The answer is Cloud Dataproc and Dataproc on GKE. These two services are correct because Dataproc is a fully managed service built specifically for Hadoop and Spark workloads, allowing you to run transient clusters that automatically scale down when idle—directly addressing the need to minimize code changes and reduce costs. Dataproc on GKE extends this by offering the flexibility to run Spark workloads on Kubernetes, which is ideal for organizations already using container orchestration. On the Google Professional Data Engineer exam, this question tests your understanding of migration paths that preserve existing Hadoop code while leveraging cloud-native cost controls; a common trap is choosing Dataflow or BigQuery, which are not Hadoop-compatible and would require significant code rewrites. Remember the memory tip: “Hadoop stays Hadoop” with Dataproc—if the workload is MapReduce, Hive, or Pig, stick with Dataproc and its GKE variant for containerized flexibility.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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.
An organization is moving on-premises Hadoop workloads to Google Cloud. They need to minimize code changes and manage transient clusters for cost savings. Which two Google Cloud services should they consider? (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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Dataproc on GKE
Options B and D are correct: Dataproc is a managed Hadoop/Spark service that can run transient clusters, and Dataproc on GKE allows running Spark workloads on GKE for flexibility. Option A is wrong because Dataflow is not compatible with Hadoop. Option C is wrong because Compute Engine requires manual cluster setup. Option E is wrong because BigQuery is not Hadoop-compatible.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Compute Engine with self-managed Hadoop
Why it's wrong here
Requires manual setup and does not minimize changes compared to Dataproc.
✗
BigQuery
Why it's wrong here
BigQuery is not Hadoop-compatible; requires code rewrite.
✓
Dataproc on GKE
Why this is correct
Allows running Spark workloads on GKE, leveraging container orchestration.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✓
Cloud Dataproc
Why this is correct
Dataproc is a managed Hadoop/Spark service supporting transient clusters.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Cloud Dataflow
Why it's wrong here
Dataflow is not Hadoop-compatible; would require code rewrite.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PDE NAT questions on configuration and troubleshooting.
Designing data processing systems — This question tests Designing data processing systems — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Dataproc on GKE — Options B and D are correct: Dataproc is a managed Hadoop/Spark service that can run transient clusters, and Dataproc on GKE allows running Spark workloads on GKE for flexibility. Option A is wrong because Dataflow is not compatible with Hadoop. Option C is wrong because Compute Engine requires manual cluster setup. Option E is wrong because BigQuery is not Hadoop-compatible.
What should I do if I get this PDE question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PDE NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
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