Quick Answer
The answer is identifying outliers, checking for completeness, and validating data types. These three practices are considered best practices for data profiling during acquisition because they directly address the most common data quality issues that arise when ingesting new datasets. Identifying outliers helps detect anomalies that could skew analysis or indicate data entry errors, while checking for completeness ensures that no critical fields or records are missing, preventing downstream failures in transformations or reporting. Validating data types confirms that each field contains the expected format, such as dates or integers, which is essential for accurate joins and calculations. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of the acquisition phase of the data lifecycle, where profiling is a proactive quality gate. A common trap is confusing data profiling during acquisition with later cleaning steps like deduplication, which occurs after ingestion. Remember the mnemonic “COV” for Completeness, Outliers, and Validation to lock in the three correct choices.
This DA0-001 practice question tests your understanding of mining and acquiring data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are best practices for data profiling during acquisition? (Choose three.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Immediately normalize data
Why wrong: Normalization should occur after profiling, not during.
Check for completeness
Ensuring all required fields are populated is essential.
Assess data types
Verifying data types helps prevent integration errors.
Identify outliers
Outliers can indicate data quality issues.
Skip validation for trusted sources
Why wrong: Even trusted sources can have issues; validation is always needed.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Check for completeness
Checking for completeness (Option B) is a best practice during data acquisition because it ensures that all required fields and records are present before further processing. Incomplete data can lead to incorrect analysis or failed transformations, so profiling for missing values or nulls is a fundamental validation step.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Immediately normalize data
Why it's wrong here
Normalization should occur after profiling, not during.
Check for completeness
Why this is correct
Ensuring all required fields are populated is essential.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Assess data types
Why this is correct
Verifying data types helps prevent integration errors.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Identify outliers
Why this is correct
Outliers can indicate data quality issues.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Skip validation for trusted sources
Why it's wrong here
Even trusted sources can have issues; validation is always needed.
Common exam traps
The trap here is that candidates confuse 'best practices for acquisition' with 'best practices for transformation,' leading them to select normalization (Option A) as an immediate step rather than a later processing stage.
Detailed technical explanation
Data profiling during acquisition typically involves scanning source metadata and sample records to assess structure, content, and quality. For example, checking data types (Option C) verifies that integer fields contain only numeric values, while identifying outliers (Option D) can reveal data entry errors or boundary conditions that affect downstream ETL logic. These checks align with the DAMA-DMBOK framework for data quality assessment.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Mining and Acquiring Data — This question tests Mining and Acquiring Data — Read the scenario before looking for a memorised answer..
The correct answer is: Check for completeness — Checking for completeness (Option B) is a best practice during data acquisition because it ensures that all required fields and records are present before further processing. Incomplete data can lead to incorrect analysis or failed transformations, so profiling for missing values or nulls is a fundamental validation step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Variation 1. 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?
mediumWhy C: 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.
Last reviewed: Jun 24, 2026
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