- A
Reduce the batch size of prediction requests sent to the endpoint.
Why wrong: Reducing batch size may help but only if the client is sending batched requests; the error suggests individual requests are large.
- B
Increase the memory limit in the Vertex AI endpoint configuration.
Configuring a higher memory machine type or increasing the memory limit in the container spec provides the needed resources.
- C
Optimize the model by quantizing weights to reduce model size.
Why wrong: While quantization reduces model size, it may not solve the OOM issue if the problem is memory during computation, and it could affect accuracy.
- D
Use a machine type with higher CPU performance.
Why wrong: CPU performance does not directly resolve memory exhaustion; memory is the bottleneck.
Quick Answer
The correct action is to increase the memory limit in the Vertex AI endpoint configuration. This resolves the out of memory error in Vertex AI custom container inference because the endpoint’s machine type directly dictates the RAM available to your container; when a TensorFlow model demands significant memory during prediction, selecting a high-memory machine like n1-highmem-8 ensures the container has enough heap space to complete inference without crashing. On the Google Professional Data Engineer exam, this scenario tests your understanding of Vertex AI resource allocation versus code-level fixes—many candidates mistakenly try to optimize the model or add swap space, but the exam emphasizes that endpoint machine types are the primary control for memory. A common trap is confusing container resource requests with endpoint machine specs; remember that Vertex AI endpoints ignore container-level memory limits and instead enforce the chosen machine’s RAM. Memory tip: “Highmem for high demand”—when you see “out of memory” on a custom container, think machine type upgrade, not code rewrite.
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 data engineer deploys a TensorFlow model on Vertex AI using a custom container. After deployment, online prediction requests sometimes fail with a 500 error and the message 'Out of memory'. The model requires significant memory during inference. Which action should the engineer take to resolve this issue?
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 memory limit in the Vertex AI endpoint configuration.
Option B is correct because Vertex AI endpoints allow you to configure a machine type with a specific memory limit. When a custom container runs out of memory during inference, increasing the memory allocation (e.g., by selecting a machine type with more RAM, such as n1-highmem-8) directly addresses the 'Out of memory' error. This ensures the container has sufficient resources to handle the model's inference workload without crashing.
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.
- ✗
Reduce the batch size of prediction requests sent to the endpoint.
Why it's wrong here
Reducing batch size may help but only if the client is sending batched requests; the error suggests individual requests are large.
- ✓
Increase the memory limit in the Vertex AI endpoint configuration.
Why this is correct
Configuring a higher memory machine type or increasing the memory limit in the container spec provides the needed resources.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Optimize the model by quantizing weights to reduce model size.
Why it's wrong here
While quantization reduces model size, it may not solve the OOM issue if the problem is memory during computation, and it could affect accuracy.
- ✗
Use a machine type with higher CPU performance.
Why it's wrong here
CPU performance does not directly resolve memory exhaustion; memory is the bottleneck.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing batch size or optimizing the model (quantization) is the first step to fix runtime OOM errors, when in fact the immediate operational fix is to allocate more memory to the deployment.
Detailed technical explanation
How to think about this question
Vertex AI custom container deployments use machine types from Compute Engine, where memory is tied to the machine series (e.g., n1-standard-4 has 15 GB RAM, n1-highmem-4 has 26 GB). The 'Out of memory' error typically occurs when the model's working set (weights, activations, intermediate tensors) exceeds the container's memory limit. Increasing the machine type to a high-memory series or a custom configuration with more RAM is the direct fix, as it allocates more physical memory to the container without code changes.
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.
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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 memory limit in the Vertex AI endpoint configuration. — Option B is correct because Vertex AI endpoints allow you to configure a machine type with a specific memory limit. When a custom container runs out of memory during inference, increasing the memory allocation (e.g., by selecting a machine type with more RAM, such as n1-highmem-8) directly addresses the 'Out of memory' error. This ensures the container has sufficient resources to handle the model's inference workload without crashing.
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: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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