The answer is to increase the GPU machine type to one with more memory, such as moving from an NVIDIA T4 to an A100. This resolves the CUDA out of memory error because the PyTorch model’s batch size, intermediate tensors, or model weights exceed the available GPU VRAM on the current machine type, causing the CUDA runtime to fail during inference. On the Google Professional Data Engineer exam, this scenario tests your understanding of Vertex AI custom container deployment and GPU resource allocation, often appearing as a trap where candidates mistakenly try to add CPUs or enable monitoring instead of addressing the hardware bottleneck. A common pitfall is assuming software fixes like model monitoring or CPU scaling can compensate for insufficient GPU memory, but the root cause is purely hardware capacity. Remember the memory tip: “GPU OOM? Upgrade the VRAM, not the CPU plan.”
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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.
A financial services company deploys a fraud detection model on Vertex AI using a custom prediction container that runs a PyTorch model. The model requires GPU acceleration. The deployment succeeds but predictions return an error: 'CUDA error: out of memory'. What should the team do to resolve this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the GPU machine type to one with more memory (e.g., from NVIDIA T4 to A100)
Option A is correct because the GPU memory is insufficient; using a machine with more GPU memory or optimizing the model is the solution. Option B (enabling model monitoring) does not fix memory. Option C (adding more CPUs) does not address GPU memory. Option D (using CPU-only) would defeat the purpose of GPU acceleration.
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 container to use a CPU-only image to avoid CUDA errors
Why it's wrong here
This would break the PyTorch model's GPU dependency and likely cause different errors.
✓
Increase the GPU machine type to one with more memory (e.g., from NVIDIA T4 to A100)
Why this is correct
The CUDA out of memory error indicates the current GPU cannot hold the model; a larger GPU or model optimization is needed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable Vertex AI Model Monitoring to automatically scale the endpoint
Why it's wrong here
Model monitoring does not affect GPU memory allocation.
✗
Add CPU replicas to distribute the inferencing load
Why it's wrong here
CPU replicas do not share GPU memory; the model still requires GPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the GPU machine type to one with more memory (e.g., from NVIDIA T4 to A100) — Option A is correct because the GPU memory is insufficient; using a machine with more GPU memory or optimizing the model is the solution. Option B (enabling model monitoring) does not fix memory. Option C (adding more CPUs) does not address GPU memory. Option D (using CPU-only) would defeat the purpose of GPU acceleration.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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