- A
Write output to BigQuery using the BigQuery connector
The connector provides efficient bulk loading.
- B
Use DataFrames or Datasets instead of RDDs where possible
DataFrames benefit from Catalyst optimizer, improving performance.
- C
Use PySpark instead of Scala for simplicity
Why wrong: Performance difference is not a key optimization; also, the question is about performance and cost.
- D
Increase the number of partitions to match the number of cores exactly
Why wrong: Partition tuning is important, but matching exactly may not be optimal; typically 2-3x cores is recommended.
- E
Use preemptible VM instances for worker nodes to reduce cost
Preemptible VMs are cheaper and suitable for batch jobs.
Question 487 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 company is migrating a Spark batch job from on-premises to Dataproc. The job uses RDDs for custom transformations and writes output to BigQuery. They want to optimize the job for performance and cost on Dataproc. Which THREE practices should they adopt? (Choose 3)
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
✓
Write output to BigQuery using the BigQuery connector
The BigQuery connector for Spark (com.google.cloud.spark.bigquery) allows direct, efficient writes to BigQuery without intermediate storage. It leverages the BigQuery Storage Write API for high-throughput, exactly-once delivery, which is far more performant than writing via JDBC or saving to GCS and then loading into BigQuery.
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.
- ✓
Write output to BigQuery using the BigQuery connector
Why this is correct
The connector provides efficient bulk loading.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use DataFrames or Datasets instead of RDDs where possible
Why this is correct
DataFrames benefit from Catalyst optimizer, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use PySpark instead of Scala for simplicity
Why it's wrong here
Performance difference is not a key optimization; also, the question is about performance and cost.
- ✗
Increase the number of partitions to match the number of cores exactly
Why it's wrong here
Partition tuning is important, but matching exactly may not be optimal; typically 2-3x cores is recommended.
- ✓
Use preemptible VM instances for worker nodes to reduce cost
Why this is correct
Preemptible VMs are cheaper and suitable for batch jobs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google PDE exam often tests the misconception that PySpark is always simpler and faster for batch jobs, but the exam expects you to know that Scala/Java DataFrames leverage the Catalyst optimizer and Tungsten execution for superior performance on Dataproc.
Detailed technical explanation
How to think about this question
The BigQuery connector uses the Spark DataSource API to write data in Avro format directly to BigQuery's storage, bypassing the need for staging files. Under the hood, it commits writes using the Storage Write API's stream-level offset tracking, ensuring exactly-once semantics even on retries. In real-world scenarios, using the connector can reduce write times by 50-70% compared to the legacy approach of exporting to GCS and then loading.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Write output to BigQuery using the BigQuery connector — The BigQuery connector for Spark (com.google.cloud.spark.bigquery) allows direct, efficient writes to BigQuery without intermediate storage. It leverages the BigQuery Storage Write API for high-throughput, exactly-once delivery, which is far more performant than writing via JDBC or saving to GCS and then loading into BigQuery.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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