Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO techniques can help reduce latency for a real-time generative AI application? (Choose two.)
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 to send tokens as generated.
Streaming and model quantization directly reduce response time. Batching is for offline, and more deploy replicas can increase throughput but not necessarily reduce latency for a single request. Prompt caching can help if prompts repeat, but not generally.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use streaming responses to send tokens as generated.
Why this is correct
Streaming eliminates waiting for the full output, reducing perceived latency.
- ✓
Quantize the model to a lower precision.
Why this is correct
Quantization reduces model size and compute, speeding up inference.
- ✗
Deploy more model replicas to handle load.
Why it's wrong here
More replicas improve throughput but do not reduce latency for a single request.
- ✗
Enable prompt caching for repeated queries.
Why it's wrong here
Caching helps only for identical prompts, not typical real-time use.
- ✗
Batch multiple user requests together.
Why it's wrong here
Batching increases latency for individual requests.
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