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Machine Learning Implementation and OperationsmediumMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

A company is deploying a machine learning model to production on Amazon SageMaker. The model requires low-latency inference (under 10 ms) for real-time predictions. The data scientist has trained a model using XGBoost and wants to minimize cost while meeting latency requirements. Which SageMaker hosting option should be used?

⚠ Common exam trap

A common mix-up: candidates confuse 'serverless' with 'low-latency' because serverless is cost-effective, but they overlook the cold-start penalty that makes it unsuitable for sub-10 ms inference; AWS often tests this by pairing a latency requirement with a cost-saving option to see if you prioritize performance constraints over cost optimization.

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 real-time endpoint with a single model

A real-time endpoint with a single model is the correct choice because it provides dedicated, always-on compute resources that can consistently achieve sub-10 ms inference latency for XGBoost models. SageMaker real-time endpoints keep instances warm and route requests directly to the model container, minimizing cold-start delays and network overhead, which is essential for low-latency requirements.

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 real-time endpoint with a single model

    Why this is correct

    Real-time endpoints provide low-latency inference.

  • Use a serverless inference endpoint

    Why it's wrong here

    Serverless has cold starts and may exceed latency requirements.

  • Use a real-time endpoint with multi-model hosting

    Why it's wrong here

    Multi-model may introduce latency due to model loading.

  • Use a batch transform job

    Why it's wrong here

    Batch transform is for offline, not real-time.

  • Use an asynchronous inference endpoint

    Why it's wrong here

    Asynchronous is for near-real-time with higher latency.

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Written by Johnson Ajibi, MSc IT Security

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.