Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A company runs a data processing pipeline on a single Compute Engine instance in us-west1-a. The instance reads data from Cloud Storage, processes it, and writes results back to Cloud Storage. The pipeline runs once per day and takes about 6 hours. Recently, the instance has been experiencing out-of-memory errors, causing the pipeline to fail. The operations team wants a cost-effective solution that can handle varying data volumes without manual intervention. They also want to ensure the pipeline completes within the daily window. What should they do?
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
✓
Redesign the pipeline to run on Cloud Dataflow and use batch mode with autoscaling.
The correct option is B: redesigning the pipeline to run on Cloud Dataflow in batch mode with autoscaling directly addresses the out-of-memory failures and varying data volumes, because Dataflow dynamically scales worker instances up or down based on workload and distributes processing across many workers, so no single machine's memory limits the job. It is also cost-effective for a once-per-day, ~6-hour job since batch mode only consumes resources while the job runs and can complete within the daily window. Option A does not fit because a managed instance group autoscaling on CPU still runs the pipeline on individual VMs and does not solve the per-instance memory ceiling or the need to parallelize the data processing. Option C is wrong because streaming mode is designed for continuously arriving data and would keep resources running and cost more for a daily batch pipeline. Option D only temporarily relieves memory pressure and does not handle varying data volumes or provide automatic scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a managed instance group with autoscaling based on CPU utilization.
Why it's wrong here
Autoscaling on CPU adds instances, yet each pipeline instance still loads the full dataset into memory, so out-of-memory failures persist. Managed instance groups suit stateless horizontally scalable web tiers, not a single daily batch job whose memory footprint per worker is the actual constraint.
- ✓
Redesign the pipeline to run on Cloud Dataflow and use batch mode with autoscaling.
Why this is correct
Cloud Dataflow batch mode with autoscaling provisions workers dynamically, so memory scales with varying data volumes and no manual intervention is needed. It also parallelises processing, ensuring the 6-hour pipeline finishes within the daily window cost-effectively.
- ✗
Redesign the pipeline to run on Cloud Dataflow and use streaming mode.
Why it's wrong here
Streaming mode processes continuously arriving events, whereas this pipeline runs once daily over bounded Cloud Storage objects, so streaming adds cost and complexity without matching the batch trigger. Dataflow streaming is correct for unbounded, real-time data, not scheduled batch reads.
- ✗
Increase the memory of the existing instance to a larger machine type.
Why it's wrong here
Resizing to a larger machine type gives more memory but remains a fixed single instance, so varying data volumes still exceed capacity and manual intervention is required. Vertical scaling suits predictable, steady workloads, not the stem's fluctuating volumes and unattended daily completion requirement.
Go deeper
Related to this question
Learn chapter
Data Lifecycle Management on Google Cloud
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Cloud storage
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.