but cannot connect a tool to a specific problem type will find these items difficult.

Visualization and reporting is where analysis becomes something another person can act on. The shift from Data Analysis is a shift in audience: you are no longer reasoning about data yourself but deciding how to present conclusions so that a specific reader understands them. This topic tests judgment about design and delivery, and the traps are subtler than in earlier topics because several options can look defensible at first glance.

– Choosing and designing visuals

The exam tests chart selection at a practical level. A bar chart, a line chart, a scatter plot, and a heat map each serve different purposes, and picking the wrong one for a given data type or audience is a tested mistake. Beyond chart type, design elements such as colour contrast, labelling, and scale affect whether a visual communicates accurately or misleads. Items in this area reward knowing that a truncated y-axis distorts perception, or that a pie chart with too many segments loses meaning, rather than knowing abstract design theory. Maps appear as a specific visual type: choropleth maps and point maps serve different analytical purposes, and the exam expects you to match the map type to the question being answered. Tables are also in scope, particularly when to prefer a table over a chart and how to structure one for clarity.

– Delivering and validating reports

Dashboard delivery and report summarisation are tested through scenario questions: given a stakeholder need, which delivery format is appropriate? A real-time operational dashboard serves a different purpose than a weekly PDF summary, and the exam expects you to articulate that difference in terms of the audience's decision-making cycle. Validation is the area where candidates most often underestimate the demand. Reporting accuracy is not just about checking numbers; it involves reviewing data lineage, confirming that filters are applied correctly, and catching visualisation errors that make accurate data look wrong. Items reward a systematic approach to identifying where a report went wrong, whether the issue is in the underlying data, the aggregation logic, or the visual rendering. Candidates who treat validation as a final check rather than an ongoing discipline tend to miss these questions.

How Visualization and reporting is tested

At 20%, this topic carries the same weight as Data Concepts and Environments, but the item style is different. Most questions present a scenario with a stated audience and a reporting goal, then ask you to select a visual type, a delivery method, or a validation step.

Chart selection items are the most straightforward, but they still catch candidates who have not thought carefully about which chart type suits ordinal versus continuous data, or which format works for comparison versus composition.

Dashboard and delivery items reward matching format to use case. Candidates who default to the most feature-rich option rather than the most appropriate one will find the distractors convincing.

Validation items are the hardest in this topic. They require you to trace a reporting problem back to its source, which demands understanding of the whole pipeline from data to display. Candidates who focus only on visual design and skip the validation sub-topic consistently lose marks here.

Data governance is the lightest topic by weight, but candidates who skip it pay a disproportionate price. After four topics focused on doing things with data, this one focuses on controlling and protecting it. The concepts here, compliance, privacy, quality assurance, and documentation, appear as the background rules that make everything else defensible in a professional setting. Items in this area reward policy thinking over technical execution.

– Documentation, lineage, and compliance

Data management practices such as documentation, versioning, and data lineage are tested at the level of purpose and application. Knowing that data lineage traces the origin and movement of a dataset, and that this matters for auditing and troubleshooting, is more useful on the exam than a textbook definition. Versioning questions typically appear in the context of what goes wrong when it is absent.

Compliance items cover retention policies, audit requirements, and the regulatory context in which data is held. The exam does not require deep legal knowledge, but it does expect you to know what a retention policy governs and why an audit trail is a compliance requirement rather than an optional practice. Regulations such as GDPR appear as named examples, and you need to know broadly what they govern without needing to cite specific articles.

– Privacy, protection, and quality assurance

Privacy and protection strategies are tested through comparison: access control, encryption, and data masking each address a different threat, and the exam asks you to match the strategy to the scenario. Confusing masking with encryption is a common and costly mistake. Access control items tend to focus on the principle of least privilege and role-based access, asking you to identify the appropriate level of access for a given role or situation.

Quality assurance in a governance context means profiling data to understand its characteristics, monitoring it over time for drift or degradation, and testing it against defined standards. Items reward knowing the difference between a profiling activity and a monitoring activity, and between a data quality rule and a business rule. Candidates who treat quality assurance as synonymous with data cleaning, a concept from Topic 2, will conflate two distinct governance functions and lose marks on questions that hinge on that distinction.

How Data governance is tested

At 14%, this topic is the smallest slice of the exam, but its items are often the most policy-oriented and therefore the least intuitive for candidates with a technical background.

Compliance and documentation items reward knowing the purpose of a practice, not just its name. The exam regularly presents a scenario where a governance step was skipped and asks what the consequence is, or presents a requirement and asks which practice satisfies it.

Privacy and protection items are comparison-heavy. The strongest responses identify the right tool for the right threat, rather than the most powerful tool available. Candidates who default to encryption as the answer to every privacy question will miss scenarios where masking or access control is the more appropriate response.

Quality assurance items test whether you understand governance-level quality controls as distinct from preparation-level cleaning. Keeping those two ideas clearly separated in your mind is what the items reward most consistently.