Google ACE Deploying and implementing a cloud solution Practice Question
This ACE practice question tests your understanding of deploying and implementing a cloud solution. 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 data analytics team needs to run a Spark job on a schedule. They want to minimize operational overhead and only pay for resources used during job execution. Which service 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Dataproc workflow templates with scheduled execution
Option B is correct because Dataproc workflow templates allow you to define a Spark job as a workflow and schedule its execution using Cloud Scheduler or a cron-like mechanism. This minimizes operational overhead by automatically provisioning a cluster, running the job, and tearing down the cluster when finished, ensuring you only pay for resources used during execution.
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.
✗
Create a Dataproc cluster and keep it running for ad-hoc jobs
Why it's wrong here
A long-running cluster costs money even when idle.
✓
Use Dataproc workflow templates with scheduled execution
Why this is correct
Workflow templates create ephemeral clusters that are deleted after job completion.
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.
✗
Provision Compute Engine instances with Spark installed and start/stop them manually
Why it's wrong here
Manual management is error-prone and not optimized for scheduling.
✗
Use BigQuery for all analytics
Why it's wrong here
BigQuery is not a Spark runtime.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between persistent clusters (always-on) and ephemeral clusters (created on-demand), and the trap here is that candidates may assume any Dataproc usage is cost-effective, overlooking that only workflow templates with scheduled execution enforce automatic teardown.
Detailed technical explanation
How to think about this question
Dataproc workflow templates leverage the Dataproc API to define a sequence of jobs (e.g., Spark, PySpark, Hive) with cluster configuration, and when triggered via Cloud Scheduler or the Dataproc Workflows API, they automatically create a cluster, execute the jobs, and delete the cluster upon completion. This is achieved through the 'deleteTtl' and 'cluster_config' settings in the template, which enforce ephemeral cluster behavior. A real-world scenario is a daily ETL pipeline that processes logs from Cloud Storage into BigQuery, where the cluster spins up for 10 minutes and then disappears, costing only for that window.
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
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this ACE question in full detail.
Deploying and implementing a cloud solution — This question tests Deploying and implementing a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Dataproc workflow templates with scheduled execution — Option B is correct because Dataproc workflow templates allow you to define a Spark job as a workflow and schedule its execution using Cloud Scheduler or a cron-like mechanism. This minimizes operational overhead by automatically provisioning a cluster, running the job, and tearing down the cluster when finished, ensuring you only pay for resources used during execution.
What should I do if I get this ACE 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.
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