DA0-002 Data Acquisition and Preparation Practice Question
A data engineer is loading a large CSV file into a relational staging table. Several columns contain numeric values with thousands separators, such as '1,234.56', and a few rows contain the text 'N/A' in those columns. The target columns are defined as DECIMAL. Which approach best prepares the data for a successful load while preserving the ability to audit rejected values?
⚠ Common exam trap
The trap here is treating 'N/A' as equivalent to zero or assuming locale settings can safely reinterpret thousands separators without corrupting numeric magnitude.
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
✓
Strip thousands separators, convert 'N/A' to NULL, validate the remaining values as numeric, and load rejected rows into an error table.
Preparing numeric text with thousands separators and non-numeric tokens requires cleaning the format, mapping non-values to NULL, validating conversion, and isolating failures. Stripping separators and converting 'N/A' to NULL allows valid rows to load as DECIMAL, while an error table preserves rejected rows for audit and reprocessing. String storage, zero substitution, and locale changes either defer errors, fabricate data, or corrupt magnitudes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Strip thousands separators, convert 'N/A' to NULL, validate the remaining values as numeric, and load rejected rows into an error table.
Why this is correct
Removing thousands separators produces parseable numeric strings, mapping 'N/A' to NULL respects the DECIMAL nullability, and validating before load prevents type errors. Routing rejected rows to an error table preserves auditability. This combination satisfies the goal of a clean load while retaining visibility into records that could not be converted, which is essential for data quality monitoring and reprocessing.
- ✗
Load the values as strings into VARCHAR columns, then cast them during every downstream query.
Why it's wrong here
Storing numeric measures as strings defers type conversion to query time, where implicit casts may fail or produce incorrect ordering and aggregation. It also prevents the database from enforcing numeric precision and scale. While it avoids load-time errors, it violates the target schema and creates ongoing performance and correctness problems, so it is not the best preparation approach for a staging load.
- ✗
Replace 'N/A' with 0 and load all values directly into the DECIMAL columns.
Why it's wrong here
Substituting zero for 'N/A' fabricates data and skews averages, sums, and other aggregates. A missing value is not the same as a measured zero, especially for financial or operational metrics. The thousands separators would also still cause conversion errors unless removed. This approach sacrifices data integrity and does not address the formatting issue, making it unsuitable for an auditable staging load.
- ✗
Set the database session to a locale that interprets commas as decimal points and load the file unchanged.
Why it's wrong here
Changing the session locale would reinterpret '1,234.56' as 1.23456 in locales where comma is the decimal separator, corrupting the magnitude of the values by a factor of 1,000. It also does not resolve the 'N/A' text entries. Locale settings are a risky global change that affects other queries, so this is not a safe or accurate preparation method.
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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
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