PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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.
A healthcare startup is building a diagnostic tool that uses a deep learning model to classify medical images. The model is trained on TensorFlow and deployed on Vertex AI Prediction. The startup has strict latency requirements: predictions must return within 200 ms for 95% of requests. Current performance shows p95 latency of 350 ms. The team has already tried using a smaller model, but accuracy dropped below acceptable levels. The traffic pattern is spiky: low load during nights but bursts of 1000 requests per second during business hours. Currently, they use a single n1-highmem-8 VM with a GPU attached. They have a budget for additional resources but need to optimize cost. The model is about 500 MB and requires GPU for inference. Which course of action should they take to meet the latency requirement while managing costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a Vertex AI Prediction endpoint with an accelerator (GPU) and enable autoscaling (min 1, max 5 nodes)
Option C is correct because it leverages Vertex AI Prediction's autoscaling to handle spiky traffic efficiently, using GPU-accelerated endpoints that can scale from 1 to 5 nodes to meet the 200 ms p95 latency requirement. This approach minimizes cost during low-load periods while providing burst capacity for the 1000 requests per second peak, addressing both the latency and budget constraints without compromising model accuracy.
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.
✗
Upgrade to an n1-highmem-16 VM with a more powerful GPU
Why it's wrong here
Vertical scaling may still hit a ceiling during bursts, and cost increases linearly without solving scale.
✗
Switch to batch prediction using Vertex AI Batch Prediction and store results in a database for retrieval
Why it's wrong here
Batch prediction is not real-time and does not meet the 200 ms latency requirement.
✓
Create a Vertex AI Prediction endpoint with an accelerator (GPU) and enable autoscaling (min 1, max 5 nodes)
Why this is correct
Autoscaling with GPU provides low latency during bursts and cost efficiency by scaling down during low load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy the model as a Cloud Function using TensorFlow Serving
Why it's wrong here
Cloud Functions do not support GPUs, and cold starts would increase latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a single-node upgrade (Option A) thinking more power solves latency, but they overlook the need for horizontal scaling to handle spiky traffic, while Option B seems cost-effective but ignores the real-time requirement, and Option D appears serverless but fails due to GPU and timeout limitations.
Detailed technical explanation
How to think about this question
Vertex AI Prediction endpoints with GPUs use NVIDIA Tesla accelerators (e.g., T4 or V100) and support autoscaling based on CPU utilization or request queue depth, with a minimum of 1 node to handle baseline load and a maximum to cap costs. The model size of 500 MB fits within the 2 GB container memory limit for Vertex AI Prediction, and the GPU ensures inference completes in milliseconds per request, enabling the endpoint to handle bursts by distributing requests across multiple replicas. Autoscaling typically takes 30-60 seconds to spin up new nodes, so for sudden spikes, pre-warming or setting a higher minimum may be needed, but the spiky pattern described (business hours bursts) allows proactive scaling.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a Vertex AI Prediction endpoint with an accelerator (GPU) and enable autoscaling (min 1, max 5 nodes) — Option C is correct because it leverages Vertex AI Prediction's autoscaling to handle spiky traffic efficiently, using GPU-accelerated endpoints that can scale from 1 to 5 nodes to meet the 200 ms p95 latency requirement. This approach minimizes cost during low-load periods while providing burst capacity for the 1000 requests per second peak, addressing both the latency and budget constraints without compromising model accuracy.
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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