CompTIA Data+ DA0-001 (V1) Performance-Based Questions (PBQs)

Data+ (V1) exam objectives summary

Data concepts and environments (15%)

• Data schemas and dimensions: identifying databases, data marts, data warehouses, data lakes, and slowly changing dimensions.
• Data types: comparing date, numeric, alphanumeric, currency, text, discrete vs. continuous, categorical/dimension, images, audio, and video.
• Data structures and file formats: comparing structured and unstructured data and file formats like text/flat files, JavaScript object notation (JSON), extensible markup language (XML), and hypertext markup language (HTML).

Data mining (25%)

• Data acquisition: explaining integration methods like delta load, extract/load/transform (ELT), and collection methods like web scraping, application programming interfaces (APIs), surveys, sampling, and observation.
• Data cleansing and profiling: identifying duplicate data, missing values, invalid data, outliers, specification mismatches, and data type validation.
• Data manipulation techniques: executing techniques like merging, blending, concatenation, appending, imputation, aggregation, transposing, normalizing, and parsing.
• Query optimization: explaining filtering, sorting, date functions, logical functions, aggregate functions, indexing, temporary tables, and execution plans.

Data analysis (23%)

• Descriptive statistics: applying measures of central tendency, dispersion, frequencies, percentages, percent change, and confidence intervals.
• Inferential statistics: explaining t-tests, z-scores, p-values, chi-squared tests, hypothesis testing, regression, and correlation.
• Analysis techniques: summarizing trend analysis, performance analysis, exploratory analysis, and link analysis.

Visualization (23%)

• Business requirements: translating requirements into reports using measures of central tendency, dispersion, and percentages.
• Report and dashboard design: using cover pages, design elements, and documentation.
• Dashboard development: applying considerations for development processes and delivery.
• Visualization types: applying line charts, pie charts, scatter plots, bar charts, histograms, heat maps, geographic maps, tree maps, stacked charts, and word clouds.
• Report types: comparing static vs. dynamic, ad-hoc, self-service, recurring, and tactical research reports.

Data governance, quality, and controls (14%)

• Data governance: summarizing access, security, storage, use, entity relationships, classification, jurisdiction, and breach reporting.
• Data quality control: applying validation methods, quality dimensions, rules, metrics, and automated checks.
• Master data management (MDM): explaining processes and circumstances for MDM.