Google PCA Manage implementation of cloud architecture Practice Question
A company runs a data analytics platform on Google Cloud using BigQuery, Dataflow, and Cloud Storage. They notice that Dataflow jobs are failing with 'out of memory' errors for certain large pipelines. The pipelines process variable amounts of data, sometimes spiking 10x normal. Which strategy should they use to handle these spikes cost-effectively?
⚠ Common exam trap
Google Cloud often tests the distinction between batch and streaming optimizations, and candidates mistakenly apply Streaming Engine (designed for stateful streaming) to batch pipelines suffering from memory spikes, missing the cost-effective autoscaling with preemptible VMs strategy.
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
✓
Configure the Dataflow pipeline to use autoscaling with a higher maximum number of workers and use preemptible VMs for cost savings.
Dataflow's autoscaling can dynamically add workers to handle sudden data spikes, and using preemptible VMs significantly reduces cost for batch pipelines that can tolerate interruptions. This approach avoids manual intervention and over-provisioning, making it cost-effective for variable workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually monitor the job and increase the number of workers when a spike is detected.
Why it's wrong here
Manual intervention is not cost-effective and may cause delays.
- ✗
Increase the machine type of the workers to a high-memory type and disable autoscaling.
Why it's wrong here
This over-provisions during normal loads and increases cost.
- ✓
Configure the Dataflow pipeline to use autoscaling with a higher maximum number of workers and use preemptible VMs for cost savings.
Why this is correct
Autoscaling adjusts workers dynamically; preemptible VMs reduce cost for fault-tolerant work.
- ✗
Use Dataflow Streaming Engine to offload state to persistent storage and reduce memory usage.
Why it's wrong here
Streaming Engine is for streaming pipelines; this is likely batch, and it doesn't address OOM directly.
Go deeper
Related to this question
Learn chapter
Introduction to Google Cloud Platform
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
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This PCA 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 PCA exam.