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PDE Practice Question: Is moving on-premises Hadoop workloads to Google…
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.)
⚠ Common exam trap
Many candidates 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.
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.
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.
- ✓
Cloud Dataproc
Why this is correct
Dataproc is a managed Hadoop/Spark service supporting transient clusters.
- ✗
Cloud Dataflow
Why it's wrong here
Dataflow is not Hadoop-compatible; would require code rewrite.
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