๐Ÿ“Š COMPTIA DATA+ ยท DA0-001

CompTIA Data+
Practice Test

Data Analytics Certification Prep | Rontechmedia ยท PracticeTest360.com

90
Questions
90
Minutes
675
Pass Score

โš  Disclaimer

This practice test is provided by Rontechmedia for educational and exam preparation purposes only. This is an unofficial, third-party study resource and is not affiliated with, endorsed by, or sponsored by CompTIA. CompTIAยฎ and Data+ยฎ are registered trademarks of the Computing Technology Industry Association.

  • Questions are original study materials created for preparation purposes only.
  • This test does not guarantee passing the actual CompTIA Data+ exam.
  • Content is based on publicly available CompTIA DA0-001 exam objectives.
  • Always verify information against official CompTIA documentation.

โ„น Exam Format

This practice test mirrors the actual DA0-001 structure:

  • 90 questions across 5 domains with weighted distribution
  • 90-minute countdown timer โ€” auto-submits when time expires
  • Scoring on the 100โ€“900 scale โ€” passing score is 675
  • Instant explanations and real-time domain performance tracking

๐Ÿ“– Study Guide Included

A comprehensive Study Guide covering all 5 DA0-001 domains โ€” data concepts, mining, analysis, visualization, and governance โ€” is available from the Home screen after entering the portal.

CompTIA Data+
DA0-001 Practice Test

Entry-to-intermediate data analytics certification prep. Master data concepts, mining techniques, statistical analysis, visualization best practices, and governance across 90 scenario-based questions.

โฑ 90 Minutes
โ“ 90 Questions
โœ… Passing: 675/900
๐Ÿ”ข Scale: 100โ€“900
๐Ÿ“… DA0-001 (Feb 2022)
๐Ÿ“Š Data Analytics Track
Exam Domains
1. Data Concepts & Environments
15%
~14 questions
2. Data Mining
25%
~22 questions
3. Data Analysis
23%
~21 questions
4. Visualization
23%
~21 questions
5. Data Governance, Quality & Controls
14%
~12 questions

๐Ÿ“Š CompTIA Data+ DA0-001 Study Guide

Comprehensive coverage of all five exam domains. Click each domain to expand key topics, tools, and exam tips.

15%Domain 1: Data Concepts & Environments
โ–ผ

Covers foundational data knowledge: database types, data structures, file formats, schemas, and the environments where data lives. Though the smallest domain, these concepts underpin everything else.

Relational vs. Non-Relational Databases
OLTP vs. OLAP Systems
Data Warehouse vs. Data Lake vs. Data Mart
Structured, Semi-structured, Unstructured Data
Common File Formats (CSV, JSON, XML, Parquet)
Star Schema & Snowflake Schema
Fact Tables & Dimension Tables
Primary Keys, Foreign Keys, Indexes
Data Types (Numeric, String, Boolean, Date)
ETL vs. ELT Pipelines
Cloud Data Environments

Exam Tips

  • OLTP = optimized for transactional reads/writes (e.g., POS systems). OLAP = optimized for analytical queries on large datasets (e.g., BI reports).
  • Data Warehouse = structured, processed, schema-on-write. Data Lake = raw, any format, schema-on-read. Data Mart = subset of warehouse for a specific department.
  • Star schema: central fact table surrounded by denormalized dimension tables. Snowflake: dimensions are normalized into sub-dimensions โ€” slower queries, less storage.
  • ETL = Transform before loading (traditional DW). ELT = Load raw, transform in destination (modern cloud DW like Snowflake, BigQuery).
25%Domain 2: Data Mining
โ–ผ

The largest domain. Covers data acquisition methods, profiling, cleansing, manipulation, SQL querying, and query optimization. Focus heavily on identifying data quality issues and applying correct manipulation techniques.

Data Acquisition (APIs, Web Scraping, Surveys)
Integration Methods (ETL, ELT, Delta Load)
Data Profiling Techniques
Data Cleansing (Duplicates, Missing Values, Outliers)
Data Manipulation (Merge, Blend, Concatenate, Append)
Normalization & Standardization
Imputation of Missing Values
SQL: SELECT, JOIN, WHERE, GROUP BY, HAVING
Aggregate Functions (SUM, AVG, COUNT, MAX, MIN)
Query Optimization (Indexes, Execution Plans)
Parsing & Transposing Data

Exam Tips

  • Data profiling discovers the shape of your data: column types, ranges, nulls, uniqueness, and relationships โ€” done BEFORE cleansing.
  • Imputation fills missing values: mean/median imputation for numeric data; mode imputation or a separate "unknown" category for categorical data.
  • INNER JOIN returns only matching rows. LEFT JOIN returns all rows from the left table, NULLs for no match on right. Know all four join types.
  • Normalization (data prep) scales numeric values to a common range (0โ€“1). Not to be confused with database normalization (reducing redundancy).
  • Delta load = only new/changed records since last extraction. Faster than full extraction for large datasets.
23%Domain 3: Data Analysis
โ–ผ

Covers descriptive and inferential statistics, hypothesis testing, regression, forecasting, and translating analytical results into business insights. Tests your ability to apply the right analysis method to a given scenario.

Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, Std Dev)
Descriptive vs. Inferential Statistics
Correlation vs. Causation
Linear Regression & Forecasting
Hypothesis Testing (Null / Alternative Hypotheses)
p-Value & Statistical Significance
Confidence Intervals
Time-Series Analysis & Trend Analysis
Segmentation & Cohort Analysis
Percent Change & Percent of Total

Exam Tips

  • Mean is sensitive to outliers; median is resistant. Use median when data is skewed. Mode is for categorical data.
  • Standard deviation measures spread โ€” how far values typically deviate from the mean. Low SD = clustered; high SD = spread out.
  • Correlation shows relationship between variables (โˆ’1 to +1). Causation requires controlled experiments. Correlation โ‰  causation.
  • p-value < 0.05 typically means the result is statistically significant โ€” you reject the null hypothesis.
  • Confidence interval: "We are 95% confident the true value falls between X and Y." Wider interval = less precise = smaller sample.
23%Domain 4: Visualization
โ–ผ

Covers selecting appropriate chart types, dashboard design principles, storytelling with data, and translating business requirements into visualizations. Equal weight to Data Analysis โ€” know your chart types and when to use them.

Chart Type Selection (Bar, Line, Scatter, Pie, Heatmap)
When to Use Each Chart Type
Dashboard Design Principles
KPIs & Metrics
Data Storytelling & Narrative
Audience-Appropriate Reporting
Comparison vs. Distribution vs. Relationship Charts
Sparklines, Gauges, & Bullet Charts
Color Theory in Data Visualization
Accessibility in Visualizations
BI Tools (Tableau, Power BI, Excel)

Exam Tips

  • Bar charts: compare categories. Line charts: show trends over time. Scatter plots: show correlation between two variables. Pie charts: show parts of a whole (use sparingly โ€” limited to few categories).
  • Heatmaps show intensity/density across two dimensions using color. Box plots show distribution, quartiles, and outliers.
  • Avoid pie charts with more than 5 slices โ€” use a bar chart instead. 3D charts distort perception and should generally be avoided.
  • KPIs must be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART). Dashboards should lead with the most important KPIs.
  • Consider your audience: executives need high-level summaries; analysts need drill-down capability and raw data access.
14%Domain 5: Data Governance, Quality & Controls
โ–ผ

Covers data governance frameworks, data quality dimensions, master data management (MDM), data classification, access controls, privacy regulations, and breach reporting. Smallest domain but highly testable on governance concepts.

Data Governance Framework
Data Quality Dimensions (Accuracy, Completeness, Consistency)
Data Stewardship & Data Ownership
Master Data Management (MDM)
Data Classification (Public, Internal, Confidential)
Data Lineage & Provenance
Access Controls & Row-Level Security
Privacy Regulations (GDPR, CCPA, HIPAA)
Data Retention & Disposal Policies
Breach Reporting Requirements
Data Validation Rules & Quality Checks

Exam Tips

  • Data quality dimensions: Accuracy (correct), Completeness (not missing), Consistency (same across systems), Timeliness (current), Uniqueness (no duplicates), Validity (conforms to rules).
  • MDM creates a single "golden record" (system of record) for key business entities like customers, products, and employees across multiple systems.
  • Data steward = manages day-to-day data quality. Data owner = accountable for data, sets policies. Data custodian = IT, implements technical controls.
  • Data lineage tracks the origin, movement, and transformation of data throughout its lifecycle โ€” critical for debugging data quality issues.
QUESTION 1 OF 90
DATA CONCEPTS
Q.001
Explanation

โ— PASS
820
SCALED SCORE (100โ€“900)
Passing Score: 675 | CompTIA Data+ DA0-001
Domain Performance

๐Ÿ“‹ Answer Review

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