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.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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?
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.
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
The error occurs because AutoML models require a machine type with sufficient memory to load the model and perform predictions. The default machine type may not have enough memory for the model's size, leading to an out-of-memory (OOM) error. Using `n1-highmem-2` provides higher memory per core, which resolves the memory constraint without changing other deployment parameters.
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
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — 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 — The error occurs because AutoML models require a machine type with sufficient memory to load the model and perform predictions. The default machine type may not have enough memory for the model's size, leading to an out-of-memory (OOM) error. Using `n1-highmem-2` provides higher memory per core, which resolves the memory constraint without changing other deployment parameters.
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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