MLS-C01 Modeling Practice Question
A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?
⚠ Common exam trap
The trap here is that candidates might choose Option C (cron job on EC2) because it seems simpler, but they overlook the operational burden of managing EC2 and the lack of native SageMaker integration for model lineage and automated deployment.
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
✓
Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.
Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.
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 AWS Step Functions to orchestrate retraining, but require a manual approval step.
Why it's wrong here
The manual approval step defeats the purpose of full automation.
- ✗
Use SageMaker training jobs manually triggered by the team each day.
Why it's wrong here
Manual triggering is not automated and requires human intervention.
- ✗
Use a cron job on an EC2 instance to run a training script.
Why it's wrong here
Requires managing EC2 instances and does not leverage SageMaker's managed training.
- ✓
Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.
Why this is correct
Combines SageMaker Pipelines for automated ML workflows with Lambda for scheduling, providing a fully automated solution.
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 |
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.