- A
Use a custom container that preloads the model into memory.
Preloading avoids loading model on each request, reducing latency.
- B
Use batch prediction instead of online prediction.
Why wrong: Batch prediction is asynchronous, not real-time.
- C
Use a machine type with a GPU accelerator.
GPU accelerates deep learning inference.
- D
Optimize the model using TorchScript or quantization.
TorchScript and quantization reduce inference time and model size.
- E
Deploy in multiple regions with Cloud Load Balancing.
Why wrong: Reduces network latency but not prediction latency.
Question 736 of 1,000
mediumMultiple SelectObjective-mapped
Optimize PyTorch Inference on Vertex AI
This PMLE practice question tests your understanding of custom container with preloading. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: custom container with preloading. 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 team wants to serve a large PyTorch model (3 GB) for online predictions with low latency. Which THREE actions should they take?
Quick Answer
The answer is to use a GPU accelerator, optimize the model with TorchScript or quantization, and deploy with a custom container that preloads the model. These three actions directly address the core challenge of serving a large PyTorch model for low-latency online predictions on Vertex AI: the GPU provides the raw compute speed, model optimization reduces the computational footprint and inference time, and preloading eliminates cold start delays by keeping the model in memory. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between actions that reduce prediction latency versus those that improve throughput or network latency—a common trap is confusing multiregion deployment (which reduces network round-trips) with actual inference speed. Remember the memory tip: "GPU, shrink, preload" to recall the three pillars of low-latency PyTorch inference on Vertex AI.
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 custom container that preloads the model into memory.
For online low-latency predictions with a large model, preloading the model into memory via a custom container (A) eliminates cold-start latency. GPU acceleration (C) significantly speeds up inference for large models. Optimizing with TorchScript or quantization (D) reduces model size and inference time. Batch prediction (B) is for offline batch processing, not online. Multi-region deployment (E) improves availability and global latency but does not directly reduce prediction latency for a single request.
Key principle: Custom container with preloading
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 custom container that preloads the model into memory.
Why this is correct
Preloading avoids loading model on each request, reducing latency.
Related concept
Custom container with preloading
- ✗
Use batch prediction instead of online prediction.
Why it's wrong here
Batch prediction is asynchronous, not real-time.
- ✓
Use a machine type with a GPU accelerator.
Why this is correct
GPU accelerates deep learning inference.
Related concept
Custom container with preloading
- ✓
Optimize the model using TorchScript or quantization.
Why this is correct
TorchScript and quantization reduce inference time and model size.
Related concept
Custom container with preloading
- ✗
Deploy in multiple regions with Cloud Load Balancing.
Why it's wrong here
Reduces network latency but not prediction latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may mistakenly think multi-region deployment or batch prediction reduce online prediction latency. Multi-region reduces network round-trip time but not inference time; batch prediction is not suitable for real-time serving.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Custom container with preloading
- GPU acceleration
- Model optimization (TorchScript/quantization)
- Online vs batch prediction
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
Custom container with preloading
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.
Review custom container with preloading, then practise related PMLE questions on the same topic to reinforce the concept.
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Custom container with preloading
What is the correct answer to this question?
The correct answer is: Use a custom container that preloads the model into memory. — For online low-latency predictions with a large model, preloading the model into memory via a custom container (A) eliminates cold-start latency. GPU acceleration (C) significantly speeds up inference for large models. Optimizing with TorchScript or quantization (D) reduces model size and inference time. Batch prediction (B) is for offline batch processing, not online. Multi-region deployment (E) improves availability and global latency but does not directly reduce prediction latency for a single request.
What should I do if I get this PMLE question wrong?
Review custom container with preloading, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Custom container with preloading
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team needs to serve a PyTorch model for production inference with strict latency requirements (p99 < 100ms). The model has dynamic control flow and uses custom kernels compiled with torch.jit. Which serving approach should they recommend?
medium- ✓ A.Build a custom container with PyTorch JIT and deploy it on Vertex AI Prediction.
- B.Convert the model to TensorFlow SavedModel and serve it on Vertex AI Prediction with TensorFlow Serving.
- C.Use Cloud Functions with a PyTorch wrapper to handle inference requests.
- D.Deploy the model on Vertex AI Prediction using the prebuilt PyTorch container.
Why A: Option A is correct because a custom container with PyTorch JIT allows full control over model execution, including dynamic control flow and custom kernels, and can be deployed on Vertex AI Prediction, which supports custom containers for low-latency inference. Option B is wrong because converting to TensorFlow SavedModel would lose PyTorch-specific features like custom JIT kernels. Option C is wrong because Cloud Functions have cold start latency and are not suited for low-latency production inference at scale. Option D is wrong because the prebuilt PyTorch container may not support custom JIT kernels or dynamic control flow optimally.
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.