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MLS-C01 Modeling Practice Question

A startup is deploying a machine learning model for real-time recommendation on Amazon SageMaker. The model is a TensorFlow model (1 GB) and the endpoint uses a single ml.c5.2xlarge instance. The inference latency is currently 500 ms per request. The startup expects traffic to increase 10x in the next month. They want to maintain latency under 500 ms. What is the most cost-effective solution?

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

Set up auto-scaling for the endpoint based on average latency or request count

Auto-scaling dynamically adds instances based on demand, handling a 10x increase in traffic while maintaining latency under 500 ms. This is more cost-effective than over-provisioning a larger instance (D) or switching to an expensive GPU instance (B). SageMaker Batch Transform (A) is not suitable for real-time inference.

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 SageMaker Batch Transform to process requests in batches

    Why it's wrong here

    Batch Transform is for offline inference, not real-time.

  • Switch to a GPU instance type for faster inference

    Why it's wrong here

    GPU instances are more expensive and may not be needed for this model size.

  • Set up auto-scaling for the endpoint based on average latency or request count

    Why this is correct

    Auto-scaling adds capacity dynamically, handling traffic spikes cost-effectively.

  • Upgrade to a larger CPU instance type, such as ml.c5.4xlarge

    Why it's wrong here

    A larger instance is more expensive and may not be necessary if auto-scaling can distribute load.

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