MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company runs a real-time fraud detection model on a SageMaker endpoint. The model is a TensorFlow neural network trained on transactional data. The endpoint uses a single ml.p3.2xlarge instance. Recently, the application’s latency has increased from 50ms to 500ms on average. The CloudWatch metrics show that CPU utilization is at 90%, GPU utilization is at 30%, and memory utilization is at 40%. The number of requests per second has remained stable. The ML team suspects the model is not fully utilizing the GPU. What action should the team take to reduce latency without changing the instance type?
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 the model for the target instance
SageMaker Neo compiles the model to optimize inference for the target hardware, improving GPU utilization and reducing latency. Option A is incorrect because SageMaker Batch Transform is for offline inference, not real-time requests. Option B is incorrect because switching to a CPU-based instance (ml.c5.large) would not leverage the GPU and could increase latency. Option D is incorrect because adding more instances improves throughput, not per-request latency, and does not address GPU underutilization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to SageMaker Batch Transform to process requests in batches
Why it's wrong here
Not for real-time inference.
- ✗
Change the endpoint to a compute-optimized instance like ml.c5.large
Why it's wrong here
Would reduce GPU utilization further.
- ✓
Use SageMaker Neo to compile the model for the target instance
Why this is correct
Neo optimizes model to better utilize GPU.
- ✗
Increase the number of instances behind the endpoint and use a load balancer
Why it's wrong here
Increases throughput but not per-request latency.
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