Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company deployed a generative AI chatbot using Vertex AI PaLM API for customer support. Users report high latency (average 5 seconds per response). They need to reduce latency without significantly affecting response quality. Which design change should they prioritize?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing computational power (larger model) or batching always improves latency, when in fact these changes can increase per-request delay or degrade quality in interactive applications.
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
✓
Apply model quantization to the deployed model
Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases the computational load and memory footprint during inference. This directly lowers latency per request on the Vertex AI PaLM API while preserving most of the model's accuracy, making it the most effective single change for reducing response time without significantly degrading quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply model quantization to the deployed model
Why this is correct
Quantization reduces model size and speeds inference with minor accuracy trade-offs.
- ✗
Migrate the chatbot to run on edge devices
Why it's wrong here
Edge deployment introduces complexity and may not be suitable for cloud APIs.
- ✗
Increase the batch size of inference requests
Why it's wrong here
Batching improves throughput but not individual response latency.
- ✗
Switch to a larger, more powerful foundation model
Why it's wrong here
Larger models generally increase latency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.