AIF-C01 Fundamentals of AI and ML Practice Question
A deployed model on an Amazon SageMaker endpoint is experiencing high inference latency (average 500ms) during peak hours. The model is a deep neural network with 10 million parameters. The endpoint uses a single ml.c5.xlarge instance. The company wants to reduce latency to under 200ms without retraining or changing the model architecture. Which action should they take?
⚠ Common exam trap
AWS often tests the misconception that scaling or switching to GPU is the default solution for latency issues, but the trap here is that the question explicitly prohibits retraining or architecture changes, making model compilation via SageMaker Neo the only viable option that directly optimizes inference speed on the existing hardware.
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 SageMaker Neo to compile and optimize the model
SageMaker Neo compiles trained models into an optimized format for the target hardware, reducing inference latency without altering the model architecture. For a deep neural network with 10 million parameters on a CPU instance, Neo applies hardware-specific optimizations like operator fusion and memory layout tuning, which can significantly lower latency. This directly addresses the requirement to reduce latency from 500ms to under 200ms without retraining or changing the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable automatic scaling to add more instances
Why it's wrong here
Scaling adds more instances to handle traffic but does not reduce latency per request; it may even increase due to distribution overhead.
- ✗
Switch to a GPU-based instance type like ml.p2.xlarge
Why it's wrong here
GPU instances can accelerate deep learning inference, but the model may not be optimized for GPU and could be CPU-bound. Also, it may be more expensive.
- ✗
Deploy the model on a multi-model endpoint
Why it's wrong here
Multi-model endpoints are for serving multiple models on one endpoint, not for reducing latency of a single model.
- ✓
Use SageMaker Neo to compile and optimize the model
Why this is correct
SageMaker Neo optimizes models for target hardware, significantly reducing inference latency without changing the model.
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