DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is building a dataset from multiple sources and needs to ensure data quality. During the data acquisition phase, which activity is most important to perform?
⚠ Common exam trap
CompTIA often tests the distinction between data profiling (discovery/assessment) and data cleaning (correction), leading candidates to mistakenly choose cleaning as the first step during acquisition when profiling must come first to identify what needs cleaning.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data profiling
Data profiling is the most important activity during the data acquisition phase because it involves examining source data to understand its structure, content, and quality issues before integration. This step identifies missing values, data types, duplicates, and inconsistencies early, preventing downstream errors in analysis. Without profiling, subsequent cleaning and modeling may be based on flawed assumptions about the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data visualization
Why it's wrong here
Visualisation displays data after it has been acquired and profiled, so it cannot detect or resolve quality defects during acquisition itself. It is tempting because charts do reveal anomalies, and it would be correct later in the workflow for exploratory analysis and communicating findings to stakeholders.
- ✗
Data cleaning
Why it's wrong here
Cleaning corrects defects after data has been ingested, whereas acquisition requires validating and profiling source records before loading them. It is tempting because cleaning is the visible quality activity, and it would be correct once data has landed and duplicates, nulls or format errors need remediation.
- ✓
Data profiling
Why this is correct
Profiling computes null counts, distinct values, ranges and formats across each source column, exposing anomalies before data enters the warehouse. This satisfies the data quality requirement by revealing inconsistencies at acquisition time, when they are cheapest to correct.
- ✗
Data modeling
Why it's wrong here
Data modelling defines structure, relationships and schemas for storing data, which does not validate the accuracy or completeness of incoming source records during acquisition. It is tempting because modelling precedes loading, and it would be correct when designing the target warehouse schema or entity relationships.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.