- A
Use Preemptible VMs for worker nodes to reduce cost
Preemptible VMs are cost-effective for fault-tolerant jobs.
- B
Use Cloud Storage instead of HDFS for data storage
Cloud Storage is scalable, durable, and cost-effective, and integrates with Dataproc.
- C
Avoid using Cloud Storage connector to prevent overhead
Why wrong: Cloud Storage connector is needed for Dataproc to access Cloud Storage.
- D
Keep HDFS for better performance
Why wrong: HDFS on Dataproc is ephemeral and not recommended; Cloud Storage is preferred.
- E
Use on-demand VMs for master node to ensure availability
Why wrong: Master node should be on-demand for reliability, but this is not a key migration consideration.
Question 280 of 1,000
mediumMultiple SelectObjective-mapped
Important Considerations When Migrating Hadoop to Dataproc on Google Cloud
This PDE practice question tests your understanding of pde exam topics. 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 is migrating on-premises Hadoop jobs to Dataproc. Which TWO considerations are important?
Quick Answer
The answer is to use Cloud Storage instead of HDFS for data storage and to use Preemptible VMs for transient tasks. Cloud Storage provides virtually unlimited scalability and eliminates the need to manage HDFS replication, while Preemptible VMs drastically reduce costs for fault-tolerant batch jobs that can handle interruptions. On the Google Professional Data Engineer exam, this Dataproc migration from Hadoop considerations question tests your understanding of cloud-native architecture versus lift-and-shift approaches; a common trap is assuming HDFS must be preserved, but Dataproc is designed to work with Cloud Storage as the primary storage layer. Remember that master nodes should always be on-demand for reliability, and the Cloud Storage connector is mandatory for reading data. A useful memory tip is "Storage in the cloud, compute on the cheap"—store data persistently in Cloud Storage and use Preemptible VMs for worker nodes to optimize cost and performance.
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 Preemptible VMs for worker nodes to reduce cost
Option A is correct because Preemptible VMs are significantly cheaper than standard VMs (up to 80% discount) and are ideal for worker nodes in Dataproc clusters, as they can handle transient failures gracefully through checkpointing and job retries. This cost optimization is a key consideration when migrating Hadoop jobs to the cloud, where compute costs can dominate. Option B is also correct because Cloud Storage provides a highly durable, scalable, and cost-effective storage solution that replaces HDFS, eliminating the need to manage HDFS replication and cluster storage, and integrates seamlessly with Dataproc through the Cloud Storage connector, which is essential for performance, not an overhead to avoid.
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 Preemptible VMs for worker nodes to reduce cost
Why this is correct
Preemptible VMs are cost-effective for fault-tolerant jobs.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Cloud Storage instead of HDFS for data storage
Why this is correct
Cloud Storage is scalable, durable, and cost-effective, and integrates with Dataproc.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Avoid using Cloud Storage connector to prevent overhead
Why it's wrong here
Cloud Storage connector is needed for Dataproc to access Cloud Storage.
- ✗
Keep HDFS for better performance
Why it's wrong here
HDFS on Dataproc is ephemeral and not recommended; Cloud Storage is preferred.
- ✗
Use on-demand VMs for master node to ensure availability
Why it's wrong here
Master node should be on-demand for reliability, but this is not a key migration consideration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that HDFS must be retained for performance in the cloud, but the correct approach is to use Cloud Storage for data storage and Preemptible VMs for cost savings, while the Cloud Storage connector is a required component, not an overhead to avoid.
Detailed technical explanation
How to think about this question
The Cloud Storage connector implements the Hadoop FileSystem API, allowing Dataproc to treat Cloud Storage as a drop-in replacement for HDFS. It uses a distributed hash table for metadata and supports read-ahead caching and write buffering to minimize latency. In real-world scenarios, jobs that shuffle data heavily may still benefit from local SSDs for intermediate data, but final outputs are written to Cloud Storage for persistence and sharing across clusters.
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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FAQ
Questions learners often ask
What does this PDE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use Preemptible VMs for worker nodes to reduce cost — Option A is correct because Preemptible VMs are significantly cheaper than standard VMs (up to 80% discount) and are ideal for worker nodes in Dataproc clusters, as they can handle transient failures gracefully through checkpointing and job retries. This cost optimization is a key consideration when migrating Hadoop jobs to the cloud, where compute costs can dominate. Option B is also correct because Cloud Storage provides a highly durable, scalable, and cost-effective storage solution that replaces HDFS, eliminating the need to manage HDFS replication and cluster storage, and integrates seamlessly with Dataproc through the Cloud Storage connector, which is essential for performance, not an overhead to avoid.
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
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