- A
Amazon SQS queue to store S3 events and a cron job to poll and start training
Why wrong: Cron jobs are not event-driven and introduce latency.
- B
Use SageMaker Pipelines with a schedule to check for new data every hour
Why wrong: Scheduled checks are less efficient than event-driven triggers.
- C
Amazon S3 event notification to directly start a SageMaker training job
Why wrong: S3 cannot directly start a SageMaker job; it needs a Lambda intermediary.
- D
Amazon CloudWatch Events to run an AWS Step Functions state machine that starts a SageMaker training job
Why wrong: Step Functions adds complexity; direct Lambda is simpler.
- E
Amazon CloudWatch Events to invoke an AWS Lambda function that starts a SageMaker training job
CloudWatch Events can capture S3 events and invoke Lambda to start training.
Building an Event-Driven Retraining Pipeline with SageMaker, S3, and Lambda
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option E is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Step Functions adds complexity; direct Lambda is simpler.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, S3 event notifications are sent as JSON payloads to EventBridge (formerly CloudWatch Events) via the default event bus. The Lambda function receives the event, extracts the bucket name and object key, and uses the SageMaker CreateTrainingJob API with the updated data location. A subtle behavior is that S3 event notifications are typically delivered within seconds but are 'best-effort' — for critical pipelines, consider using S3 Event Notifications with SQS as a dead-letter queue to handle failures. In real-world scenarios, this pattern is often combined with SageMaker Processing jobs for data validation before training.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon CloudWatch Events to invoke an AWS Lambda function that starts a SageMaker training job — Option E is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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