- A
Change the container to use a CPU-only image to avoid CUDA errors
Why wrong: This would break the PyTorch model's GPU dependency and likely cause different errors.
- B
Increase the GPU machine type to one with more memory (e.g., from NVIDIA T4 to A100)
The CUDA out of memory error indicates the current GPU cannot hold the model; a larger GPU or model optimization is needed.
- C
Enable Vertex AI Model Monitoring to automatically scale the endpoint
Why wrong: Model monitoring does not affect GPU memory allocation.
- D
Add CPU replicas to distribute the inferencing load
Why wrong: CPU replicas do not share GPU memory; the model still requires GPU.
Question 979 of 1,000
Resolving CUDA Out of Memory Error on Vertex AI — GPU Memory Issues | Google Professional Data Engineer Explained
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?
Quick Answer
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.”
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
✓
Increase the GPU machine type to one with more memory (e.g., from NVIDIA T4 to A100)
Option B is correct because the CUDA out-of-memory error indicates that the GPU's VRAM is insufficient to load the PyTorch model or process the inference batch. Increasing the GPU machine type to one with more memory, such as from an NVIDIA T4 (16 GB) to an A100 (40 or 80 GB), directly resolves the capacity issue. Vertex AI prediction endpoints allow you to select different accelerator types and sizes, and this change ensures the model fits within GPU memory.
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
The trap here is that candidates may confuse a resource exhaustion error (out of memory) with a scaling or monitoring issue, leading them to choose options like Model Monitoring or adding CPU replicas, rather than recognizing the need for a larger GPU machine type.
Detailed technical explanation
How to think about this question
CUDA out-of-memory errors occur when the model weights, activations, and batch data exceed the GPU's VRAM capacity. For PyTorch models, the memory footprint depends on model size (number of parameters), data type (e.g., FP32 vs FP16), and batch size. In Vertex AI, you can specify the machine type and accelerator (e.g., n1-standard-4 with NVIDIA_TESLA_T4) in the endpoint deployment; upgrading to a machine type with a larger GPU (e.g., a2-highgpu-1g with A100) provides more VRAM. A real-world scenario is deploying a large transformer model (e.g., BERT-large) on a T4, which may exceed 16 GB even with a small batch size, requiring an A100 or memory optimization techniques like gradient checkpointing.
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
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FAQ
Questions learners often ask
What does this PDE question test?
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 B is correct because the CUDA out-of-memory error indicates that the GPU's VRAM is insufficient to load the PyTorch model or process the inference batch. Increasing the GPU machine type to one with more memory, such as from an NVIDIA T4 (16 GB) to an A100 (40 or 80 GB), directly resolves the capacity issue. Vertex AI prediction endpoints allow you to select different accelerator types and sizes, and this change ensures the model fits within GPU memory.
What should I do if I get this PDE 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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Last reviewed: Jul 4, 2026
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