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
- Identify the business question before choosing a tool or chart.
- Name the data type: categorical, numerical, ordinal, time series, structured, semi-structured, or unstructured.
- Check quality: completeness, accuracy, consistency, validity, timeliness, uniqueness, and bias.
- Choose the analysis method that matches the question.
- Choose the visualization that makes the intended comparison obvious.
- 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 |
| 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.
One-line decision rule
Data+ answers should preserve analytic integrity: right question, right data, right method, right visual, and right governance.