Quick Answer
The answer is data types being incorrectly inferred, as this is the most common issue when importing a CSV file with mixed numeric and text fields. This occurs because CSV import tools often rely on heuristics to guess column types, and ambiguous data—like a column containing both numbers and text—can cause the system to misclassify the entire field, reading numbers as text strings or converting text to numeric values erroneously. On the CompTIA Data+ DA0-001 exam, this concept tests your understanding of data profiling and preprocessing, often appearing in scenario-based questions where you must identify why a calculation fails after import. A common trap is assuming missing headers or file size cause the problem, but the core issue is inference logic struggling with mixed types. Memory tip: think “mixed signals, wrong types”—when data types are inconsistent, the import tool sends the wrong signal.
This DA0-001 practice question tests your understanding of mining and acquiring data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is importing a CSV file that contains a mixture of numeric and text fields. What is the most common issue when importing?
Duplicate rows
Why wrong: Duplicates are a data quality issue, not an import issue.
Missing header row
Why wrong: Missing headers affect column naming, not type inference.
Data types being incorrectly inferred
CSV import tools often guess types incorrectly, leading to conversion errors.
File size limitation
Why wrong: File size is not specific to mixed data types.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Data types being incorrectly inferred
Data type inference often fails, causing numbers to be read as text or vice versa. File size limitations, missing headers, and duplicate rows are less common or not specific to mixed types.
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.
Duplicate rows
Why it's wrong here
Duplicates are a data quality issue, not an import issue.
Missing header row
Why it's wrong here
Missing headers affect column naming, not type inference.
Data types being incorrectly inferred
Why this is correct
CSV import tools often guess types incorrectly, leading to conversion errors.
Related concept
Read the scenario before looking for a memorised answer.
File size limitation
Why it's wrong here
File size is not specific to mixed data types.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which DA0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Data types being incorrectly inferred — Data type inference often fails, causing numbers to be read as text or vice versa. File size limitations, missing headers, and duplicate rows are less common or not specific to mixed types.
Identify which DA0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DA0-001 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 DA0-001 exam.
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