MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?
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
✓
Add a Catch rule in the Step Functions state machine to invoke a Lambda alert function
Step Functions supports error handling via Catch rules; a Catch on the training or evaluation task can transition to a Lambda function that sends an alert.
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 SQS dead-letter queue for failed training jobs
Why it's wrong here
An SQS dead-letter queue captures failed messages for later reprocessing; it does not itself send alerts. This suits durable retention and redrive of failed task invocations, whereas the stem requires an immediate notification when the evaluation state fails.
- ✓
Add a Catch rule in the Step Functions state machine to invoke a Lambda alert function
Why this is correct
A Catch rule on the evaluation state captures the failure and transitions to a Lambda task that sends the alert. This satisfies the stem's requirement to notify the team when evaluation fails, since Step Functions otherwise terminates the execution without invoking downstream alerting.
- ✗
Configure SageMaker training job to publish to SNS on failure
Why it's wrong here
SNS notification on training job failure alerts only when the SageMaker training job itself fails, but the stem's alert condition is evaluation failure, which occurs after training succeeds. This suits alerting on training errors, not downstream evaluation outcomes.
- ✗
Use EventBridge to monitor the training job status
Why it's wrong here
EventBridge monitoring the training job status cannot detect evaluation failure, since evaluation runs as a separate Step Functions state after training completes. EventBridge rules on SageMaker job state changes suit reacting to training lifecycle events, not post-training evaluation results.
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.