MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)
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
✓
AWS Step Functions
A solution: Amazon EventBridge detects S3 events (new data), invokes a Lambda function that checks model performance (e.g., via SageMaker Model Monitor or custom metrics), and then starts a Step Functions workflow if degradation is detected. The other services: SageMaker Pipelines could replace Step Functions but is not listed as an option; SageMaker Model Monitor can track performance but is not an event source; CloudWatch Logs is not directly involved in the trigger logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Step Functions
Why this is correct
Step Functions orchestrates the retraining pipeline.
- ✓
AWS Lambda
Why this is correct
Lambda can evaluate model performance metrics and decide to start the retraining.
- ✓
Amazon EventBridge
Why this is correct
EventBridge can listen for S3 events to trigger the workflow.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs is for log storage, not for triggering workflows based on new data.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry manages model versions but does not trigger retraining based on performance degradation.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Same concept, more angles
1 more way this is tested on MLA-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 wants to trigger a model retraining pipeline whenever new training data arrives in an S3 bucket. They also need to send a notification to a Slack channel when the retraining completes. Which TWO AWS services should they use to implement this event-driven workflow? (Select TWO.)
easy- A.Amazon SQS
- ✓ B.AWS Lambda
- C.AWS CloudTrail
- ✓ D.Amazon EventBridge
- E.SageMaker Model Registry
Why B: AWS Lambda is correct because it can be triggered directly by S3 events (e.g., s3:ObjectCreated) to invoke the model retraining pipeline. Amazon EventBridge is correct because it can capture completion events from the retraining pipeline (e.g., SageMaker training job state changes) and route them to a target like a Slack webhook via Lambda or SNS, enabling the notification workflow.
JA
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.