MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.c5.xlarge instance. The company wants to reduce costs without affecting performance. The current traffic pattern shows a daily peak of 500 requests per second for 2 hours, and the rest of the day sees fewer than 50 requests per second. The model has a cold start time of about 30 seconds. What should the company do?
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 an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.
Configuring an auto scaling policy that scales down during low traffic reduces costs, and keeping a minimum of 1 instance avoids cold starts during low traffic, ensuring low latency. Option A is incorrect because serverless endpoints have cold starts and may not handle the peak of 500 TPS. Option C is wrong because a single instance may not handle the peak traffic, causing latency. Option D is wrong because Batch Transform is for batch predictions, not real-time inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a serverless inference endpoint.
Why it's wrong here
Serverless has cold start and max concurrency limits.
- ✓
Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.
Why this is correct
Auto scaling reduces instances during low traffic, and minimum instance prevents cold starts.
- ✗
Use a single ml.c5.xlarge instance and rely on it.
Why it's wrong here
Single instance may not handle peak load.
- ✗
Use SageMaker Batch Transform for all predictions.
Why it's wrong here
Batch Transform is for offline, not real-time.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company has deployed a machine learning model on Amazon SageMaker for real-time inference. The endpoint uses a single ml.c5.xlarge instance. Recently, the traffic has increased, and the endpoint is returning HTTP 503 (Service Unavailable) errors during peak hours. The CloudWatch metrics show that the CPU utilization is consistently above 90% during peak times, and the Invocations metric shows that requests are being throttled. The data science team has already optimized the model to reduce inference time by 20%, but the errors persist. The company needs to resolve the issue without increasing costs significantly. Which course of action should be taken?
hard- A.Change the instance type to a larger size, such as ml.c5.2xlarge
- B.Switch to batch transform to process requests in batches
- C.Use spot instances to reduce costs and add more instances
- ✓ D.Configure auto-scaling for the endpoint to add instances based on CPU utilization
Why D: Configuring auto-scaling for the endpoint based on CPU utilization dynamically adjusts the number of instances to handle increased traffic, reducing HTTP 503 errors without incurring high costs during low traffic. Option A is wrong because upgrading to a larger instance type (e.g., ml.c5.2xlarge) would increase costs even during low-traffic periods, which does not align with the goal of minimizing cost increases. Option B is wrong because batch transform is designed for offline, asynchronous processing, not real-time inference as required here. Option C is wrong because spot instances can be interrupted and reclaimed by AWS, leading to potential service disruptions, and merely adding more instances without scaling logic does not solve the capacity issue efficiently.
JA
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.