MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The team has multiple models that need to be served, but usage patterns are unpredictable and traffic spikes occur several times a day. The team wants to minimize costs while maintaining low latency. Which THREE actions should the team take?
⚠ Common exam trap
Many exam-takers confuse provisioned concurrency (a Lambda feature) with SageMaker endpoint warm-up strategies, leading them to select Option A, which is not applicable to SageMaker inference endpoints.
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 SageMaker inference with Spot Instances to reduce cost.
Using Spot Instances for SageMaker inference can significantly reduce costs (up to 60-90% compared to On-Demand) while still providing the compute needed for real-time inference. Spot Instances are suitable when the workload can tolerate interruptions, and with SageMaker's managed Spot support, the endpoint can automatically fall back to On-Demand capacity if Spot capacity is reclaimed, ensuring availability during traffic spikes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable provisioned concurrency on the endpoint to reduce cold starts.
Why it's wrong here
Provisioned concurrency is a Lambda feature, not SageMaker.
- ✓
Use SageMaker inference with Spot Instances to reduce cost.
Why this is correct
Spot Instances are cheaper but can be interrupted; for cost savings, sometimes acceptable.
- ✓
Use a SageMaker multi-model endpoint to serve multiple models on the same instance.
Why this is correct
Multi-model endpoints share resources among models, reducing cost.
- ✓
Configure automatic scaling on the endpoint to handle traffic spikes.
Why this is correct
Automatic scaling adds or removes instances based on load.
- ✗
Use SageMaker Batch Transform for all inference requests.
Why it's wrong here
Batch Transform processes entire datasets asynchronously in a single job, so it cannot deliver the real-time, sub-second latency required for a live inference pipeline with unpredictable traffic spikes. This option is tempting because Batch Transform is cost-effective for large, offline batch predictions where latency is irrelevant, making it ideal for scheduled or ad-hoc bulk processing rather than streaming requests.
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