PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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.
A healthcare startup is deploying a natural language processing (NLP) model for extracting medical entities from clinical notes. The model is a fine-tuned BERT model served on Vertex AI Prediction using a custom container. The team observes that prediction latency is around 500ms per request, but they need to handle up to 100 requests per second (QPS) with end-to-end latency under 200ms. The model currently runs on n1-standard-4 machines (4 vCPU, 15 GB memory). During load testing, CPU utilization reaches 90% and memory usage is 12 GB. The team is considering options to meet the requirements. Which action should they take?
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 with a GPU, such as n1-standard-4 with a NVIDIA Tesla T4 accelerator, and optimize the model with TensorRT.
Option A is correct because the bottleneck is CPU-bound inference (90% CPU utilization) with memory well within limits (12 GB of 15 GB). Adding a GPU (NVIDIA Tesla T4) and optimizing with TensorRT reduces per-request latency via hardware acceleration and graph optimizations, enabling sub-200ms inference at 100 QPS. This directly addresses the latency requirement without changing the machine family or scaling strategy.
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 with a GPU, such as n1-standard-4 with a NVIDIA Tesla T4 accelerator, and optimize the model with TensorRT.
Why this is correct
GPU accelerates BERT inference and TensorRT further optimizes latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch to n1-highmem-4 machines to provide more memory for the model.
Why it's wrong here
More memory does not reduce CPU-bound inference time.
✗
Deploy the model using TensorFlow Serving with CPU-only nodes and increase the number of replicas.
Why it's wrong here
Adding replicas helps throughput but per-request latency remains high.
✗
Move the model to Cloud Run with automatic scaling to handle the QPS.
Why it's wrong here
Cloud Run does not support GPU and has a 60s timeout, but latency still high.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling horizontally (more replicas or Cloud Run) solves latency problems, when the real issue is per-request compute bottleneck that requires hardware acceleration or model optimization.
Detailed technical explanation
How to think about this question
TensorRT optimizes trained models by fusing layers, quantizing weights (e.g., FP16 or INT8), and selecting kernel algorithms for the target GPU, which can reduce inference latency by 2-5x for transformer models like BERT. Vertex AI Prediction supports custom containers with GPU accelerators, and the n1-standard-4 with T4 provides 16 GB GPU memory and 65 TFLOPS (FP16), sufficient for BERT-base inference. In practice, a fine-tuned BERT model can achieve ~50-100ms latency on a T4 with TensorRT, meeting the 200ms target even under load.
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.
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: Use a machine type with a GPU, such as n1-standard-4 with a NVIDIA Tesla T4 accelerator, and optimize the model with TensorRT. — Option A is correct because the bottleneck is CPU-bound inference (90% CPU utilization) with memory well within limits (12 GB of 15 GB). Adding a GPU (NVIDIA Tesla T4) and optimizing with TensorRT reduces per-request latency via hardware acceleration and graph optimizations, enabling sub-200ms inference at 100 QPS. This directly addresses the latency requirement without changing the machine family or scaling strategy.
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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Question Discussion
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