MLS-C01 Modeling Practice Question
A company runs an e-commerce platform on AWS. They have a SageMaker endpoint serving a product recommendation model. The model uses a custom container with a TensorFlow model. Recently, the endpoint has been returning high latency and occasional 504 errors during peak traffic. The data scientist observes that the model inference time is around 200 ms per request, but the endpoint is configured with a single ml.c5.large instance. The traffic spikes can reach 100 requests per second. The data scientist needs to reduce latency and eliminate 504 errors. Which course of action is most appropriate?
⚠ Common exam trap
Many exam-takers confuse performance bottlenecks with model optimization or cost-saving strategies, and incorrectly choose Elastic Inference or multi-model endpoints, which address different problems (GPU acceleration or multi-model hosting) rather than the core issue of insufficient compute capacity and lack of auto scaling.
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
✓
Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge
The endpoint is bottlenecked by both instance size and concurrency. With a single ml.c5.large instance handling 100 requests per second and a 200 ms inference time, the instance can only process about 5 requests per second (1000 ms / 200 ms = 5 requests per second per instance). Application Auto Scaling based on the 'InvocationsPerInstance' metric will add instances during traffic spikes, while upgrading to ml.c5.xlarge doubles compute capacity per instance, reducing latency and eliminating 504 errors caused by request queue overflow.
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 Amazon Elastic Inference to attach an EI accelerator to the endpoint instance
Why it's wrong here
EI reduces inference time per request but does not increase the number of requests the endpoint can handle concurrently.
- ✓
Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge
Why this is correct
Auto scaling adds instances to handle load; a larger instance reduces per-request latency.
- ✗
Switch to a multi-model endpoint to serve multiple models on the same instance
Why it's wrong here
Multi-model endpoints help with model loading but do not directly address capacity for a single model.
- ✗
Replace the SageMaker endpoint with an AWS Lambda function that loads the model from S3 and returns predictions
Why it's wrong here
Lambda has timeout limits and may not be suitable for complex model inference with 200 ms latency.
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Written by Johnson Ajibi, MSc IT Security
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