- A
Compute Engine with self-managed Hadoop
Why wrong: Requires manual setup and does not minimize changes compared to Dataproc.
- B
BigQuery
Why wrong: BigQuery is not Hadoop-compatible; requires code rewrite.
- C
Dataproc on GKE
Allows running Spark workloads on GKE, leveraging container orchestration.
- D
Cloud Dataproc
Dataproc is a managed Hadoop/Spark service supporting transient clusters.
- E
Cloud Dataflow
Why wrong: Dataflow is not Hadoop-compatible; would require code rewrite.
Question 230 of 1,000
mediumMultiple SelectObjective-mapped
Migrating On-Premises Hadoop Workloads to Google Cloud with Dataproc
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.
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.
Quick Answer
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.
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
✓
Dataproc on GKE
Cloud Dataproc (option D) is a managed service for running Spark and Hadoop clusters. It supports transient clusters that can be created on-demand and deleted when idle, minimizing costs. It also allows direct migration of on-premises Hadoop code with minimal changes because it supports standard Hadoop/Spark APIs. Dataproc on GKE (option C) provides similar benefits but runs containerized workloads on GKE, offering additional ephemeral cluster capabilities and integration with Kubernetes. Both options minimize code changes and enable transient clusters for cost savings, while BigQuery (option B) requires rewriting SQL queries and Cloud Dataflow (option E) requires converting to Beam pipelines. Compute Engine with self-managed Hadoop (option A) does not provide transient cluster management by default.
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.
- ✗
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
Read the scenario before looking for a memorised answer.
- ✓
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
Read the scenario before looking for a memorised answer.
- ✗
Cloud Dataflow
Why it's wrong here
Dataflow is not Hadoop-compatible; would require code rewrite.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Cloud Dataflow (a Google Cloud service that runs Beam pipelines) with Dataproc, not realizing that Dataflow requires rewriting Hadoop jobs into Beam pipelines, while Dataproc on GKE and Cloud Dataproc directly support unmodified Hadoop/Spark code.
Detailed technical explanation
How to think about this question
Dataproc on GKE uses the Dataproc operator to provision Spark and Hadoop clusters as Kubernetes pods, with ephemeral storage backed by PersistentVolumes or Cloud Storage. This enables automatic scaling of worker nodes based on resource utilization via the Kubernetes Horizontal Pod Autoscaler, and clusters can be deleted when idle to avoid compute costs. A real-world scenario is running nightly ETL jobs where the cluster spins up, processes data, and shuts down, leveraging preemptible VMs for further savings.
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.
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing Data Processing Systems practice questions
Practise PDE questions linked to Designing Data Processing Systems.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Dataproc on GKE — Cloud Dataproc (option D) is a managed service for running Spark and Hadoop clusters. It supports transient clusters that can be created on-demand and deleted when idle, minimizing costs. It also allows direct migration of on-premises Hadoop code with minimal changes because it supports standard Hadoop/Spark APIs. Dataproc on GKE (option C) provides similar benefits but runs containerized workloads on GKE, offering additional ephemeral cluster capabilities and integration with Kubernetes. Both options minimize code changes and enable transient clusters for cost savings, while BigQuery (option B) requires rewriting SQL queries and Cloud Dataflow (option E) requires converting to Beam pipelines. Compute Engine with self-managed Hadoop (option A) does not provide transient cluster management by default.
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.
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?
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 →
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.