- A
Enable response caching for frequent similar queries
Caching avoids model inference for duplicate or similar requests, reducing latency.
- B
Increase the max input token limit to capture more context
Why wrong: Larger context increases processing time, increasing latency.
- C
Use automatic scaling to add more replicas
Why wrong: Autoscaling helps handle load but does not reduce per-request latency; it maintains throughput.
- D
Deploy a smaller distilled version of the model
Smaller models have lower compute requirements and faster inference.
- E
Set the model to preemptible instances
Why wrong: Preemptible instances are cheaper but can be terminated, not a latency optimization.
Question 231 of 997
Reduce Latency for Online Prediction on Vertex AI
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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.
Which TWO actions can help reduce latency for an online prediction endpoint served by a large language model on Vertex AI? (Select TWO.)
Quick Answer
The answer is deploying a smaller distilled version of the model and enabling response caching. Distillation reduces the model’s parameter count and computational overhead, directly cutting inference time, while caching stores frequent responses so the endpoint avoids redundant processing for identical inputs. On the Google Cloud Generative AI Leader exam, this question tests your understanding of practical latency optimization for large language models on Vertex AI, often appearing as a multi-select trap where options like increasing batch size or relying on automatic scaling seem plausible but actually degrade or fail to directly address latency. A common mistake is confusing platform-managed optimizations, such as autoscaling, with actions you explicitly configure. Remember the memory tip: “Smaller model, smarter cache” — these two actions give you direct control over the two biggest latency bottlenecks: model size and repeated computation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable response caching for frequent similar queries
Option A is correct because enabling response caching for frequent similar queries reduces latency by serving cached predictions instead of recomputing them. This is particularly effective for large language models where inference is computationally expensive, as it avoids redundant processing for identical or semantically similar requests. Option D is correct because deploying a smaller distilled version of the model reduces the computational cost per inference, directly lowering latency. Distillation compresses the model while retaining most of its accuracy, making it faster to run predictions.
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.
- ✓
Enable response caching for frequent similar queries
Why this is correct
Caching avoids model inference for duplicate or similar requests, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the max input token limit to capture more context
Why it's wrong here
Larger context increases processing time, increasing latency.
- ✗
Use automatic scaling to add more replicas
Why it's wrong here
Autoscaling helps handle load but does not reduce per-request latency; it maintains throughput.
- ✓
Deploy a smaller distilled version of the model
Why this is correct
Smaller models have lower compute requirements and faster inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the model to preemptible instances
Why it's wrong here
Preemptible instances are cheaper but can be terminated, not a latency optimization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse scaling (Option C) with latency reduction, but scaling improves throughput under load, not per-request speed, while preemptible instances (Option E) are a cost-saving measure incompatible with online serving requirements.
Detailed technical explanation
How to think about this question
Response caching in Vertex AI leverages a cache key derived from the input text and model parameters; when a request matches a cached entry, the model skips inference entirely, returning the stored response in milliseconds. This is especially beneficial for LLMs with high inference costs, as it reduces GPU compute usage and tail latency for repeated queries, such as common customer support questions or code snippets.
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
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable response caching for frequent similar queries — Option A is correct because enabling response caching for frequent similar queries reduces latency by serving cached predictions instead of recomputing them. This is particularly effective for large language models where inference is computationally expensive, as it avoids redundant processing for identical or semantically similar requests. Option D is correct because deploying a smaller distilled version of the model reduces the computational cost per inference, directly lowering latency. Distillation compresses the model while retaining most of its accuracy, making it faster to run predictions.
What should I do if I get this Generative AI Leader 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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Last reviewed: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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