- A
Configure HDFS replication factor to 3 to ensure data durability during cluster restarts.
Why wrong: Incorrect. HDFS replication factor of 3 is unnecessary when using Cloud Storage as the primary storage because Cloud Storage provides data durability and availability by default. Moreover, HDFS is ephemeral if using ephemeral clusters, so replication does not persist after cluster deletion. This approach does not minimize operational overhead or cost.
- B
Rewrite the Spark jobs as Dataflow pipelines to take advantage of serverless processing.
Why wrong: Incorrect. While Dataflow is serverless, rewriting existing Spark jobs as Dataflow pipelines would require significant redevelopment effort and is not necessary to minimize overhead for occasional jobs. The question specifically asks for strategies for existing Spark jobs on Dataproc.
- C
Store all data in Cloud Storage instead of HDFS, and use the Cloud Storage connector to access it.
Correct. Storing data in Cloud Storage decouples storage from compute, enabling ephemeral clusters. The Cloud Storage connector provides Hadoop-compatible access, eliminating HDFS overhead and reducing cost because storage is billed separately and persists beyond cluster lifetime.
- D
Create an ephemeral Dataproc cluster for each job and delete it after completion.
Correct. Ephemeral clusters are created per job and deleted after completion. This minimizes cost because you only pay for compute during job execution, and operational overhead is low because no persistent cluster management is needed.
- E
Use a small persistent cluster that runs continuously and submit jobs to it.
Why wrong: Incorrect. A small persistent cluster incurs continuous costs even when idle, which is not cost-effective for jobs that run only a few times per day. It also requires ongoing management.
Dataproc Ephemeral Clusters for Cost Savings
This PDE practice question tests your understanding of building and operationalizing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: ephemeral clusters. 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 their on-premises Apache Spark jobs to Google Cloud Dataproc. They want to minimize operational overhead and cost for jobs that run only a few times per day. Which TWO strategies should they adopt? (Choose TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Quick Answer
The answer is to create an ephemeral Dataproc cluster for each job and delete it after completion, combined with storing data in Cloud Storage rather than HDFS. This approach directly achieves Dataproc ephemeral cluster cost savings by decoupling storage from compute, allowing clusters to spin up only when needed and terminate immediately after job completion. The Cloud Storage connector provides Hadoop-compatible file system access, eliminating the need for persistent HDFS replication and its associated costs. On the Google Professional Data Engineer exam, this scenario tests your understanding of cost optimization patterns for intermittent workloads, often appearing as a trap where candidates mistakenly choose persistent clusters or manual scaling. The key insight is that ephemeral clusters eliminate idle compute costs entirely, while Cloud Storage ensures data persistence without cluster dependency. Memory tip: think "spin up, process, delete" — like a rental car you return immediately after the trip, paying only for the miles driven.
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
Store all data in Cloud Storage instead of HDFS, and use the Cloud Storage connector to access it.
Option C is correct because storing data in Cloud Storage decouples storage from compute, allowing ephemeral clusters to be spun up and down without data loss. The Cloud Storage connector provides Hadoop-compatible file system access, eliminating the need for HDFS replication and reducing costs by avoiding persistent cluster storage. Option D is correct because ephemeral Dataproc clusters are created per job and deleted after completion, which minimizes cost and operational overhead for intermittent workloads, as there is no need to maintain a persistent cluster. Options A and B are incorrect: A proposes HDFS replication, which is unnecessary when using Cloud Storage, and B suggests rewriting jobs as Dataflow pipelines, which is not a required strategy for the stated goal of minimizing overhead for existing Spark jobs. Option E is incorrect because a persistent cluster incurs continuous costs and operational overhead, which is not optimal for jobs that run only a few times per day.
Key principle: Ephemeral clusters
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure HDFS replication factor to 3 to ensure data durability during cluster restarts.
Why it's wrong here
Incorrect. HDFS replication factor of 3 is unnecessary when using Cloud Storage as the primary storage because Cloud Storage provides data durability and availability by default. Moreover, HDFS is ephemeral if using ephemeral clusters, so replication does not persist after cluster deletion. This approach does not minimize operational overhead or cost.
- ✗
Rewrite the Spark jobs as Dataflow pipelines to take advantage of serverless processing.
Why it's wrong here
Incorrect. While Dataflow is serverless, rewriting existing Spark jobs as Dataflow pipelines would require significant redevelopment effort and is not necessary to minimize overhead for occasional jobs. The question specifically asks for strategies for existing Spark jobs on Dataproc.
- ✓
Store all data in Cloud Storage instead of HDFS, and use the Cloud Storage connector to access it.
Why this is correct
Correct. Storing data in Cloud Storage decouples storage from compute, enabling ephemeral clusters. The Cloud Storage connector provides Hadoop-compatible access, eliminating HDFS overhead and reducing cost because storage is billed separately and persists beyond cluster lifetime.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Ephemeral clusters
- ✓
Create an ephemeral Dataproc cluster for each job and delete it after completion.
Why this is correct
Correct. Ephemeral clusters are created per job and deleted after completion. This minimizes cost because you only pay for compute during job execution, and operational overhead is low because no persistent cluster management is needed.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Ephemeral clusters
- ✗
Use a small persistent cluster that runs continuously and submit jobs to it.
Why it's wrong here
Incorrect. A small persistent cluster incurs continuous costs even when idle, which is not cost-effective for jobs that run only a few times per day. It also requires ongoing management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake is to think that a persistent cluster is needed for data durability or to avoid job startup latency. However, for jobs that run only a few times per day, ephemeral clusters with Cloud Storage are more cost-effective and operationally simpler.
Detailed technical explanation
How to think about this question
The Cloud Storage connector implements the Hadoop FileSystem API, allowing Spark to read/write data directly to GCS buckets using the gs:// scheme. When using ephemeral clusters, data is persisted in Cloud Storage, so cluster deletion does not affect data durability. The connector uses a configurable replication factor (default 1) at the GCS level, which is managed by Google's infrastructure, not the cluster, reducing overhead.
KKey Concepts to Remember
- Ephemeral clusters
- Cloud Storage connector
- Decoupling storage and compute
- Cost optimization for intermittent workloads
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
Ephemeral clusters
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.
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Ephemeral clusters.
What is the correct answer to this question?
The correct answer is: Store all data in Cloud Storage instead of HDFS, and use the Cloud Storage connector to access it. — Option C is correct because storing data in Cloud Storage decouples storage from compute, allowing ephemeral clusters to be spun up and down without data loss. The Cloud Storage connector provides Hadoop-compatible file system access, eliminating the need for HDFS replication and reducing costs by avoiding persistent cluster storage. Option D is correct because ephemeral Dataproc clusters are created per job and deleted after completion, which minimizes cost and operational overhead for intermittent workloads, as there is no need to maintain a persistent cluster. Options A and B are incorrect: A proposes HDFS replication, which is unnecessary when using Cloud Storage, and B suggests rewriting jobs as Dataflow pipelines, which is not a required strategy for the stated goal of minimizing overhead for existing Spark jobs. Option E is incorrect because a persistent cluster incurs continuous costs and operational overhead, which is not optimal for jobs that run only a few times per day.
What should I do if I get this PDE question wrong?
Review ephemeral clusters, then practise related PDE questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Ephemeral clusters
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Last reviewed: Jun 30, 2026
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