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CompTIA DA0-002 Cheat Sheet: Data, Quality, and Visualization

April 24, 2026

CompTIA DA0-002 cheat sheet for data, quality, visualization, traps, and final review.

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Use this cheat sheet for CompTIA Data+ V2 (DA0-002) after you know the vocabulary and need sharper exam decisions. Data+ questions reward disciplined analysis: define the question, validate the source, clean the data, choose the right method, communicate the result, and protect the data.

Read every Data+ question in this order

  1. Identify the business question before choosing a tool or chart.
  2. Name the data type: categorical, numerical, ordinal, time series, structured, semi-structured, or unstructured.
  3. Check quality: completeness, accuracy, consistency, validity, timeliness, uniqueness, and bias.
  4. Choose the analysis method that matches the question.
  5. Choose the visualization that makes the intended comparison obvious.
  6. Add governance: privacy, access, retention, lineage, documentation, and ethical use.

DA0-002 answer sequence

Use this when the stem mixes business question, data type, quality, method, visualization, or governance.

    flowchart TD
	  S["Scenario"] --> Q["Identify the business question"]
	  Q --> T["Name the data type"]
	  T --> L["Check data quality"]
	  L --> M["Choose the analysis method"]
	  M --> V["Choose the clearest visualization"]

Data type and storage chooser

Requirement Better fit Watch for
relational records with joins relational database keys, normalization, referential integrity, SQL
analytical queries across large datasets data warehouse schema design, aggregation, historical data, BI
raw multi-format storage data lake governance, cataloging, quality, access, lifecycle
semi-structured events JSON, XML, logs, document stores schema drift and parsing logic
time-based measurement time series trend, seasonality, interval, and missing-period handling
business glossary and ownership data governance catalog lineage, definitions, steward, and policy

Cleaning and transformation map

Problem Strong answer pattern
missing values decide whether to remove, impute, flag, or investigate based on context
duplicates define uniqueness rule before deduplicating
inconsistent formats standardize date, currency, casing, units, and category labels
outliers investigate first; do not automatically delete
wrong granularity aggregate or disaggregate only when the business question supports it
join mismatch check keys, cardinality, nulls, duplicates, and join type
reproducibility concern document transformation steps and assumptions

Statistics and interpretation

Concept Fast distinction
mean sensitive to outliers
median more robust for skewed distributions
mode most frequent value; useful for categorical data
range simple spread, highly outlier-sensitive
standard deviation dispersion around the mean
correlation relationship strength, not causation
confidence interval range of plausible values for an estimate
p-value evidence against a null hypothesis, not business importance

Chart chooser

Need Better chart
compare categories bar chart
show trend over time line chart
show composition stacked bar, treemap, or limited pie only when simple
show distribution histogram, box plot
show relationship scatter plot
show ranking sorted bar chart
show geographic pattern map only when location is central to the question
show key performance state KPI card plus context, not a lonely number

Dashboard and reporting checklist

Question Exam instinct
who is the audience? executive, manager, analyst, operator, or customer
what action should follow? prioritize only the metrics that support that action
what is the time frame? define current period, comparison period, refresh cadence, and latency
what is the denominator? ratios and rates often matter more than counts
what can be filtered? keep filters meaningful and controlled
what can be misunderstood? label axes, units, assumptions, and definitions

Governance and ethics

Risk Control
unauthorized access role-based access, least privilege, and review
sensitive data exposure classification, masking, anonymization, encryption, and approved sharing
misleading analysis source notes, assumptions, confidence, sample size, and limitations
stale reporting refresh schedule, data lineage, and owner accountability
biased result representative data, segmentation, and fairness review
poor auditability documented transformations, definitions, and versioned reports

Common traps

Trap Better instinct
tool-first answer start with question, data, quality, and audience
average-only reasoning check median, distribution, outliers, and segments
correlation equals causation require design, evidence, or controlled analysis before causation
pretty chart over clear chart choose the chart that makes the comparison easiest
cleaning without documentation transformation choices are part of the analysis evidence
governance after publishing privacy and access decisions happen before analysis is shared

Final 15-minute review

If the stem says… Start here
bad data quality dimension, root cause, cleaning choice, and documentation
compare groups categorical field, metric, denominator, and bar chart
trend time grain, seasonality, missing periods, and line chart
relationship scatter plot, correlation, confounders, and causation warning
executive dashboard KPI, context, action, target, trend, and concise visual
sensitive data classification, masking, access, retention, and ethics

Practice fit

Use IT Mastery for the exact product route, practice status, spaced review when available, and close-answer explanation practice as coverage expands.

Open the exact IT Mastery route here: DA0-002 on MasteryExamPrep.

One-line decision rule

Data+ answers should preserve analytic integrity: right question, right data, right method, right visual, and right governance.

Revised on Sunday, May 10, 2026
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