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MLS-C01 Modeling Practice Question

A company wants to deploy a machine learning model that provides real-time inference with low latency. The model is a small ensemble of three tree-based models. Which Amazon SageMaker approach is most appropriate?

⚠ Common exam trap

Watch out — candidates often confuse 'real-time inference' with 'serverless' or 'batch processing,' assuming that serverless or Lambda are always cheaper or simpler, but they fail to account for cold-start latency and execution limits that break low-latency requirements.

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 a SageMaker real-time endpoint with a single inference container.

A SageMaker real-time endpoint with a single inference container is the most appropriate approach because it provides persistent, low-latency inference by keeping the model loaded in memory and handling requests synchronously. For a small ensemble of three tree-based models, a single container can host all models (e.g., using a custom inference script or a multi-model endpoint) and deliver sub-second response times, meeting the real-time requirement.

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 SageMaker real-time endpoint with a single inference container.

    Why this is correct

    Real-time endpoints provide low-latency inference.

  • Use a SageMaker batch transform job.

    Why it's wrong here

    Batch transform is for asynchronous, large-scale predictions, not real-time.

  • Use AWS Lambda with the model packaged in a layer.

    Why it's wrong here

    Lambda has limited memory and runtime, and is not optimal for model inference.

  • Use a SageMaker Serverless Inference endpoint.

    Why it's wrong here

    Serverless has cold starts that increase latency.

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