easyMultiple Select
Two Key Actions to Reduce Vertex AI Endpoint Latency for Deep Learning Models
Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?
⚠ Common exam trap
Google Cloud often tests the misconception that adding more compute resources (larger machine types) always reduces latency, when in fact it can increase overhead and does not address the root cause of slow inference, which is model complexity.
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
✓
Optimize the model architecture to reduce size
Optimizing the model architecture to reduce size directly decreases the computational load during inference, which lowers prediction latency. Smaller models require fewer floating-point operations (FLOPs) per prediction, enabling faster response times on Vertex AI endpoints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of features
Why it's wrong here
More features increase computation.
- ✓
Optimize the model architecture to reduce size
Why this is correct
Smaller models predict faster.
- ✓
Use a custom prediction container with optimized dependencies
Why this is correct
Reduces overhead.
- ✗
Use a larger machine type with more vCPUs
Why it's wrong here
Not directly addressing latency; may help but not best.
- ✗
Set min replicas to 0 to save cost
Why it's wrong here
Cold starts increase latency.
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