MLS-C01 Modeling Practice Question
A company is building a real-time fraud detection system using Amazon SageMaker. The model must have low latency (under 10ms) and high throughput (thousands of predictions per second). The team has trained a gradient boosting model using XGBoost. Which SageMaker inference option is MOST suitable?
⚠ Common exam trap
Many candidates confuse 'real-time' with 'serverless' or 'asynchronous', failing to recognize that serverless endpoints introduce cold-start latency and throughput limits that break the sub-10ms and high-throughput 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 a multi-model endpoint.
A multi-model endpoint (MME) on SageMaker is the most suitable option because it allows you to host multiple XGBoost models on a single endpoint, sharing the underlying instance to maximize throughput and minimize latency. MMEs keep models loaded in memory and route requests to the correct model with sub-10ms overhead, meeting the low-latency and high-throughput requirements for real-time fraud detection.
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 asynchronous inference.
Why it's wrong here
Asynchronous inference is for near-real-time, not sub-10ms latency.
- ✓
Deploy the model on a SageMaker real-time endpoint with a multi-model endpoint.
Why this is correct
Multi-model endpoints optimize cost and latency for high throughput.
- ✗
Deploy the model on a SageMaker serverless endpoint.
Why it's wrong here
Serverless endpoints may have cold start latency and throughput limitations.
- ✗
Use a batch transform job.
Why it's wrong here
Batch transform is for offline predictions, not real-time.
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