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.)
⚠ Common exam trap
MLA-C01 often tests the confusion between orchestration services (Step Functions), compute/glue logic (Lambda), and event routing (EventBridge), catching candidates who include Model Registry or CloudWatch Logs as part of the trigger/evaluate/orchestrate trio.
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
Amazon EventBridge (C) is the correct event-routing service to detect the arrival of new data (e.g., an S3 PutObject event) and trigger the pipeline, since it can match events and invoke targets such as Step Functions. AWS Step Functions (A) is correct because it orchestrates the multi-step ML retraining workflow, including the conditional logic that checks whether model performance has degraded below the threshold before proceeding. AWS Lambda (B) is correct because it provides the serverless compute to evaluate the performance metric against the threshold and return a decision that Step Functions uses in its Choice state to branch into retraining or stop. Amazon CloudWatch Logs (D) is not correct because it is a logging/monitoring service, not an event trigger or orchestration component for this pipeline. SageMaker Model Registry (E) is not correct because, while it tracks model versions and approval status, it does not itself trigger retraining based on incoming data or performance degradation.
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 provides the orchestration layer, chaining the data-arrival event, the degradation check and the retraining workflow into one state machine, which satisfies the requirement to trigger retraining conditionally rather than on every new object.
- ✓
AWS Lambda
Why this is correct
AWS Lambda supplies the serverless compute that evaluates the performance metric against the threshold and decides whether to start the Step Functions state machine. It satisfies the stem's conditional trigger requirement, since Step Functions alone cannot inspect model metrics or respond to new data arriving in the pipeline.
- ✓
Amazon EventBridge
Why this is correct
Amazon EventBridge detects new data arriving in S3 and routes events to Step Functions, triggering the retraining pipeline. It provides the event-driven trigger mechanism, while a separate service must evaluate model performance against the degradation threshold.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs stores and queries log events; it cannot itself evaluate a performance threshold or invoke Step Functions. It would be correct for centralising pipeline logs or building metric filters, but the trigger decision needs CloudWatch alarms or EventBridge rules.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry versions and approves models; it stores metadata and approval status rather than evaluating live performance metrics. It is correct for governing model lineage and promotion, but degradation detection requires CloudWatch alarms on the endpoint's published metrics.
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
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.