Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which TWO strategies are effective for reducing latency in a generative AI chat application deployed on Vertex AI? (Select 2)
⚠ Common exam trap
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.
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
✓
Use streaming responses
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.
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 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.
- ✗
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.
- ✗
Use larger batch sizes
Why it's wrong here
Increases per-request latency.
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