The answer is to apply feature selection to reduce the number of input features. This is correct because the latency spike during peak hours stems from the large feature set increasing the computational load for both preprocessing and inference in the scikit-learn model; reducing features directly lowers the per-request processing time without sacrificing accuracy if the retained features preserve predictive power. On the Google Professional Data Engineer exam, this scenario tests your understanding that latency optimization on Vertex AI often requires addressing the model’s computational bottleneck rather than simply scaling resources—a common trap is choosing autoscaling or machine type upgrades, which only mask the symptom. Remember the memory tip: “Fewer features, faster inferences” — when you see a scikit-learn model with a large feature set and latency issues, think feature selection first.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services company deploys a regression model to predict loan default risk. The model is served using Vertex AI Endpoints with autoscaling. After deployment, latency increases significantly during peak hours, causing timeouts. The model uses scikit-learn and has a large feature set. Which action should the team take to reduce latency while maintaining prediction accuracy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply feature selection to reduce the number of input features.
Option D is correct because the latency spike is caused by the large feature set, which increases the time for preprocessing and inference in the scikit-learn model. Reducing the number of input features via feature selection directly decreases the computational load per request, lowering latency without sacrificing accuracy if the selected features retain predictive power. This addresses the root cause, unlike scaling or resource changes that only mask the symptom.
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.
✗
Switch to batch prediction for all requests.
Why it's wrong here
Batch prediction is not appropriate for real-time serving.
✗
Increase the minimum number of replicas in the endpoint to handle peak load.
Why it's wrong here
This addresses capacity but not per-request latency.
✗
Increase the memory allocation for the serving container.
Why it's wrong here
More memory may not reduce computation time.
✓
Apply feature selection to reduce the number of input features.
Why this is correct
Reducing features decreases model size and inference time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling solutions (increasing replicas or memory) with performance optimization, but the question specifically asks for reducing latency per request, which requires addressing the computational bottleneck—feature reduction—rather than adding more resources.
Detailed technical explanation
How to think about this question
In scikit-learn models, the inference time scales linearly with the number of features because each feature requires a multiplication and addition operation in the decision function (e.g., for linear models or tree splits). Feature selection techniques like mutual information or recursive feature elimination can reduce the feature count by 50-90% while retaining 95%+ of the model's predictive power, directly cutting latency. In Vertex AI, the model container's CPU utilization is the primary driver of autoscaling decisions, so reducing features lowers CPU time per request, allowing the existing replicas to handle more requests without triggering timeouts.
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: Apply feature selection to reduce the number of input features. — Option D is correct because the latency spike is caused by the large feature set, which increases the time for preprocessing and inference in the scikit-learn model. Reducing the number of input features via feature selection directly decreases the computational load per request, lowering latency without sacrificing accuracy if the selected features retain predictive power. This addresses the root cause, unlike scaling or resource changes that only mask the symptom.
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.
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 →
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 company deploys a scikit-learn model on Vertex AI for online predictions. The model is packaged in a custom container with all dependencies. Users report high latency (over 5 seconds) for predictions. The model size is 2 GB. What is the most likely cause of the high latency?
easy
A.Using online predictions instead of batch prediction
B.Not enabling GPU acceleration
✓ C.Using a custom container with a large unoptimized model
D.Using a small machine type (e.g., n1-standard-2)
Why C: Option C is correct because a 2 GB model loaded into a custom container without optimization (e.g., quantization, pruning, or ONNX conversion) will cause significant cold-start latency and per-request loading overhead. Vertex AI online predictions require the model to be loaded into memory for each request or container instance; a large, unoptimized model increases both loading time and inference time, easily exceeding 5 seconds.
Last reviewed: Jun 11, 2026
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