The answer is to set the accelerator type and count in the model deployment configuration. This is necessary because Vertex AI inference requires an explicit machine type that includes a GPU, such as an n1-standard-4 with an attached NVIDIA Tesla T4, to provide the physical hardware for GPU acceleration; without selecting a GPU machine type, the inference will default to CPU regardless of any other settings. On the Google Professional Machine Learning Engineer exam, this tests your understanding of Vertex AI’s deployment architecture, where the common trap is assuming that a GPU-enabled model will automatically use a GPU—it will not unless you specify the accelerator in the deployment resource. Remember the mnemonic “GPU is not automatic; you must attach it to the machine” to avoid this pitfall.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
A company has a TensorFlow model that requires GPU for inference. They are deploying on Vertex AI. Which TWO configurations are necessary to ensure GPU is used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Select a machine type that includes a GPU (e.g., NVIDIA Tesla T4).
Option D is correct because Vertex AI requires you to explicitly select a machine type that includes a GPU (e.g., n1-standard-4 with an attached NVIDIA Tesla T4) to provide the physical hardware for GPU acceleration. Without selecting a GPU machine type, the inference will run on CPU only, regardless of any other configuration.
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.
✗
Set the environment variable TF_GPU_ALLOCATOR=cuda_malloc_async.
Why it's wrong here
Optional performance tweak, not required for GPU use.
✗
Use the pre-built TensorFlow serving container, which automatically uses GPU if available.
Why it's wrong here
The pre-built container does not automatically use GPU; accelerator must be attached.
✗
Build a custom container with GPU drivers.
Why it's wrong here
Pre-built containers already have needed drivers; custom container not required.
✓
Select a machine type that includes a GPU (e.g., NVIDIA Tesla T4).
Why this is correct
Necessary to have GPU hardware available.
Related concept
Read the scenario before looking for a memorised answer.
✓
Set the accelerator type and count in the model deployment configuration.
Why this is correct
Attaches the GPU to the container.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that simply using a pre-built container or setting environment variables is sufficient to enable GPU acceleration, when in fact you must both select a GPU-capable machine type and explicitly configure the accelerator in the deployment settings.
Detailed technical explanation
How to think about this question
Vertex AI uses the `accelerator` field in the model deployment configuration (via the `Endpoint.deploy_model` API or gcloud command) to specify the type and count of GPUs, such as `NVIDIA_TESLA_T4` and `count=1`. The machine type must be compatible with the chosen accelerator (e.g., n1-standard-4 supports attaching GPUs, while e2-standard-4 does not). A common real-world mistake is selecting a GPU-capable machine type but forgetting to set the accelerator configuration, resulting in the GPU being physically present but not utilized by the serving container.
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: Select a machine type that includes a GPU (e.g., NVIDIA Tesla T4). — Option D is correct because Vertex AI requires you to explicitly select a machine type that includes a GPU (e.g., n1-standard-4 with an attached NVIDIA Tesla T4) to provide the physical hardware for GPU acceleration. Without selecting a GPU machine type, the inference will run on CPU only, regardless of any other configuration.
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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