This PMLE practice question tests your understanding of pmle exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Exhibit
Refer to the exhibit:
$ gcloud ai endpoints deploy-model $ENDPOINT_ID \
--model $MODEL_ID \
--display-name=my-model \
--machine-type=n1-standard-2 \
--min-replica-count=1 \
--max-replica-count=5 \
--traffic-split=0=100
ERROR: (gcloud.ai.endpoints.deploy-model) RESOURCE_EXHAUSTED: The machine type n1-standard-2 is not available in region us-central1 for AutoML models.
A user receives this error when deploying an AutoML model. What should they do?
Exhibit
Refer to the exhibit:
$ gcloud ai endpoints deploy-model $ENDPOINT_ID \
--model $MODEL_ID \
--display-name=my-model \
--machine-type=n1-standard-2 \
--min-replica-count=1 \
--max-replica-count=5 \
--traffic-split=0=100
ERROR: (gcloud.ai.endpoints.deploy-model) RESOURCE_EXHAUSTED: The machine type n1-standard-2 is not available in region us-central1 for AutoML models.
A
Change the region to us-west1
Why wrong: Wrong: Machine type restriction may be global, not regional.
B
Use machine type n1-highmem-2
Correct: n1-highmem-2 is a supported machine type for AutoML.
C
Increase the min-replica-count to 2
Why wrong: Wrong: Does not fix machine type availability.
D
Remove the traffic-split flag
Why wrong: Wrong: Traffic split is not the cause of the error.
The answer is to use machine type n1-highmem-2 when deploying an AutoML model to resolve an OOM error. This is correct because AutoML models, especially those with complex architectures or large feature spaces, require substantial memory to load the model graph and handle prediction requests; the default machine type often lacks the necessary RAM, causing the out-of-memory failure. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of deployment resource allocation and the trade-off between compute and memory—a common trap is to scale up CPU cores instead of memory, which does not address the root cause. Remember that AutoML models are memory-intensive, not necessarily compute-intensive, so prioritize high-memory machine families like n1-highmem. For a quick memory tip: think "High Mem for AutoML"—when you see OOM, go straight to a high-memory machine type.
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 machine type n1-highmem-2
When deploying an AutoML model, memory constraints are a common cause of deployment failures. Using a machine type with higher memory, such as n1-highmem-2, helps ensure the model can be loaded and served without out-of-memory errors. The other options do not address memory requirements: changing the region does not affect compute resources, increasing the min replica count does not increase per-instance memory, and removing traffic-split flags does not resolve memory issues.
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.
✗
Change the region to us-west1
Why it's wrong here
Wrong: Machine type restriction may be global, not regional.
✓
Use machine type n1-highmem-2
Why this is correct
Correct: n1-highmem-2 is a supported machine type for AutoML.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the min-replica-count to 2
Why it's wrong here
Wrong: Does not fix machine type availability.
✗
Remove the traffic-split flag
Why it's wrong here
Wrong: Traffic split is not the cause of the error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling (increasing replicas) with resource allocation (increasing memory per replica), leading them to choose Option C instead of addressing the per-instance memory bottleneck.
Detailed technical explanation
How to think about this question
AutoML models, especially those with large feature spaces or complex architectures, can require significant RAM during inference. The `n1-highmem-2` machine type offers 13 GB of memory compared to the default `n1-standard-2` (7.5 GB), which is often critical for models exceeding the default memory threshold. In real-world scenarios, this error commonly occurs when deploying models with high-dimensional embeddings or ensemble methods, where memory usage spikes during the first prediction request.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Visual reference
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.
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use machine type n1-highmem-2 — When deploying an AutoML model, memory constraints are a common cause of deployment failures. Using a machine type with higher memory, such as n1-highmem-2, helps ensure the model can be loaded and served without out-of-memory errors. The other options do not address memory requirements: changing the region does not affect compute resources, increasing the min replica count does not increase per-instance memory, and removing traffic-split flags does not resolve memory issues.
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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