Are flashcards effective for the Data+ exam?

Yes. Flashcards use active recall — retrieving an answer from memory — which research shows is one of the most effective study methods, especially in short, spaced sessions. They're ideal for the Data+'s heavy load of definitions: statistics terms, chart types, ETL concepts, and governance acronyms.

What topics do the Data+ flashcards cover?

All five DA0-002 domains: Data Concepts & Environments (databases, warehouses, lakes, data types), Data Acquisition & Preparation (ETL/ELT, cleansing, joins), Data Analysis (mean/median/mode, standard deviation, correlation, analytics types), Visualization & Reporting (chart selection, KPIs, dashboards), and Data Governance (quality dimensions, MDM, privacy).

How should I study with these Data+ flashcards?

Lead with the heaviest domains — Data Analysis (24%) and Data Acquisition & Preparation (22%). Drill the statistics measures and chart-selection cards with Type and Quiz modes until automatic, then mix in the other domains. Pair the cards with our full practice test before exam day.

Are the Data+ flashcards free?

Yes — 100% free, all four study modes, no paywall and no sign-up.

Do these flashcards match the current Data+ exam blueprint?

Yes. The cards are organized to the current CompTIA Data+ DA0-002 (V2) exam objectives — the version that replaced DA0-001 — covering all five scored domains in their official proportions.

Data in a defined schema of rows and columns, like a relational database table.

Data with no predefined model — text, images, audio, video.

Data with tags/markers (JSON, XML) but no rigid table structure.

Descriptive, categorical data (nominal or ordinal).

Numeric, measurable data (interval or ratio) that supports math.

Categorical data with no inherent order (e.g., colors, countries).

Categorical data with a meaningful order but unequal gaps (e.g., poor/fair/good).

Numeric data with order and equal gaps but no true zero (e.g., °C).

Numeric data with a true zero, allowing all arithmetic (e.g., sales, height).

Countable data taking whole-number values (e.g., number of orders).

Measurable data taking any value in a range (e.g., temperature, weight).

A store organizing data into related tables linked by keys; queried with SQL.

A column whose value uniquely identifies each row in a table.

A column referencing another table's primary key, enforcing relationships.

Normalization (database)

Organizing tables to reduce redundancy and improve integrity.

Adding redundancy to a schema to speed up analytical reads.

Online Transaction Processing — fast, small reads/writes that run daily operations.

Online Analytical Processing — complex queries/aggregations over large historical data.

A central analytical store of structured, modeled data (schema-on-write).

A subject-specific subset of a data warehouse for one department.

A repository holding vast raw data in native format (schema-on-read).

A hybrid adding warehouse structure/management on top of a data lake.

Structure is defined before loading (data warehouse).

Structure is applied only when the data is queried (data lake).

A warehouse design: a central fact table linked to dimension tables.

A star schema whose dimension tables are further normalized.

A warehouse table of measurable events/metrics, with keys to dimensions.

A warehouse table of descriptive attributes used to filter/group facts.

Data about data — descriptions, types, source, and definitions of fields.

Datasets too large/complex for traditional tools — the V's.

The sheer scale of data generated and stored.

The speed at which data is generated and must be processed.

The range of data formats — structured, semi-, unstructured.

The trustworthiness and quality of the data.

The business worth extracted from the data.

Data analytics lifecycle

Question → acquire → prepare → analyze → visualize → communicate/act.

Cloud data environment

Storing and processing data on managed cloud platforms for scale/elasticity.

Data stored and processed on an organization's own hardware.

Any origin of data — database, file, API, sensor, survey, web.

A record (row) is one entity; a field (column) is one attribute of it.

A plain file (e.g., CSV) storing data with no relational structure.

Comma-Separated Values — a common flat-file format for tabular data.

JavaScript Object Notation — a lightweight semi-structured data format.

Extensible Markup Language — a tag-based semi-structured data format.

Structured Query Language — the language for querying relational databases.

Your FREE CompTIA Data+ Flashcards 2026 – 250+ Cards

Realistic, CompTIA Data+ (DA0-002) exam-style flashcards across all 5 domains — flip, match, type, and quiz yourself.

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CompTIA Data+ Flashcards

Structured data: Data in a defined schema of rows and columns, like a relational database table.
Unstructured data: Data with no predefined model — text, images, audio, video.
Semi-structured data: Data with tags/markers (JSON, XML) but no rigid table structure.
Qualitative data: Descriptive, categorical data (nominal or ordinal).
Quantitative data: Numeric, measurable data (interval or ratio) that supports math.
Nominal data: Categorical data with no inherent order (e.g., colors, countries).
Ordinal data: Categorical data with a meaningful order but unequal gaps (e.g., poor/fair/good).
Interval data: Numeric data with order and equal gaps but no true zero (e.g., °C).
Ratio data: Numeric data with a true zero, allowing all arithmetic (e.g., sales, height).
Discrete data: Countable data taking whole-number values (e.g., number of orders).
Continuous data: Measurable data taking any value in a range (e.g., temperature, weight).
Relational database: A store organizing data into related tables linked by keys; queried with SQL.
Primary key: A column whose value uniquely identifies each row in a table.
Foreign key: A column referencing another table's primary key, enforcing relationships.
Normalization (database): Organizing tables to reduce redundancy and improve integrity.
Denormalization: Adding redundancy to a schema to speed up analytical reads.
OLTP: Online Transaction Processing — fast, small reads/writes that run daily operations.
OLAP: Online Analytical Processing — complex queries/aggregations over large historical data.
Data warehouse: A central analytical store of structured, modeled data (schema-on-write).
Data mart: A subject-specific subset of a data warehouse for one department.
Data lake: A repository holding vast raw data in native format (schema-on-read).
Data lakehouse: A hybrid adding warehouse structure/management on top of a data lake.
Schema-on-write: Structure is defined before loading (data warehouse).
Schema-on-read: Structure is applied only when the data is queried (data lake).
Star schema: A warehouse design: a central fact table linked to dimension tables.
Snowflake schema: A star schema whose dimension tables are further normalized.
Fact table: A warehouse table of measurable events/metrics, with keys to dimensions.
Dimension table: A warehouse table of descriptive attributes used to filter/group facts.
Metadata: Data about data — descriptions, types, source, and definitions of fields.
Big data: Datasets too large/complex for traditional tools — the V's.
Volume (big data): The sheer scale of data generated and stored.
Velocity (big data): The speed at which data is generated and must be processed.
Variety (big data): The range of data formats — structured, semi-, unstructured.
Veracity (big data): The trustworthiness and quality of the data.
Value (big data): The business worth extracted from the data.
Data analytics lifecycle: Question → acquire → prepare → analyze → visualize → communicate/act.
Cloud data environment: Storing and processing data on managed cloud platforms for scale/elasticity.
On-premises data: Data stored and processed on an organization's own hardware.
Data source: Any origin of data — database, file, API, sensor, survey, web.
Record vs. field: A record (row) is one entity; a field (column) is one attribute of it.
Flat file: A plain file (e.g., CSV) storing data with no relational structure.
CSV: Comma-Separated Values — a common flat-file format for tabular data.
JSON: JavaScript Object Notation — a lightweight semi-structured data format.
XML: Extensible Markup Language — a tag-based semi-structured data format.
SQL: Structured Query Language — the language for querying relational databases.
NoSQL database: A non-relational store (document, key-value, graph, column) for flexible/large data.
ETL: Extract, Transform, Load — clean/shape data before loading (classic warehouse).
ELT: Extract, Load, Transform — load raw first, transform in the target (cloud/lake).
Data integration: Combining data from multiple sources into one unified store.
Data ingestion: Bringing data into a system from its sources (batch or streaming).
Batch processing: Processing data in scheduled, grouped chunks.
Stream processing: Processing data continuously as it arrives in real time.
API: Application Programming Interface — a defined way for systems to exchange data.
Web scraping: Extracting data from web pages programmatically.
Data acquisition: Gathering data from sources: databases, APIs, files, surveys, sensors.
Sampling: Selecting a subset of data to represent a larger population.
Random sampling: Each member of the population has an equal chance of selection.
Sampling bias: A non-representative sample that distorts conclusions.
Data cleansing: Detecting and fixing errors/inconsistencies to improve quality.
Missing value: An absent entry; handled by deletion or imputation.
Imputation: Filling missing values using a strategy (mean, median, predicted).
Deduplication: Removing duplicate records, often via a unique key.
Outlier: A value far outside the typical range; may be an error or true extreme.
Sentinel value: A placeholder (999, -1, 'N/A') that stands in for missing/invalid data.
Normalization (scaling): Rescaling numeric values to a fixed range, typically 0 to 1.
Standardization (scaling): Rescaling values to mean 0 and standard deviation 1 (z-score).
Data type conversion: Casting a value from one type to another (e.g., text to date).
Parsing: Breaking raw text into structured fields (e.g., splitting a full name).
Recoding: Replacing values with standardized codes or categories.
Data validation: Checking that data conforms to defined rules and formats.
Data profiling: Examining data to summarize its content, structure, and quality.
Data blending: Combining data from different sources for a single analysis.
Data transformation: Converting data into a structure/format suitable for analysis.
Aggregation: Summarizing detailed data into totals (sum, count, average) by group.
Filtering: Keeping only rows that meet a condition.
Sorting: Ordering rows by one or more columns.
Join: Combining rows from two tables based on a related key.
Inner join: Returns only rows with matching keys in both tables.
Outer join: Returns matching rows plus unmatched rows from one or both tables.
Pivot / transpose: Reshaping data by turning rows into columns or vice versa.
Data mining: Discovering patterns/relationships in large datasets.
Classification: Supervised technique assigning records to known categories.
Regression (mining): Supervised technique modeling a numeric relationship for prediction.
Clustering: Unsupervised technique grouping similar records with no labels.
Association rules: Finding items that frequently co-occur (market-basket analysis).
Apriori algorithm: A classic algorithm for mining frequent itemsets / association rules.
Supervised learning: Learning from labeled data (classification, regression).
Unsupervised learning: Finding structure in unlabeled data (clustering, association).
Overfitting: A model that memorizes training data and fails on new data.
Training data: The dataset used to build (fit) a model.
Feature: An input variable (column) used in analysis or a model.
Descriptive statistics: Statistics that summarize a dataset (center and spread).
Mean: The arithmetic average of all values; sensitive to outliers.
Median: The middle value of sorted data; robust to outliers.
Mode: The most frequently occurring value.
Range: The difference between the maximum and minimum values.
Variance: A measure of spread — the average squared distance from the mean.
Standard deviation: Spread around the mean, expressed in the data's own units (variance has squared units).
Percentile: The value below which a given percentage of data falls.
Quartile: Values splitting sorted data into four equal parts (Q1, Q2, Q3).
Interquartile range (IQR): Q3 minus Q1 — the middle 50% of the data.
Central tendency: The center of a distribution — mean, median, or mode.
Skewness: The asymmetry of a distribution (left- or right-skewed).
Right-skewed distribution: A long tail to the right; mean sits above the median.
Left-skewed distribution: A long tail to the left; mean sits below the median.
Normal distribution: A symmetric bell curve; ~68% of values within 1 standard deviation.
Frequency: How often a value or category appears in a dataset.
Correlation: How two variables move together, from -1 to +1.
Correlation coefficient: A number from -1 to +1 measuring linear relationship strength/direction.
Positive correlation: Both variables move in the same direction.
Negative correlation: One variable rises as the other falls.
Causation: One variable directly causes a change in another; not proven by correlation.
Correlation vs. causation: Correlation shows variables move together; it does not prove cause.
Confounding variable: A hidden third factor that influences both correlated variables.
Hypothesis testing: Deciding whether sample evidence supports a claim about a population.
Null hypothesis: The default claim of no effect/difference, which a test tries to reject.
p-value: Probability of results this extreme if the null hypothesis is true.
Significance level (alpha): The threshold (often 0.05) for rejecting the null hypothesis.
Confidence interval: A range of plausible values for a population parameter.
Regression analysis: Modeling how a dependent variable changes with predictors.
Linear regression: Fitting a straight line to model a relationship between variables.
Trend analysis: Identifying the direction of data over time.
Time-series analysis: Analyzing data points ordered by time to find patterns/seasonality.
Cohort analysis: Comparing groups that share a characteristic over time.
Exploratory data analysis (EDA): Initial investigation to summarize and visualize data's main features.
Descriptive analytics: Analytics that summarizes what happened (reports, KPIs).
Diagnostic analytics: Analytics that explains why something happened.
Predictive analytics: Analytics that forecasts what is likely to happen.
Prescriptive analytics: Analytics that recommends what action to take.
Analytics maturity ladder: Descriptive → diagnostic → predictive → prescriptive (rising value).
Outlier (analysis): An extreme value that can distort the mean and other statistics.
Variability: How spread out the values in a dataset are.
Weighted average: An average where some values count more than others.
Statistical significance: A result unlikely to be due to chance alone.
Sample vs. population: A sample is a subset; a population is the entire group of interest.
Bar chart: Compares values across distinct categories (bars have gaps).
Column chart: A vertical bar chart comparing categories.
Line chart: Shows a trend in a value over time.
Pie chart: Shows parts of a whole as slices; best for few categories.
Stacked bar chart: Shows parts of a whole within each category bar.
Scatter plot: Plots two numeric variables as points to show their relationship.
Bubble chart: A scatter plot encoding a third variable as point size.
Histogram: Shows the distribution of one continuous variable using bins (bars touch).
Box plot: Shows a distribution's median, quartiles, and outliers.
Pareto chart: Bars ordered largest-to-smallest with a cumulative line (80/20).
Heat map: Uses color intensity to show magnitude across two dimensions.
Tree map: Shows hierarchical parts of a whole as nested rectangles.
Waterfall chart: Shows how an initial value rises/falls through sequential changes.
Geographic / choropleth map: Shows data by location using shading or markers.
Gantt chart: A bar chart of tasks over time, used for project schedules.
Bar vs. histogram: Bar compares categories (gaps); histogram shows distribution (bars touch).
Chart selection: Match the chart to the goal: compare, trend, part-of-whole, relationship, distribution.
Dashboard: An interactive at-a-glance display of key metrics and visuals.
KPI: Key Performance Indicator — a measurable value showing progress to a goal.
Metric: A quantifiable measure used to track performance.
Drill-down: Navigating from a summary into more detailed data.
Filter (visualization): Limiting a view to data meeting a condition.
Ad hoc report: A one-off report created to answer a specific question.
Recurring report: A scheduled report produced regularly for monitoring.
Self-service analytics: Tools letting non-analysts explore data on their own.
Data storytelling: Communicating insight with a clear narrative around the visuals.
Chartjunk: Decorative clutter that distracts from a chart's message.
Misleading axis: Truncating or distorting an axis to exaggerate differences.
Truncated y-axis: A bar-chart axis not starting at zero, overstating differences.
Annotation: A note added to a chart to highlight or explain a point.
Legend: A key explaining the colors/symbols used in a chart.
Trend line: A line summarizing the direction of points in a scatter plot.
Outlier (visualization): A point far from the others, easily spotted on scatter/box plots.
Audience-appropriate reporting: Tailoring detail and KPIs to who will read the report.
Sparkline: A tiny inline chart showing a trend without axes.
Gauge chart: A dial showing a single value against a target range.
Funnel chart: Shows values dropping through sequential stages (e.g., a sales funnel).
Color encoding: Using color to represent a category or magnitude in a visual.
Accessibility (visuals): Designing charts readable by all, e.g., colorblind-safe palettes.
Report vs. dashboard: Reports are detailed/static; dashboards are summary/interactive.
Data governance: Policies, roles, and standards controlling data across its lifecycle.
Data owner: The person accountable for a data domain and its policy.
Data steward: The person responsible for day-to-day data quality and proper use.
Data custodian: The role managing the technical storage and security of data.
Master Data Management (MDM): Maintaining one authoritative 'golden record' per core entity.
Golden record: The single, trusted version of an entity across all systems.
Data catalog: An organized inventory of data assets with descriptions/metadata.
Data lineage: A record of data's origin and how it moves/transforms through systems.
Data dictionary: Documentation defining each field's name, type, and meaning.
Data quality: The degree to which data is fit for its intended purpose.
Accuracy (quality): Values correctly reflect the real-world fact.
Completeness (quality): No required values are missing.
Consistency (quality): Values agree across systems and records.
Timeliness (quality): Data is current and available when needed.
Uniqueness (quality): No unintended duplicate records exist.
Validity (quality): Values conform to the defined format and rules.
Integrity (quality): Relationships between data are correctly maintained.
PII: Personally Identifiable Information — data that can identify an individual.
PHI: Protected Health Information — health data covered by HIPAA.
Data classification: Labeling data by sensitivity to apply the right controls.
Data masking: Replacing sensitive values with realistic fakes for safe use.
Anonymization: Removing identifiers so individuals cannot be re-identified.
Pseudonymization: Replacing identifiers with reversible tokens.
Encryption at rest: Encrypting stored data so it can't be read if accessed.
Encryption in transit: Encrypting data as it moves across a network.
Access control: Limiting who can view or change data (least privilege).
Least privilege: Granting only the access a role actually needs.
Role-based access control (RBAC): Granting permissions based on a user's role.
Data retention: Keeping data only as long as required by policy or law.
Data disposal: Securely destroying data that is no longer needed.
GDPR: EU General Data Protection Regulation governing personal-data privacy.
HIPAA: U.S. law protecting health information (PHI).
PCI-DSS: The security standard for handling payment-card data.
CCPA: The California Consumer Privacy Act governing consumer data rights.
Compliance: Adhering to laws, regulations, and internal policies for data use.
Data privacy: The right to control how personal data is collected and used.
Data security: Protecting data from unauthorized access, change, or loss.
Audit trail: A record of who accessed or changed data and when.
Data breach: Unauthorized access to or disclosure of protected data.
Data ethics: Responsible, fair, and transparent use of data.
Consent: A person's permission to collect and use their personal data.
Data minimization: Collecting only the personal data actually needed.
Master data: The core, shared entities of a business (customers, products, suppliers).
Reference data: Standardized lookup values used across systems (e.g., country codes).
Database vs. data warehouse: A database runs operations (OLTP); a warehouse is built for analysis (OLAP).
ER diagram: Entity-Relationship diagram — a visual model of tables and their relationships.
Cardinality: The nature of a relationship between tables (one-to-one, one-to-many, many-to-many).
Index (database): A structure that speeds up lookups on a column at the cost of extra storage.
Query: A request to retrieve or manipulate data, typically written in SQL.
ACID properties: Atomicity, Consistency, Isolation, Durability — guarantees of reliable transactions.
Data type: The kind of value a field holds (integer, string, date, boolean, float).
Boolean data: A value that is either true or false.
Streaming data: Data generated continuously in real time (e.g., sensor or clickstream data).
Data integrity: Maintaining accuracy and consistency of data over its lifecycle.
Schema: The defined structure of a database — its tables, fields, types, and relationships.
Surrogate key: An artificial unique key (e.g., an auto-number) with no business meaning.
Granularity: The level of detail stored in a dataset (e.g., daily vs. monthly).
Dimensional modeling: Designing a warehouse around facts and dimensions for analysis.
Data wrangling: The hands-on work of cleaning and reshaping raw data for analysis.
Data pipeline: An automated series of steps that moves and transforms data.
Source system: The original system where data is created or first captured.
Staging area: A temporary store where data lands before transformation/loading.
Data enrichment: Adding value to data by combining it with additional sources.
Concatenation: Joining text fields end to end (e.g., first + last name).
Binning: Grouping continuous values into ranges (bins/buckets).
Encoding (categorical): Converting categories into numeric form for analysis/modeling.
Survey data: Data collected directly from respondents via questionnaires.
Observational data: Data collected by watching/measuring without intervention.
Structured query: A precise data request using SQL filters, joins, and aggregations.
Left join: Returns all rows from the left table plus matches from the right.
Union: Stacks the rows of two result sets with matching columns.
Cross-validation: Splitting data to test how well a model generalizes.
Decision tree: A model that splits data on feature values to classify or predict.
Inferential statistics: Using a sample to draw conclusions about a population.
Descriptive vs. inferential: Descriptive summarizes data; inferential generalizes to a population.
t-test: A test for whether two group means differ significantly.
Chi-square test: A test for association between two categorical variables.
Dependent variable: The outcome being measured or predicted.
Independent variable: An input thought to influence the outcome.
R-squared: The share of variance in the outcome explained by a regression model.
Forecasting: Predicting future values from historical data.
Seasonality: Repeating patterns in time-series data tied to the calendar.
Moving average: An average over a sliding window that smooths short-term noise.
Bias (statistical): A systematic error that skews results away from the truth.
Margin of error: The range of uncertainty around a survey or estimate.
Distribution: How values of a variable are spread across their range.
Aggregate function: A calculation over many rows: SUM, COUNT, AVG, MIN, MAX.
Key driver analysis: Identifying which variables most influence an outcome.
Stacked area chart: Shows the trend of parts of a whole over time.
Combo chart: Combines two chart types (e.g., bars + line) on one axis set.
Dual-axis chart: Plots two measures with different scales on separate y-axes.
Data label: Text on a chart showing a point's exact value.
Axis scale: The range and intervals of a chart axis (linear or log).
Categorical axis: An axis listing discrete categories (e.g., regions).
Continuous axis: An axis representing a numeric range.
Visualization best practice: Clear labels, honest scales, the right chart, minimal clutter.
Highlighting: Emphasizing key data points with color or annotation.
Interactive report: A report users can filter, sort, and drill into.
Static report: A fixed report that does not change after it is produced.
Executive dashboard: A high-level dashboard of strategic KPIs for leaders.
Operational dashboard: A real-time dashboard for monitoring day-to-day activity.
3-D chart pitfall: Adding 3-D effects that distort proportions and mislead readers.
Data lifecycle: The stages of data from creation through use, storage, and disposal.
Data quality dimension: An attribute used to measure quality (accuracy, completeness, etc.).
Data quality rule: A defined check data must pass (e.g., 'email must contain @').
Stewardship: Day-to-day responsibility for a data domain's quality and use.
Regulatory compliance: Meeting legal requirements for how data is handled.
Sensitive data: Data requiring extra protection (PII, PHI, financial, credentials).
Tokenization: Replacing sensitive data with a non-sensitive token reference.
Data sovereignty: The principle that data is subject to the laws of where it is stored.
Privacy by design: Building privacy protections into systems from the start.
Right to be forgotten: A GDPR right to have one's personal data erased.
Data quality assessment: Measuring data against quality dimensions and rules.
Confidentiality: Ensuring data is accessible only to authorized parties.
Availability (data): Ensuring data is accessible to authorized users when needed.
Data archiving: Moving inactive data to long-term, lower-cost storage.

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By Career EmployerUpdated Jun 19, 2026

250+ free Data+ flashcards — 4 ways to study

Data+ Flashcard of the Day

The classic card. Do you know this one?

Click Study Flashcards above to open the flashcard hub — hundreds of Data+ cards you can flip, match, type, or quiz yourself on. Every card is drawn from the five official DA0-002 domains, so you study exactly what the exam tests.^[1] Pair them with our free practice test and study guide.

CompTIA Data+ is one of the 14 CompTIA certifications — explore our CompTIA flashcards to compare and prep across the whole family.

Data+ Flashcard Study Modes

Most flashcard sites give you one thing: a card to flip. Ours has four modes so you can both learn the material and prove you know it — the difference between recognizing an answer and recalling it under exam pressure.

Flip (Study) — the classic card. Flip term ↔ definition, shuffle the deck, and mark each card “Got it” or “Still learning.”
Match (Game) — a timed game: pair each term to its definition as fast as you can. Great for cementing statistics terms, chart types, and acronyms.
Type (Recall) — read the definition and type the term. Typing forces true active recall instead of passive recognition.
Quiz (Test) — multiple-choice questions generated from the cards, so you self-test exactly like exam day.

Free CompTIA Data+ flashcards from Career Employer — active recall for the DA0-002 exam

Why Flashcards Work for the Data+

Flashcards aren’t busywork — they’re built on active recall: pulling an answer out of memory strengthens it far more than re-reading notes. Pair that with spacing — short sessions across several days rather than one cram — and you retain more in less time.

The Data+ is dense with things you simply have to know cold — statistics measures, the four analytics types, chart selection, ETL versus ELT, data-quality dimensions, and privacy acronyms.^[1] Spaced flashcards are the most efficient way to keep it all fresh. Used alongside our practice test and study guide, they turn review time into measurable progress.

Data+ Flashcards by Domain

The cards are organized by the five official DA0-002 domains. Drill the highest-weighted ones first — Data Analysis and Data Acquisition & Preparation make up nearly half the exam:^[1]

Data+ flashcards by domain and weight
Domain	Exam weight
Data Analysis	24%
Data Acquisition & Preparation	22%
Data Concepts & Environments	20%
Visualization & Reporting	20%
Data Governance, Quality & Controls	14%

How to Get the Most Out of These Flashcards

Lead with the heavy domains. Data Analysis (24%) and Data Acquisition & Preparation (22%) are nearly half the exam — start there.
Memorize the statistics & chart cards. Use Type and Quiz on measures of central tendency, standard deviation, and chart selection until you can recall them without hesitation.
Drill your weakest domain. Pick a single domain deck and grind it until the Match time drops and the Quiz score climbs.
Then prove it. When the cards feel easy, confirm with a full practice test — aim comfortably above passing before exam day.

Data+ Flashcards FAQ

Hundreds of free Data+ flashcards, organized across all five DA0-002 domains — Data Concepts & Environments, Data Acquisition & Preparation, Data Analysis, Visualization & Reporting, and Data Governance. They're free with no account required.

References

1.CompTIA. “CompTIA Data+ (DA0-002) Certification — Exam Details & Objectives.” comptia.org. ↑
2.CompTIA. “CompTIA Data+ — Your Questions Answered (FAQ).” comptia.org. ↑

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