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 company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?
Configure auto-scaling with higher min and max instances
Auto-scaling handles traffic spikes.
Reduce the number of input features
Why wrong: Reducing features might degrade accuracy.
Switch from online to batch prediction
Why wrong: Batch prediction is not for real-time.
Use a larger machine type for the model
Why wrong: Larger machines may not address scaling dynamically.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Configure auto-scaling with higher min and max instances
Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Configure auto-scaling with higher min and max instances
Why this is correct
Auto-scaling handles traffic spikes.
Related concept
Read the scenario before looking for a memorised answer.
Reduce the number of input features
Why it's wrong here
Reducing features might degrade accuracy.
Switch from online to batch prediction
Why it's wrong here
Batch prediction is not for real-time.
Use a larger machine type for the model
Why it's wrong here
Larger machines may not address scaling dynamically.
Common exam traps
Google Cloud often tests the misconception that reducing features or using batch prediction is the primary way to reduce latency, but the real exam trap is that candidates overlook the need to maintain real-time capability and accuracy, and instead choose a solution that changes the model or prediction mode rather than scaling infrastructure.
Detailed technical explanation
Vertex AI Prediction uses a regional endpoint with an HTTP load balancer that distributes requests to model server containers. Auto-scaling adjusts the number of replicas based on CPU utilization or request count, and setting a higher min instance count ensures that the load balancer always has warm replicas ready, avoiding the 10–30 second cold-start penalty when new instances are provisioned. The max instance cap prevents runaway costs while still allowing elasticity during bursts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Configure auto-scaling with higher min and max instances — Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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