MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)
⚠ Common exam trap
Watch out — candidates often confuse AWS Glue as a compute trigger for ML retraining, but Glue is designed for batch ETL and lacks the event-driven, low-latency invocation capabilities required for this architecture.
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
✓
SageMaker (training jobs or pipelines)
Amazon SageMaker provides the training jobs and pipelines that execute the ML retraining workflow. Amazon EventBridge acts as the event bus that triggers retraining based on events such as new data arrival or model drift detection. AWS Lambda serves as the lightweight compute layer that can preprocess events, invoke SageMaker APIs, or orchestrate conditional logic before starting a training job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker (training jobs or pipelines)
Why this is correct
SageMaker executes the actual retraining.
- ✓
Amazon EventBridge
Why this is correct
EventBridge captures events such as new data arrival in S3.
- ✓
AWS Lambda
Why this is correct
Lambda can process the event and trigger the retraining pipeline.
- ✗
AWS Glue
Why it's wrong here
Glue is for ETL, not typically used for event-driven retraining triggers.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs is for monitoring, not part of the event-driven trigger.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.