- A
Keep HDFS on persistent Cloud Dataproc clusters and use BigQuery for SQL queries.
Why wrong: Persistent clusters with HDFS are costly; BigQuery cannot directly access HDFS data.
- B
Use Cloud Dataflow for all batch processing and BigQuery for storage and querying.
Why wrong: This would require rewriting all ETL code (Spark to Dataflow SDK) and migrating Hive queries to BigQuery SQL, which is a major change.
- C
Migrate HDFS to Cloud Storage, create a Cloud Dataproc cluster for Spark jobs, and use BigQuery for interactive SQL queries via a Hive metastore linked to BigQuery.
Why wrong: Hive metastore linked to BigQuery is not straightforward; existing Spark jobs would need changes.
- D
Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries.
Cloud Dataproc can use Cloud Storage as the data layer; most Spark and Hive jobs need minimal changes (e.g., file path prefix). Ephemeral clusters reduce cost. This preserves existing code.
Quick Answer
The answer is to use Cloud Dataproc with ephemeral clusters and Cloud Storage instead of HDFS for data storage. This architecture is correct because Cloud Storage is HDFS-compatible, meaning existing Spark jobs and Hive queries can run without any code changes, while ephemeral clusters automatically spin down when idle, drastically reducing costs for batch and interactive SQL workloads on 50 TB of growing data. On the Google Professional Data Engineer exam, this scenario tests your understanding of the separation of compute and storage—a core principle of cloud-native data engineering—and the common trap is choosing persistent clusters or managed services like BigQuery that would require rewriting job configurations. Remember the memory tip: “Ephemeral compute, persistent storage” to instantly recall that Dataproc clusters are temporary but Cloud Storage keeps your data always available, preserving existing code and minimizing migration friction.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 their on-premises Hadoop cluster to Google Cloud. The existing cluster runs HDFS, Hive, and Spark jobs. The migration must minimize changes to existing job code and configuration. The data volume is 50 TB and growing. The team expects to run both batch and interactive SQL queries. Which architecture should they use?
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 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
Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries.
Option D is correct because it uses Cloud Storage as the underlying storage layer, which is HDFS-compatible and allows existing Spark jobs to run without code changes. Ephemeral Dataproc clusters reduce costs and provide native Hive support for interactive SQL queries, meeting both batch and interactive requirements without altering job configurations.
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.
- ✗
Keep HDFS on persistent Cloud Dataproc clusters and use BigQuery for SQL queries.
Why it's wrong here
Persistent clusters with HDFS are costly; BigQuery cannot directly access HDFS data.
- ✗
Use Cloud Dataflow for all batch processing and BigQuery for storage and querying.
Why it's wrong here
This would require rewriting all ETL code (Spark to Dataflow SDK) and migrating Hive queries to BigQuery SQL, which is a major change.
- ✗
Migrate HDFS to Cloud Storage, create a Cloud Dataproc cluster for Spark jobs, and use BigQuery for interactive SQL queries via a Hive metastore linked to BigQuery.
Why it's wrong here
Hive metastore linked to BigQuery is not straightforward; existing Spark jobs would need changes.
- ✓
Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries.
Why this is correct
Cloud Dataproc can use Cloud Storage as the data layer; most Spark and Hive jobs need minimal changes (e.g., file path prefix). Ephemeral clusters reduce cost. This preserves existing code.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that BigQuery must be used for all SQL queries in a migration, ignoring that Dataproc's Hive can directly query data in Cloud Storage without code changes, making it a simpler path for interactive SQL on existing Hive workloads.
Detailed technical explanation
How to think about this question
Cloud Storage implements the Hadoop FileSystem API via the gs:// connector, allowing Spark and Hive to read/write data without code changes. Ephemeral Dataproc clusters auto-scale and terminate after job completion, reducing costs for batch workloads, while the built-in Hive on Dataproc uses Cloud Storage as its warehouse directory, enabling interactive SQL queries via JDBC/ODBC with minimal latency compared to BigQuery for small-to-medium datasets.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries. — Option D is correct because it uses Cloud Storage as the underlying storage layer, which is HDFS-compatible and allows existing Spark jobs to run without code changes. Ephemeral Dataproc clusters reduce costs and provide native Hive support for interactive SQL queries, meeting both batch and interactive requirements without altering job configurations.
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
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Last reviewed: Jun 30, 2026
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