DA0-002 Data Acquisition and Preparation Practice Question
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?
⚠ Common exam trap
DA0-002 often tests the misconception that CSV import issues are about file size or duplicates, when the real culprit is automatic data type inference on mixed columns.
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 types being incorrectly inferred
When importing CSV files, the most common issue is that the import tool (e.g., Excel, pandas, SQL Server Import Wizard) automatically infers data types based on the first rows it reads. Mixed numeric and text fields often cause the tool to guess wrong — for example, treating a numeric column with a stray text value as text, or converting leading-zero codes (like ZIP codes) to integers and losing the zeros. This type inference mismatch is the classic CSV import pitfall.
Answer analysis
Option-by-option breakdown
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
Mixed numeric and text fields typically cause type-inference errors, where numbers are read as text or leading zeros and codes are coerced to numbers. Duplicate rows are tempting because they are a familiar data-quality problem, but they stem from source data, not from mixed field types.
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Missing header row
Why it's wrong here
A missing header row causes the first data record to be consumed as column names, misaligning or losing that row. It is tempting because header handling is a genuine import setting, but mixed numeric and text fields instead trigger type-inference and delimiter issues.
- ✓
Data types being incorrectly inferred
Why this is correct
CSV files carry no type metadata, so the import engine must guess each column's type from sampled values. Mixed numeric and text fields cause misinference — for example, leading-zero codes becoming integers or numeric-looking text converting to numbers — which is the most common CSV import problem.
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File size limitation
Why it's wrong here
File size is rarely the blocker, since CSV imports stream or chunk readily; the recurring failure is type inference, where numeric-looking identifiers lose leading zeros or dates are misparsed as text. Size limits matter when loading enormous datasets into memory-bound tools, not for typical CSV ingestion.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.