MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is deploying a machine learning model for real-time fraud detection using Amazon SageMaker. The model must have a p99 inference latency under 50ms. Which TWO actions should the ML team take to meet the latency requirement?
⚠ Common exam trap
Candidates often assume that using a GPU instance (like ml.g4dn.xlarge) alone guarantees low latency. However, without model optimization (e.g., via SageMaker Neo), the overhead from unoptimized kernels and framework runtime can still cause p99 latency to exceed 50ms. Neo compiles the model specifically for the target instance, reducing inference 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
✓
Use SageMaker Neo to compile and optimize the model for the target instance type.
SageMaker Neo compiles and optimizes trained models for specific hardware targets, reducing inference latency by up to 2x without sacrificing accuracy. By applying hardware-specific optimizations such as kernel fusion and memory layout tuning, Neo ensures the model runs efficiently on the chosen instance type, directly helping to achieve sub-50ms p99 latency.
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 multi-model endpoint to reduce cold starts.
Why it's wrong here
Multi-model endpoints reduce cold starts for many models but not per-model latency.
- ✓
Use SageMaker Neo to compile and optimize the model for the target instance type.
Why this is correct
Neo optimizes the model to run faster on specific hardware.
- ✗
Use SageMaker Batch Transform for near-real-time inference.
Why it's wrong here
Batch Transform is for asynchronous, not real-time, inference.
- ✗
Configure automatic scaling to add instances based on CPU utilization.
Why it's wrong here
Scaling improves throughput but not per-request latency.
- ✓
Select a GPU instance type such as ml.g4dn.xlarge.
Why this is correct
GPU instances accelerate inference for deep learning models.
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