MLS-C01 Modeling Practice Question

A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model must respond within 100 milliseconds. The initial deployment uses a single ml.m5.large instance, but latency is too high. Which change should the engineer make to reduce latency?

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

✓

Switch to a compute-optimized instance like ml.c5.2xlarge.

A compute-optimized instance like ml.c5.2xlarge provides more CPU and memory, reducing inference latency. Option B is wrong because batch transform is for offline predictions, not real-time; it does not reduce latency for real-time inference. Option C is wrong because using a smaller instance (ml.t2.medium) reduces resources and would likely increase latency, not reduce it. Option D is wrong because multi-model endpoints share resources among models and can lead to contention, potentially increasing 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.

✓
Switch to a compute-optimized instance like ml.c5.2xlarge.
Why this is correct
Compute-optimized instances provide higher CPU performance, reducing prediction latency.
✗
Use batch transform instead of real-time endpoint.
Why it's wrong here
Batch transform is for asynchronous predictions on large datasets, not for real-time low-latency inference.
✗
Deploy to a single ml.t2.medium instance to reduce cost.
Why it's wrong here
A smaller instance would likely increase latency due to limited compute resources.
✗
Deploy the model on a multi-model endpoint.
Why it's wrong here
Multi-model endpoints are designed for hosting multiple models, not for reducing latency of a single model.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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