MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data science team is deploying a machine learning model using Amazon SageMaker. The model requires GPU inference and must handle variable traffic with low latency. Which TWO options should the team implement to meet these requirements? (Choose TWO.)
⚠ Common exam trap
A common mix-up: candidates confuse multi-model endpoints with batch transform jobs or think that Elastic Inference can substitute for a full GPU instance, but the question specifically requires GPU inference and low latency, which only GPU instances or multi-model endpoints with GPU instances can reliably provide.
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 multi-model endpoint with a GPU instance to serve multiple models.
A is correct because a SageMaker multi-model endpoint with a GPU instance allows you to host multiple models on a single endpoint, dynamically loading and unloading them based on traffic, while providing GPU acceleration for low-latency inference. This approach efficiently handles variable traffic patterns by scaling the endpoint and leveraging GPU compute for deep learning models.
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 multi-model endpoint with a GPU instance to serve multiple models.
Why this is correct
Correct: Multi-model endpoint on GPU provides GPU inference and efficient resource utilization for variable traffic.
- ✗
Deploy to a SageMaker real-time endpoint using a CPU instance and attach an Elastic Inference accelerator.
Why it's wrong here
Wrong: Elastic Inference accelerators have limited GPU capacity and may not meet all GPU requirements.
- ✗
Use AWS Lambda with an attached GPU function for inference.
Why it's wrong here
Wrong: AWS Lambda does not support GPU.
- ✗
Host the model on a SageMaker batch transform job with GPU instances.
Why it's wrong here
Wrong: Batch transform is for offline inference, not real-time low latency.
- ✓
Deploy the model to a SageMaker real-time endpoint using a GPU instance type.
Why this is correct
Correct: GPU instance provides GPU inference, and real-time endpoint offers low latency.
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