This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. 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 strategies are effective for reducing latency in a generative AI chat application deployed on Vertex AI? (Select 2)
Deploy on TPU instead of GPU
Why wrong: TPUs may not always be faster for generative models.
Use streaming responses
Reduces perceived latency.
Increase the max output tokens
Why wrong: Longer outputs increase latency.
Enable model quantization
Reduces model size and inference time.
Use larger batch sizes
Why wrong: Increases per-request latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use streaming responses
Option B is correct because streaming responses reduce perceived latency by sending tokens to the client as they are generated, rather than waiting for the full response. This leverages server-sent events (SSE) or chunked transfer encoding to deliver partial results immediately, improving user experience in chat applications.
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.
Deploy on TPU instead of GPU
Why it's wrong here
TPUs may not always be faster for generative models.
Use streaming responses
Why this is correct
Reduces perceived latency.
Related concept
Read the scenario before looking for a memorised answer.
Increase the max output tokens
Why it's wrong here
Longer outputs increase latency.
Enable model quantization
Why this is correct
Reduces model size and inference time.
Related concept
Read the scenario before looking for a memorised answer.
Use larger batch sizes
Why it's wrong here
Increases per-request latency.
Common exam traps
Google Cloud often tests the distinction between reducing actual latency (e.g., model optimization) versus reducing perceived latency (e.g., streaming), and candidates mistakenly choose options that increase throughput (like larger batch sizes) without realizing they harm per-request latency.
Trap categories for this question
Command / output trap
Longer outputs increase latency.
Detailed technical explanation
Streaming responses in Vertex AI use the gRPC or HTTP/2 streaming APIs, where the model emits tokens incrementally via a generator pattern. This reduces time-to-first-token (TTFT) to near-zero, but does not reduce total generation time; it only shifts the delivery schedule. In practice, for long responses, this can make the application feel 2-3x faster to users, even though the total compute is unchanged.
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Use streaming responses — Option B is correct because streaming responses reduce perceived latency by sending tokens to the client as they are generated, rather than waiting for the full response. This leverages server-sent events (SSE) or chunked transfer encoding to deliver partial results immediately, improving user experience in chat applications.
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 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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