- A
Cloud Dataflow, for running the Apache Spark code as a streaming pipeline
Why wrong: Cloud Dataflow runs Apache Beam (not Spark) pipelines. If the existing code is written in Spark (PySpark, Scala Spark), Dataproc is the appropriate managed platform. Migrating Spark to Beam requires significant code changes.
- B
Cloud Dataproc, which runs managed Apache Spark clusters that can be created for the job and deleted on completion — paying only during the processing window
Dataproc is the correct choice for managed Apache Spark. The ephemeral cluster pattern (create cluster → run Spark job → delete cluster) is the recommended cost-optimization approach for batch jobs. The cluster exists only while needed, minimizing cost.
- C
Compute Engine VMs, by manually installing Apache Spark on a cluster of VMs each day before the job
Why wrong: Manually managing VM clusters with custom Spark installations requires significant operational effort and time for cluster setup. Dataproc automates cluster creation, Spark installation, and configuration in minutes.
- D
BigQuery, by running the Spark transformation directly within BigQuery's execution engine
Why wrong: BigQuery executes SQL-based queries and BigQuery ML models, not Apache Spark code. While BigQuery can run Apache Spark through BigQuery Spark stored procedures (a newer feature), the standard managed Spark platform is Dataproc.
Quick Answer
Cloud Dataproc is the correct choice because it provides managed Apache Spark clusters that can be created on demand for the batch job and automatically deleted upon completion, ensuring you only pay for the processing time. This ephemeral cluster model perfectly matches the requirement of a large cluster that exists only during the daily job, without manual infrastructure management. On the Google Cloud Digital Leader exam, this scenario tests your understanding of how Cloud Dataproc enables ephemeral Spark cluster batch processing on Google Cloud, distinguishing it from always-on services like Dataproc with persistent clusters or Dataflow for streaming. A common trap is choosing Cloud Dataflow, which excels at streaming but is not optimized for large-scale Spark batch jobs; remember that Dataproc is the native Spark and Hadoop service. Memory tip: think “Dataproc = Spark on demand, pay-per-batch” — if the job is batch and the cluster is temporary, Dataproc is your answer.
Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
This GCDL practice question tests your understanding of google cloud products, services, and solutions. 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 healthcare company needs to run a large batch processing job that analyzes patient records using Apache Spark, transforming data from Cloud Storage and writing results to BigQuery. The job runs once daily and requires a large cluster that should exist only during the job. Which Google Cloud product best handles this ephemeral large-batch Spark workload?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Cloud Dataproc, which runs managed Apache Spark clusters that can be created for the job and deleted on completion — paying only during the processing window
Cloud Dataproc is the correct choice because it provides managed Apache Spark clusters that can be created on demand for the batch job and automatically deleted upon completion, ensuring you only pay for the processing time. This ephemeral cluster model perfectly matches the requirement of a large cluster that exists only during the daily job, without manual infrastructure management.
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.
- ✗
Cloud Dataflow, for running the Apache Spark code as a streaming pipeline
Why it's wrong here
Cloud Dataflow runs Apache Beam (not Spark) pipelines. If the existing code is written in Spark (PySpark, Scala Spark), Dataproc is the appropriate managed platform. Migrating Spark to Beam requires significant code changes.
- ✓
Cloud Dataproc, which runs managed Apache Spark clusters that can be created for the job and deleted on completion — paying only during the processing window
Why this is correct
Dataproc is the correct choice for managed Apache Spark. The ephemeral cluster pattern (create cluster → run Spark job → delete cluster) is the recommended cost-optimization approach for batch jobs. The cluster exists only while needed, minimizing cost.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Compute Engine VMs, by manually installing Apache Spark on a cluster of VMs each day before the job
Why it's wrong here
Manually managing VM clusters with custom Spark installations requires significant operational effort and time for cluster setup. Dataproc automates cluster creation, Spark installation, and configuration in minutes.
- ✗
BigQuery, by running the Spark transformation directly within BigQuery's execution engine
Why it's wrong here
BigQuery executes SQL-based queries and BigQuery ML models, not Apache Spark code. While BigQuery can run Apache Spark through BigQuery Spark stored procedures (a newer feature), the standard managed Spark platform is Dataproc.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between managed services that run native Spark (Dataproc) versus those that use different execution engines (Dataflow, BigQuery), leading candidates to confuse Dataflow's ability to run batch pipelines with running Spark code directly.
Detailed technical explanation
How to think about this question
Cloud Dataproc leverages the Google Kubernetes Engine (GKE) or Compute Engine under the hood to provision clusters in under 90 seconds, using preemptible VMs for cost savings on transient workloads. The job can be submitted via the Dataproc Jobs API, which automatically scales the cluster based on the workload and deletes it upon completion, with billing metered per second for compute and storage. A real-world scenario involves a healthcare company processing 10 TB of patient records daily; Dataproc's ephemeral clusters avoid the cost of idle infrastructure while integrating natively with Cloud Storage and BigQuery via connectors.
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 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this GCDL question test?
Google Cloud products, services, and solutions — This question tests Google Cloud products, services, and solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Cloud Dataproc, which runs managed Apache Spark clusters that can be created for the job and deleted on completion — paying only during the processing window — Cloud Dataproc is the correct choice because it provides managed Apache Spark clusters that can be created on demand for the batch job and automatically deleted upon completion, ensuring you only pay for the processing time. This ephemeral cluster model perfectly matches the requirement of a large cluster that exists only during the daily job, without manual infrastructure management.
What should I do if I get this GCDL 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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 11, 2026
This GCDL 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 GCDL exam.
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