Quick Answer
Cloud Dataproc is the correct choice because it is a managed Spark and Hadoop service that natively supports Hive workloads, enabling you to migrate Hive to Google Cloud Dataproc with minimal code changes while leveraging transient clusters that automatically scale to match demand. This works because Dataproc runs the same open-source Hive Metastore and execution engine as on-premises deployments, so existing HiveQL scripts and table definitions transfer almost verbatim, and its transient cluster feature—where clusters are created only for the duration of a job and then deleted—directly satisfies the requirement for cost-efficient, ephemeral compute. On the Google Professional Data Engineer exam, this question tests your understanding of lift-and-shift migration patterns versus re-architecting; a common trap is choosing Cloud Bigtable or BigQuery, which require schema redesign and do not support HiveQL natively. Remember the mnemonic: “Hive on HDFS? Dataproc’s your DFS.”
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.
A team needs to migrate an existing on-premises Hadoop Hive workload to Google Cloud. They want to minimize code changes and use a managed service for transient clusters. Which service should they choose?
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.
Cloud Dataflow
Why wrong: Dataflow is for Beam pipelines, not Hive workloads.
Cloud Dataprep
Why wrong: Dataprep is for visual data transformation, not Hive execution.
Cloud Dataproc
Dataproc is fully compatible with Hadoop/Hive and offers ephemeral clusters with minimal code changes.
BigQuery
Why wrong: BigQuery is a serverless data warehouse, not a Hadoop environment.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Dataproc
Cloud Dataproc is the correct choice because it is a managed Spark and Hadoop service that supports Hive workloads natively, allowing you to run existing Hive scripts with minimal changes. It also supports transient clusters, which can be automatically scaled up and down, aligning with the requirement for transient clusters.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Dataflow
Why it's wrong here
Dataflow is for Beam pipelines, not Hive workloads.
Cloud Dataprep
Why it's wrong here
Dataprep is for visual data transformation, not Hive execution.
Cloud Dataproc
Why this is correct
Dataproc is fully compatible with Hadoop/Hive and offers ephemeral clusters with minimal code changes.
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.
BigQuery
Why it's wrong here
BigQuery is a serverless data warehouse, not a Hadoop environment.
Common exam traps
The trap here is that candidates often confuse Cloud Dataflow's ability to process batch data with Hadoop compatibility, but Dataflow does not support Hive or transient Hadoop clusters, making Dataproc the only correct option for minimizing code changes.
Detailed technical explanation
Cloud Dataproc uses the Hadoop Distributed File System (HDFS) and YARN for resource management, allowing Hive to run as a SQL-on-Hadoop engine. Transient clusters in Dataproc can be created with preemptible instances for cost savings and automatically deleted after job completion, which is ideal for intermittent workloads. Under the hood, Dataproc integrates with Google Cloud Storage via the gs:// connector, enabling data persistence beyond cluster lifetime without code changes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Dataproc — Cloud Dataproc is the correct choice because it is a managed Spark and Hadoop service that supports Hive workloads natively, allowing you to run existing Hive scripts with minimal changes. It also supports transient clusters, which can be automatically scaled up and down, aligning with the requirement for transient clusters.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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.
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