MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select THREE.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing batch size always reduces latency, but for real-time endpoints, larger batches increase per-request processing time, making it a throughput optimization, not a latency reduction technique.
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.
A is correct because SageMaker Neo compiles the trained model into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia), using Apache TVM to fuse operations and prune unused computations. This reduces inference latency by up to 2x without requiring code changes, making it a direct latency-reduction measure for real-time 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.
- ✓
Enable SageMaker Neo to compile the model.
Why this is correct
Neo optimizes models for target hardware, reducing latency.
- ✗
Increase the batch size for inference.
Why it's wrong here
Larger batch sizes increase latency per request, though throughput may improve.
- ✓
Use GPU instances for inference.
Why this is correct
GPUs accelerate deep learning inference.
- ✓
Reduce the input data size (e.g., lower resolution images).
Why this is correct
Smaller inputs reduce computation time.
- ✗
Use a multi-model endpoint to share the instance.
Why it's wrong here
Multi-model endpoints can add latency when loading models.
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