Quick Answer
The answer is to use Cloud Dataproc, leverage the Dataproc Jobs API for submission, and replace HDFS with Cloud Storage. This trio is correct because Dataproc is a fully managed service that runs the same open-source Spark and Hadoop components as an on-premises cluster, so existing job logic requires minimal modification. By swapping HDFS for Cloud Storage, you eliminate the need to rewrite data access patterns, while the Dataproc Jobs API allows you to submit jobs programmatically, preserving your existing automation and workflow scripts. On the Google Professional Data Engineer exam, this scenario tests your understanding of lift-and-shift migration patterns versus refactoring; a common trap is assuming you must rewrite Spark code or use a different compute service like Dataflow. Remember the mnemonic “DCS” for Dataproc, Cloud Storage, and Submission API—these three keep your migration clean and your modifications minimal.
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 company is migrating an on-premises Hadoop cluster to Google Cloud. They need to run existing Spark jobs with minimal modification. Which THREE strategies should they consider? (Choose THREE.)
Migrate to BigQuery for all analytics.
Why wrong: Requires code changes.
Use Cloud Dataproc with Spark and Hive components.
Compatible with existing code.
Store data in Cloud Storage instead of HDFS.
No migration needed.
Rewrite Spark jobs as Dataflow pipelines.
Why wrong: Major code changes.
Use Dataproc Jobs API to submit jobs.
Minimal modification.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Cloud Dataproc with Spark and Hive components.
Option B is correct because Cloud Dataproc is a managed Spark and Hadoop service that supports the same Spark and Hive components used on-premises, allowing existing Spark jobs to run with minimal modification. It provides native integration with Cloud Storage, which can replace HDFS without changing job logic, and the Dataproc Jobs API enables programmatic job submission, preserving existing workflows.
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.
Migrate to BigQuery for all analytics.
Why it's wrong here
Requires code changes.
Use Cloud Dataproc with Spark and Hive components.
Why this is correct
Compatible with existing code.
Related concept
Read the scenario before looking for a memorised answer.
Store data in Cloud Storage instead of HDFS.
Why this is correct
No migration needed.
Related concept
Read the scenario before looking for a memorised answer.
Rewrite Spark jobs as Dataflow pipelines.
Why it's wrong here
Major code changes.
Use Dataproc Jobs API to submit jobs.
Why this is correct
Minimal modification.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may assume BigQuery or Dataflow are the only Google Cloud data processing options, overlooking that Dataproc is specifically designed for minimal-change migrations of existing Spark/Hadoop workloads.
Detailed technical explanation
Cloud Dataproc uses the same Apache Spark and Hive binaries as on-premises clusters, so jobs that rely on Hive Metastore or Spark SQL can be migrated directly. Cloud Storage acts as a drop-in replacement for HDFS via the gs:// connector, which implements the Hadoop FileSystem API, allowing jobs to read/write data without code changes. The Dataproc Jobs API submits jobs as REST requests, supporting the same job parameters as the spark-submit command.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Use Cloud Dataproc with Spark and Hive components. — Option B is correct because Cloud Dataproc is a managed Spark and Hadoop service that supports the same Spark and Hive components used on-premises, allowing existing Spark jobs to run with minimal modification. It provides native integration with Cloud Storage, which can replace HDFS without changing job logic, and the Dataproc Jobs API enables programmatic job submission, preserving existing workflows.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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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