The answer is to use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4. This is correct because the high CPU utilization and memory pressure indicate that the CPU is the bottleneck for LLM inference latency, not the model size or input volume; offloading the computationally intensive matrix operations to a GPU drastically reduces per-query latency and frees CPU resources for preprocessing and I/O. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of hardware acceleration trade-offs—a common trap is to scale horizontally with more replicas, which doesn’t address the root cause of CPU-bound inference. Remember the memory tip: when you see high CPU and near-capacity memory on small text inputs, think “GPU offload, not more nodes.”
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.
You are a Machine Learning Engineer at a financial services company. You have trained a large language model (LLM) using a custom container on Vertex AI Training. The model is used for sentiment analysis on financial news articles. You have deployed the model to a Vertex AI Endpoint for online prediction. However, during peak trading hours, users report high latency ( > 5 seconds) and occasional timeout errors. The model is deployed on n1-highmem-8 machines with 1 replica. You monitor the endpoint and see that CPU utilization is high ( > 90%) and memory is near capacity. The queries are relatively small text inputs. Which course of action should you take to reduce latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4.
Option B is correct because the high CPU utilization and memory pressure indicate that the CPU is the bottleneck for inference, not the model size or input volume. Switching to GPU accelerators like NVIDIA Tesla T4 offloads the computationally intensive matrix operations of the LLM to the GPU, drastically reducing per-query latency and freeing CPU resources for preprocessing and I/O. This directly addresses the root cause of >5-second latency during peak hours.
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.
✗
Deploy the model to multiple endpoints and use round-robin load balancing.
Why it's wrong here
This adds complexity and does not address the underlying compute bottleneck; latency per request remains high.
✓
Use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4.
Why this is correct
GPUs excel at matrix operations common in LLMs, dramatically reducing inference latency per request.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the machine type to n1-highmem-16 and keep 1 replica.
Why it's wrong here
Doubling RAM and vCPUs may provide marginal improvement but not as much as a GPU; also cost increases.
✗
Reduce the batch size for predictions to lower memory usage.
Why it's wrong here
The queries are small text inputs; batch size is not configurable per request in online prediction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling up CPU resources (vertical scaling) is the solution for high-latency inference, when in fact the correct approach for deep learning models is to offload computation to specialized hardware like GPUs or TPUs.
Detailed technical explanation
How to think about this question
LLMs rely heavily on transformer architectures that perform large matrix multiplications and attention mechanisms, which are highly parallelizable on GPUs via CUDA cores. The NVIDIA Tesla T4, with its 2560 CUDA cores and 16 GB of GDDR6 memory, can process these operations orders of magnitude faster than a CPU, even a high-core-count one like the n1-highmem-16. In Vertex AI Prediction, attaching a GPU accelerator automatically enables TensorFlow Serving or PyTorch Serve to use GPU kernels, reducing inference latency from seconds to milliseconds for small text inputs.
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.
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: Use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4. — Option B is correct because the high CPU utilization and memory pressure indicate that the CPU is the bottleneck for inference, not the model size or input volume. Switching to GPU accelerators like NVIDIA Tesla T4 offloads the computationally intensive matrix operations of the LLM to the GPU, drastically reducing per-query latency and freeing CPU resources for preprocessing and I/O. This directly addresses the root cause of >5-second latency during peak hours.
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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