Generative AI Leader Fundamentals of Generative AI Practice Question
A machine learning engineer is building a text-to-image model using Vertex AI. They want to reduce inference latency. Which strategy is most effective?
⚠ Common exam trap
Many exam-takers confuse throughput optimization (batch processing) with latency reduction, or assume that more steps or higher resolution improve quality without considering the latency trade-off.
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 a smaller model variant
Using a smaller model variant directly reduces the number of parameters and computational operations required per inference pass, which lowers latency. In text-to-image models like Imagen or Stable Diffusion, the model size is the primary driver of forward-pass time, so a smaller variant (e.g., fewer layers or reduced latent dimensions) yields faster generation.
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 a larger image resolution
Why it's wrong here
Larger resolution increases computation.
- ✓
Use a smaller model variant
Why this is correct
Smaller models are faster.
- ✗
Enable batch processing
Why it's wrong here
Batch processing may increase latency per sample.
- ✗
Increase the number of inference steps
Why it's wrong here
More steps increase latency.
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