The answer is to use a machine type with more memory per instance, such as n1-highmem-16. This is correct because the 'Out of memory' error in a batch prediction job on Vertex AI occurs when individual worker nodes exhaust their RAM while processing assigned data shards, not because of cluster size or data distribution. By upgrading to a high-memory machine type, each node gains sufficient capacity to hold the model and intermediate computations, directly resolving the bottleneck. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of resource tuning for batch prediction workloads, where a common trap is to increase the number of machines rather than per-instance memory. Remember the memory tip: when a batch prediction job fails mid-stream, think "shard size vs. node memory"—more RAM per node, not more nodes, fixes the OOM.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Workaround, but does not address the root cause; each chunk still processed on same machine type.
✗
Enable model parallelism within the prediction script.
Why it's wrong here
Model parallelism is for model too large for one device, not for data processing OOM.
✗
Increase the number of machines to distribute data more.
Why it's wrong here
Does not increase per-instance memory; OOM persists on each machine.
✓
Use a machine type with more memory per instance.
Why this is correct
Directly addresses the OOM by providing more memory for each worker.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Vertex AI batch prediction uses sharding: the input data is split into shards, and each worker processes one shard at a time. The n1-standard-16 machine has 16 vCPUs and 60 GB of memory; if the model itself consumes a large portion of that (e.g., a large embedding table or deep network), the remaining memory for the batch of input data may be insufficient. Upgrading to n1-highmem-16 (16 vCPUs, 104 GB memory) or n1-highmem-32 provides the headroom needed without altering the job configuration or data layout. In practice, you can also adjust the batch size in the prediction script to reduce per-node memory usage, but the most effective single change is to use a memory-optimized machine type.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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