MLS-C01 Practice Question: Machine Learning Implementation and Operations
A financial services company is deploying a machine learning model for credit risk assessment. The model must have an inference latency under 200ms and must be able to handle up to 1000 transactions per second (TPS). The company wants to minimize costs. The model is a gradient boosting model implemented in XGBoost. Which SageMaker deployment option should the team choose?
⚠ Common exam trap
A common mix-up: candidates choose Serverless Inference (Option D) thinking it automatically scales to any load, but they overlook the cold start latency and concurrency limits that prevent it from meeting strict sub-200ms latency and 1000 TPS 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
✓
Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.
A SageMaker real-time endpoint with multiple instances behind a load balancer provides the sub-200ms inference latency and 1000 TPS throughput required for synchronous, low-latency predictions. XGBoost models are well-suited for real-time endpoints, and horizontal scaling with multiple instances ensures high availability and cost efficiency by matching capacity to demand.
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 transactions in batches.
Why it's wrong here
Batch Transform is for async batch processing, not real-time.
- ✗
Use SageMaker asynchronous inference for queued requests.
Why it's wrong here
Asynchronous inference has higher latency due to queuing.
- ✓
Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.
Why this is correct
Real-time endpoints provide sub-second latency and can scale to 1000 TPS.
- ✗
Use SageMaker Serverless Inference for automatic scaling.
Why it's wrong here
Serverless can have cold starts and may not handle 1000 TPS with low latency.
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