hardMultiple Choice
PMLE Practice Question: Runs a batch prediction job on Vertex AI for a…
An organization runs a batch prediction job on Vertex AI for a large dataset (10 TB). The job is configured to use a cluster of 100 n1-standard-16 machines. Midway through, the job fails with 'Out of memory' errors. What is the most effective mitigation strategy?
⚠ Common exam trap
Test-takers frequently confuse scaling horizontally (adding more machines) with scaling vertically (increasing per-machine resources), assuming that distributing data further will fix memory exhaustion when the bottleneck is per-node RAM capacity, not data volume per node.
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 a machine type with more memory per instance.
The 'Out of memory' error indicates that individual worker nodes are running out of RAM when processing their assigned data shards. Using a machine type with more memory per instance (e.g., n1-highmem-16) directly addresses the root cause by providing each node with sufficient memory to hold the model and its intermediate computations, without changing the data distribution or parallelism strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Split the input data into smaller chunks and run multiple jobs.
Why it's wrong here
Chunking reduces per-job data volume but leaves each worker's memory footprint unchanged, so the same out-of-memory failure recurs on the same machine type. It is tempting because splitting large datasets across parallel jobs is standard for throughput scaling, and would be correct if the bottleneck were job duration or quota rather than per-machine memory.
- ✗
Enable model parallelism within the prediction script.
Why it's wrong here
Model parallelism splits a single model's layers across devices, which addresses models too large for one accelerator's memory, not per-worker batch memory pressure. It is tempting because it is the standard remedy when a model itself cannot fit on one machine.
- ✗
Increase the number of machines to distribute data more.
Why it's wrong here
Adding machines increases aggregate throughput but each worker still processes the same batch size, so per-machine memory usage is unchanged. It is tempting because horizontal scaling normally resolves Vertex AI batch prediction slowness, and the cluster size is the most visible setting.
- ✓
Use a machine type with more memory per instance.
Why this is correct
The 'Out of memory' failure indicates each worker exhausts RAM while processing its shard, not that the cluster is too small. Moving to a machine type with more memory per instance directly addresses the per-instance memory ceiling.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.