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Google ACE Practice Question: A data analytics team needs to run a Spark job on…
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?
⚠ Common exam trap
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.
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 Dataproc workflow templates with scheduled execution
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.
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 incurs continuous compute and licensing costs even when no jobs are running; for a scheduled workload, you'd be paying for idle capacity between runs. It also requires manual cluster management and does not leverage Dataproc's managed autoscaling or deletion policies, making it inefficient and operationally heavier than ephemeral clusters.
- ✓
Use Dataproc workflow templates with scheduled execution
Why this is correct
Workflow templates allow you to define a cluster configuration and job list, then instantiate it on a schedule via Cloud Scheduler or Dataproc's scheduled deletion. The template creates an ephemeral cluster at job start, runs the Spark job, and deletes the cluster after completion, ensuring you pay only for the duration of the job. This integrates natively with Cloud Scheduler and manages cluster lifecycle automatically.
- ✗
Provision Compute Engine instances with Spark installed and start/stop them manually
Why it's wrong here
Manual start/stop is error-prone and requires custom scripts for scheduling, health checks, and cluster coordination. Unlike Dataproc, you must handle software updates, configuration drift, and reconnection logic yourself. There is no built-in integration with Cloud Scheduler or workflow templates, so scaling and lifecycle management become operational burdens.
- ✗
Use BigQuery for all analytics
Why it's wrong here
BigQuery is a serverless data warehouse, not a Spark runtime. It cannot execute Spark code, so using it for all analytics would not satisfy the requirement to run a Spark job. While BigQuery is powerful for SQL queries and can be used for some ETL, it lacks a Spark engine and custom dependency support.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE 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 ACE exam.