A team runs a Dataproc job that processes data every hour, and the cluster is also used for ad-hoc queries throughout the day. Which cluster type is most appropriate for this workload pattern?

A single job-based cluster that is reused for all jobs

Job-based cluster that is created for each hourly job and deleted after

A persistent cluster that is stopped between jobs to save cost

Persistent cluster that remains running to serve both the hourly jobs and ad-hoc queries

A data engineering team runs a batch ETL job every night that processes the same volume of data. The job currently runs on a large Dataproc cluster that is left running 24/7. Which cost optimization strategy would most directly reduce costs while still meeting the nightly processing requirement?

Use preemptible (spot) VMs for the worker nodes of the cluster

Move the cluster to a different region with lower network latency

Migrate the job to a larger cluster to reduce runtime

Increase the number of worker nodes to improve parallelism

A data engineering team runs a nightly Dataproc batch job that processes a large dataset. The job's resource usage varies significantly: it starts with a heavy shuffle phase, then has a long period of low CPU usage, and finally a short burst of high memory usage. The team wants to minimize cost while ensuring the job completes within the nightly window. What should they configure?

Enable secondary worker autoscaling based on memory utilization.

Use a fixed cluster sized for the peak memory requirement.

Enable primary worker autoscaling based on CPU utilization.

Enable autoscaling on both primary and secondary workers with a policy that scales based on the job's resource needs.

A company runs a critical data processing job that must complete within a strict SLA every morning. The job's resource usage varies significantly day to day. Which approach best ensures sufficient resources are available for this business-critical process?

Use autoscaling with a minimum size that covers the baseline and a maximum that covers the peak, with priority given to this job

Configure autoscaling with a minimum cluster size that meets the peak demand

Use a job-based cluster that is created just before the job runs and deleted after

Use a fixed-size cluster that is always running to guarantee capacity

A data engineering team has a nightly batch job that is business-critical and must finish before the start of the business day. They are planning capacity for the cluster. Which capacity planning practice is most important to ensure the job completes on time?

Over-provision the cluster to handle the maximum possible data volume every night

Analyze historical job runtimes and data volumes to determine a baseline cluster size with a buffer for spikes

Rely on autoscaling alone without any minimum cluster size

Use the smallest cluster possible to minimize cost, and accept occasional delays

Google CloudProfessional Data Engineer

Domain 5Objective 1

5.1 Optimizing Resources PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 1)

Part of the Maintaining and automating data workloads domain, which accounts for ~18% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~9–14 in this domain), expect 2–3 from this objective — we provide 11 practice questions to prepare you well beyond it. (estimate)

11questions here

3free pages

5concepts

~18%of the exam

Questions 1–5

1foundation · easy
A team runs a Dataproc job that processes data every hour, and the cluster is also used for ad-hoc queries throughout the day. Which cluster type is most appropriate for this workload pattern?
Select an answer first
2foundation · easy
A data engineering team runs a batch ETL job every night that processes the same volume of data. The job currently runs on a large Dataproc cluster that is left running 24/7. Which cost optimization strategy would most directly reduce costs while still meeting the nightly processing requirement?
Select an answer first
3application · medium
A data engineering team runs a nightly Dataproc batch job that processes a large dataset. The job's resource usage varies significantly: it starts with a heavy shuffle phase, then has a long period of low CPU usage, and finally a short burst of high memory usage. The team wants to minimize cost while ensuring the job completes within the nightly window. What should they configure?
Select an answer first
4foundation · easy
A company runs a critical data processing job that must complete within a strict SLA every morning. The job's resource usage varies significantly day to day. Which approach best ensures sufficient resources are available for this business-critical process?
Select an answer first
5foundation · easy
A data engineering team has a nightly batch job that is business-critical and must finish before the start of the business day. They are planning capacity for the cluster. Which capacity planning practice is most important to ensure the job completes on time?
Select an answer first

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