PDE Ingesting and Processing the Data Practice Question
You are loading 10 GB of daily CSV files from a GCS bucket into a BigQuery table. The files contain some malformed rows that you want to skip. Which BigQuery load configuration should you use?
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
✓
Use the 'max_bad_records' option set to a value like 10.
BigQuery allows setting max_bad_records in load jobs; records exceeding this threshold cause the job to fail. Setting max_bad_records to a value greater than 0 allows the load to succeed while skipping malformed rows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'skip_leading_rows' option.
Why it's wrong here
skip_leading_rows skips header rows, not malformed rows.
- ✗
Use the 'ignore_unknown_values' option.
Why it's wrong here
ignore_unknown_values ignores extra columns not in the schema, but does not handle malformed rows.
- ✓
Use the 'max_bad_records' option set to a value like 10.
Why this is correct
max_bad_records specifies the number of allowed bad records; if the number of bad records exceeds this, the load fails.
- ✗
Use the 'allow_jagged_rows' option.
Why it's wrong here
allow_jagged_rows is for CSV files with missing trailing columns; it does not handle malformed rows.
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