PDE Storing the Data Practice Question
A data engineer is loading a 2 TB CSV dataset from Cloud Storage into a partitioned BigQuery table. The load job fails with an error indicating too many errors during parsing. The source files have inconsistent column counts and some rows contain embedded newlines. The engineer wants to load the data reliably with minimal preprocessing. What should the engineer do?
⚠ Common exam trap
The trap here is treating the load error as a throughput or error-tolerance problem, when the real issue is that CSV parsing cannot represent embedded newlines without careful quoting.
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
✓
Convert the CSV files to newline-delimited JSON or Parquet, then load the converted files into BigQuery.
CSV parsing is delimiter-based and breaks when rows contain embedded newlines or inconsistent column counts. Converting to newline-delimited JSON or Parquet removes delimiter ambiguity and lets BigQuery load the data reliably, addressing the root cause rather than suppressing errors or increasing throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Load the CSV files into a single external table and query it with the ignoreUnknownValues option.
Why it's wrong here
External tables over CSV still use the same parser, so embedded newlines and inconsistent column counts produce the same errors. ignoreUnknownValues applies to extra fields in JSON, not CSV parsing. Querying an external table also does not fix the data, and performance is lower than a native load for a 2 TB dataset.
- ✗
Set the maxBadRecords option to a high value and rerun the load job with the same CSV files.
Why it's wrong here
Raising maxBadRecords allows the job to skip malformed rows, but it silently drops data and does not fix rows with embedded newlines, which can shift column boundaries across records. The resulting table would be incomplete and potentially misaligned. This approach masks the parsing problem rather than resolving it.
- ✓
Convert the CSV files to newline-delimited JSON or Parquet, then load the converted files into BigQuery.
Why this is correct
Newline-delimited JSON and Parquet handle embedded newlines and variable schemas without relying on delimiter-based parsing. Converting the source removes the ambiguity that caused the load failure, and BigQuery loads these formats natively with schema autodetection or an explicit schema, yielding reliable ingestion with minimal manual row fixing.
- ✗
Increase the load job's maximum parallelism by splitting the CSV files into smaller objects in Cloud Storage.
Why it's wrong here
Splitting files improves throughput but does not change how each file is parsed, so rows with embedded newlines and inconsistent columns still fail. Parallelism affects speed, not correctness. The load would still hit the error threshold, and the underlying parsing issue would remain unresolved.
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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 Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud 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 PDE exam.