Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company has a generative AI model that is too slow for real-time inference. What architectural change would help?
⚠ Common exam trap
Google Cloud often tests the distinction between training optimization (distributed training) and inference optimization (quantization, pruning, hardware acceleration), so the trap here is that candidates confuse improving training speed with improving inference 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
✓
Apply model quantization and deploy on TPUs
Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which significantly decreases memory footprint and computation time, enabling faster inference. Deploying on TPUs (Tensor Processing Units) further accelerates matrix operations through specialized hardware, making this combination ideal for real-time latency requirements.
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 and deploy on TPUs
Why this is correct
Quantization reduces memory footprint and speeds up computation, and TPUs provide high throughput for trained models.
- ✗
Switch to a larger, more accurate model
Why it's wrong here
Larger models typically have higher latency, making the problem worse.
- ✗
Deploy the model on more powerful CPUs
Why it's wrong here
CPUs are less efficient than TPUs/GPUs for generative inference; the bottleneck may be architecture, not raw power.
- ✗
Use distributed training across multiple GPUs
Why it's wrong here
Distributed training improves model training speed, not inference latency.
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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.