Quick Answer
The correct first step is to standardize text fields using a lookup table. This approach is essential because inconsistent spellings, such as 'Jon' versus 'John', represent variations of the same entity, and attempting to merge or deduplicate records without first normalizing these values would risk creating duplicate entries or losing critical data. Standardizing via a lookup table maps all variants to a single canonical form, ensuring referential integrity before any matching logic is applied. On the CompTIA Data+ DA0-001 exam, this concept tests your understanding of the data preparation workflow, where cleaning must precede transformation or integration. A common trap is jumping straight to fuzzy matching or deduplication algorithms, which can introduce false positives if raw text inconsistencies remain. Remember the mnemonic “Clean before you combine” to recall that standardization always comes before merging.
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.
A data analyst discovers that a dataset contains multiple records for the same customer with different spellings (e.g., 'Jon' vs 'John'). Which data preparation step should be applied first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Merge all records into one per customer.
Why wrong: Merging without standardizing may propagate inconsistencies.
Remove duplicates based on exact match.
Why wrong: Exact match won't catch spelling variations.
Standardize text fields using a lookup table.
Standardization harmonizes variations like 'Jon' and 'John'.
Flag records for manual review.
Why wrong: Manual review is not scalable and should follow automation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Standardize text fields using a lookup table.
Option C is correct because the first step when dealing with inconsistent text values (like 'Jon' vs 'John') is to standardize the data using a lookup table or reference mapping. This ensures that all variations are normalized to a canonical form before any merging or deduplication is attempted, preventing data loss and preserving referential integrity.
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.
Merge all records into one per customer.
Why it's wrong here
Merging without standardizing may propagate inconsistencies.
Remove duplicates based on exact match.
Why it's wrong here
Exact match won't catch spelling variations.
Standardize text fields using a lookup table.
Why this is correct
Standardization harmonizes variations like 'Jon' and 'John'.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Flag records for manual review.
Why it's wrong here
Manual review is not scalable and should follow automation.
Common exam traps
The trap here is that candidates often jump to 'remove duplicates' (Option B) because they think of exact-match deduplication, but the question specifically tests the understanding that data quality issues like inconsistent spellings must be resolved through standardization before any deduplication logic can be applied.
Detailed technical explanation
Standardization typically involves creating a mapping table (e.g., a CSV or database lookup) that associates each variant with a canonical value, then applying a SQL UPDATE or ETL transform to replace the variants. In real-world scenarios, this step is critical before any join or aggregation operation, as even a single character difference (e.g., 'Jon' vs 'John') can cause mismatches in foreign key relationships or skew aggregate statistics.
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: Standardize text fields using a lookup table. — Option C is correct because the first step when dealing with inconsistent text values (like 'Jon' vs 'John') is to standardize the data using a lookup table or reference mapping. This ensures that all variations are normalized to a canonical form before any merging or deduplication is attempted, preventing data loss and preserving referential integrity.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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