Question 282 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is deploying a machine learning model using SageMaker and wants to automate the retraining pipeline. The training data is updated daily in an S3 bucket. Which combination of AWS services should the data scientist use to trigger a new training job when new data arrives?
⚠ Common exam trap
AWS often tests the misconception that S3 event notifications can directly invoke SageMaker actions, but in reality, S3 events can only trigger Lambda, SQS, SNS, or EventBridge — a middleman service is always required to call the SageMaker API.
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
✓
Amazon CloudWatch Events to invoke an AWS Lambda function that starts a SageMaker training job
Amazon S3 event notifications can be sent to Amazon CloudWatch Events (via Amazon EventBridge), which then triggers an AWS Lambda function. The Lambda function contains code to start a SageMaker training job using the boto3 SDK. This serverless architecture provides a fully automated, event-driven pipeline that responds immediately when new data arrives in the S3 bucket, without polling or manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SQS queue to store S3 events and a cron job to poll and start training
Why it's wrong here
Cron jobs are not event-driven and introduce latency.
- ✗
Use SageMaker Pipelines with a schedule to check for new data every hour
Why it's wrong here
Scheduled checks are less efficient than event-driven triggers.
- ✗
Amazon S3 event notification to directly start a SageMaker training job
Why it's wrong here
S3 cannot directly start a SageMaker job; it needs a Lambda intermediary.
- ✗
Amazon CloudWatch Events to run an AWS Step Functions state machine that starts a SageMaker training job
Why it's wrong here
Amazon CloudWatch Events cannot natively respond to S3 object creation events; it schedules tasks or reacts to service-specific events, not S3 PUT events. This means it cannot trigger a training job immediately when new data arrives. The option is tempting because AWS Step Functions excels at orchestrating retraining pipelines, and CloudWatch Events is often used for scheduled automation. In a scenario requiring retraining on a fixed daily schedule rather than event-driven triggering, this combination would be correct.
- ✓
Amazon CloudWatch Events to invoke an AWS Lambda function that starts a SageMaker training job
Why this is correct
CloudWatch Events can capture S3 events and invoke Lambda to start training.
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 |
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Last reviewed: Jul 4, 2026
This MLS-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 MLS-C01 exam.
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