MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to host a model that performs real-time inference. The model receives around 100 requests per second with occasional spikes up to 500 requests per second. The current endpoint uses 2 ml.m5.large instances. During spikes, latency increases significantly, and some requests time out. What is the MOST cost-effective solution to handle the spikes without losing requests?
⚠ Common exam trap
Watch out — candidates often choose Option A (scaling up) thinking it simplifies management, but they overlook that vertical scaling (larger instance) does not inherently improve throughput under bursty traffic if the bottleneck is request handling concurrency, and it wastes cost during low load.
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 SageMaker managed scaling with a target tracking policy and add a buffer based on the average spike duration
SageMaker managed scaling with a target tracking policy automatically adjusts the number of instances based on a specified metric (e.g., invocation count or latency), and adding a buffer based on the average spike duration ensures that additional capacity is provisioned before the spike hits, preventing timeouts. This is the most cost-effective approach as it scales out during spikes and scales in during normal load, avoiding over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the instances with a single larger instance type, such as ml.m5.4xlarge
Why it's wrong here
A single large instance may still be overwhelmed and is more expensive per request.
- ✗
Use an Amazon SQS queue to buffer incoming requests and process them asynchronously
Why it's wrong here
Asynchronous processing adds latency and may not meet real-time requirements.
- ✗
Use AWS Lambda with a provisioned concurrency to handle the spikes
Why it's wrong here
Lambda is not directly integrated with SageMaker endpoints; it would require custom code.
- ✓
Configure SageMaker managed scaling with a target tracking policy and add a buffer based on the average spike duration
Why this is correct
Managed scaling with a buffer allows proactive scaling to handle spikes.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.