- A
Use Dataproc to run a Spark job that cleans the data and writes to BigQuery
Why wrong: Unnecessary complexity for a single file.
- B
Use the Storage Write API to stream each row, skipping bad ones in code
Why wrong: Streaming is for real-time; batch load is better for a 10 GB file.
- C
Use a Dataflow pipeline to read CSV, filter bad rows, and write to BigQuery
Why wrong: Overkill for a simple load; inefficient for a single file.
- D
Use the bq command-line tool with the --max_bad_records flag
bq load with --max_bad_records skips malformed rows efficiently.
Question 470 of 1,000
PDE Ingesting and Processing the Data Practice Question
This PDE practice question tests your understanding of ingesting and processing the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 engineer needs to load a 10 GB CSV file from GCS into BigQuery. The file contains some malformed rows that should be skipped. Which approach is most efficient?
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 bq command-line tool with the --max_bad_records flag
Option D is correct because the `bq` command-line tool's `--max_bad_records` flag allows BigQuery's native CSV loader to skip malformed rows up to a specified limit during a load job. This is the most efficient approach for a one-time batch load of a 10 GB file, as it avoids the overhead of spinning up separate processing clusters (Dataproc, Dataflow) or streaming each row individually, leveraging BigQuery's optimized ingestion pipeline.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Dataproc to run a Spark job that cleans the data and writes to BigQuery
Why it's wrong here
Unnecessary complexity for a single file.
- ✗
Use the Storage Write API to stream each row, skipping bad ones in code
Why it's wrong here
Streaming is for real-time; batch load is better for a 10 GB file.
- ✗
Use a Dataflow pipeline to read CSV, filter bad rows, and write to BigQuery
Why it's wrong here
Overkill for a simple load; inefficient for a single file.
- ✓
Use the bq command-line tool with the --max_bad_records flag
Why this is correct
bq load with --max_bad_records skips malformed rows efficiently.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that complex ETL pipelines (Spark, Dataflow) are always required for data cleaning, when in fact BigQuery's native load options like `--max_bad_records` can handle common malformed row scenarios directly and more efficiently.
Detailed technical explanation
How to think about this question
The `--max_bad_records` flag works by allowing BigQuery to accept a load job even if some rows fail parsing, up to the specified threshold; the malformed rows are logged but not loaded. Under the hood, BigQuery's load job uses a parallel, distributed reader that can handle CSV parsing errors gracefully, making it ideal for large files with occasional bad rows. In a real-world scenario, if the file had many malformed rows (e.g., >5000), you would need to increase the limit or pre-clean the data, but for typical cases this is the most efficient path.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Ingesting and Processing the Data — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the bq command-line tool with the --max_bad_records flag — Option D is correct because the `bq` command-line tool's `--max_bad_records` flag allows BigQuery's native CSV loader to skip malformed rows up to a specified limit during a load job. This is the most efficient approach for a one-time batch load of a 10 GB file, as it avoids the overhead of spinning up separate processing clusters (Dataproc, Dataflow) or streaming each row individually, leveraging BigQuery's optimized ingestion pipeline.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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