- A
Use a machine type from the n1-highmem series, such as n1-highmem-32 (208 GB) or higher.
n1-highmem provides high memory per CPU, with sizes up to 416 GB.
- B
Use multiple replicas and split the model across them.
Why wrong: Model parallelism across replicas is complex and not directly supported by Vertex AI endpoints without custom container logic.
- C
Use a custom container to load the model and optimize its memory footprint.
You can optimize the model (e.g., quantize, use memory-efficient libraries) to fit within available memory.
- D
Use batch prediction instead of online prediction.
Why wrong: Batch prediction also runs on machines with memory limits; it does not solve the per-instance memory requirement.
- E
Deploy the model on Cloud Run with 32 GB memory.
Why wrong: Cloud Run max memory is 32 GB, insufficient for 200 GB.
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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.
You need to deploy a model that requires a large amount of memory (over 200 GB) for inference. The model is a custom PyTorch model. Vertex AI endpoints have machine type limitations. Which TWO actions can you take to handle this memory requirement? (Choose 2 correct answers)
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
Use a machine type from the n1-highmem series, such as n1-highmem-32 (208 GB) or higher.
Option A is correct because the n1-highmem-32 machine type provides 208 GB of memory, which meets the requirement of over 200 GB. Vertex AI endpoints support this machine series, allowing you to deploy a custom PyTorch model with sufficient RAM for inference without modification.
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.
- ✓
Use a machine type from the n1-highmem series, such as n1-highmem-32 (208 GB) or higher.
Why this is correct
n1-highmem provides high memory per CPU, with sizes up to 416 GB.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use multiple replicas and split the model across them.
Why it's wrong here
Model parallelism across replicas is complex and not directly supported by Vertex AI endpoints without custom container logic.
- ✓
Use a custom container to load the model and optimize its memory footprint.
Why this is correct
You can optimize the model (e.g., quantize, use memory-efficient libraries) to fit within available memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use batch prediction instead of online prediction.
Why it's wrong here
Batch prediction also runs on machines with memory limits; it does not solve the per-instance memory requirement.
- ✗
Deploy the model on Cloud Run with 32 GB memory.
Why it's wrong here
Cloud Run max memory is 32 GB, insufficient for 200 GB.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that model parallelism across replicas is a valid strategy for memory constraints, but in Vertex AI, replicas are stateless and cannot share model weights for a single inference request.
Detailed technical explanation
How to think about this question
The n1-highmem series uses Intel Xeon processors and offers high memory-to-vCPU ratios, with n1-highmem-32 providing 6.5 GB per vCPU. For custom PyTorch models, memory optimization techniques like quantization or model sharding can further reduce footprint, but the primary solution is selecting a machine type with adequate RAM. Vertex AI endpoints also support custom containers, enabling you to load the model with memory-efficient libraries like TorchScript or ONNX Runtime.
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 PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a machine type from the n1-highmem series, such as n1-highmem-32 (208 GB) or higher. — Option A is correct because the n1-highmem-32 machine type provides 208 GB of memory, which meets the requirement of over 200 GB. Vertex AI endpoints support this machine series, allowing you to deploy a custom PyTorch model with sufficient RAM for inference without modification.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
This PMLE 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 PMLE exam.
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