MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO options are valid ways to reduce inference latency for a model deployed on a SageMaker real-time endpoint? (Select TWO.)
⚠ Common exam trap
Test-takers frequently confuse improving throughput (e.g., load balancing) with reducing per-request latency, or they mistakenly think increasing timeout values can speed up inference, when in fact it only extends the allowed wait time.
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
✓
Enable SageMaker Neo to compile the model for the target instance
SageMaker Neo compiles the trained model into an optimized binary for the specific target instance type, using hardware-specific instructions (e.g., Intel MKL-DNN, NVIDIA TensorRT) to reduce inference latency without sacrificing accuracy. This compilation optimizes the model graph and fuses operations, leading to faster execution on the deployed endpoint.
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 SageMaker batch transform instead of real-time endpoint
Why it's wrong here
Batch is not real-time.
- ✗
Deploy the model to multiple instances behind a load balancer
Why it's wrong here
Increases throughput but not per-request latency.
- ✓
Enable SageMaker Neo to compile the model for the target instance
Why this is correct
Neo optimizes model for faster inference.
- ✓
Use a GPU instance type for the endpoint
Why this is correct
GPUs accelerate deep learning inference.
- ✗
Increase the endpoint's invocation timeout
Why it's wrong here
Timeout does not affect speed.
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