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Deployment and Orchestration of ML WorkflowsmediumMultiple SelectObjective-mapped

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 ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

About these practice questions

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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.