The answer is GPU or TPU requirements for model inference, along with latency and throughput needs and cost optimization, as the three primary factors for choosing a compute option for serving a deep learning model in production on Google Cloud. This is correct because deep learning inference workloads are computationally intensive, often requiring specialized hardware like GPUs or TPUs to achieve acceptable latency for real-time predictions, while batch serving may prioritize throughput on CPUs. On the Google Professional Machine Learning Engineer exam, this question tests your ability to balance hardware acceleration against operational constraints like autoscaling and regional availability, with a common trap being to overemphasize training hardware over serving-specific needs. A useful memory tip is to think of the "LGC" mnemonic: Latency, GPU/TPU, and Cost—these three pillars ensure your serving infrastructure aligns with both model performance and business requirements.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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.
Which THREE factors should be considered when choosing a compute option for serving a deep learning model in production on Google Cloud? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Integration with Vertex AI for model monitoring
A is correct because Vertex AI provides integrated model monitoring capabilities, including feature drift detection, prediction skew analysis, and outlier detection, which are essential for maintaining model performance in production. Without this integration, you would need to build custom monitoring pipelines, increasing operational complexity.
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.
✓
Integration with Vertex AI for model monitoring
Why this is correct
Monitoring integration is crucial for production.
Related concept
Read the scenario before looking for a memorised answer.
✓
Autoscaling capabilities to handle variable traffic
Why this is correct
Production needs to scale to meet demand.
Related concept
Read the scenario before looking for a memorised answer.
✓
GPU or TPU requirements for model inference
Why this is correct
Deep learning models often need accelerators for low latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
The programming language used for training
Why it's wrong here
Training language doesn't dictate serving options.
✗
The color of the team's logo
Why it's wrong here
Irrelevant.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might think the training language (D) matters for serving, but Google Cloud serving infrastructure is language-agnostic as long as the model is exported in a supported format, making this a common distractor.
Detailed technical explanation
How to think about this question
When selecting compute for serving, GPU/TPU requirements depend on model architecture and latency SLAs; for example, a large transformer model may need a TPU v4 pod for low-latency batch inference, while a smaller CNN can run on a single GPU. Autoscaling in Google Cloud uses managed instance groups with custom metrics (e.g., request queue depth) or Horizontal Pod Autoscaling on GKE, which must be configured to avoid cold starts during traffic spikes. Vertex AI Prediction integrates with Cloud Monitoring to trigger autoscaling based on request rate, but custom compute options require manual setup of these scaling policies.
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.
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.
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Integration with Vertex AI for model monitoring — A is correct because Vertex AI provides integrated model monitoring capabilities, including feature drift detection, prediction skew analysis, and outlier detection, which are essential for maintaining model performance in production. Without this integration, you would need to build custom monitoring pipelines, increasing operational complexity.
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.
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Question Discussion
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