MLS-C01 Data Engineering Practice Question
A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The input data is located in an S3 bucket, and the team needs to transform the data before inference using a custom Python script. The transformation should run on a serverless infrastructure and must be triggered automatically when new data arrives in S3. Which combination of services should the team use?
⚠ Common exam trap
Test-takers frequently confuse batch-oriented services like Glue or SageMaker Processing with real-time event-driven needs, or assume Kinesis Firehose can directly invoke a SageMaker endpoint without an intermediate Lambda function.
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
✓
Use AWS Lambda functions triggered by S3 events to run the transformation, then invoke a SageMaker endpoint.
AWS Lambda functions can be triggered directly by S3 events (e.g., ObjectCreated) to run a custom Python transformation script on the incoming data, and then invoke a SageMaker endpoint for real-time inference. This combination meets the serverless infrastructure requirement and provides automatic, event-driven processing without managing any servers.
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 AWS Lambda functions triggered by S3 events to run the transformation, then invoke a SageMaker endpoint.
Why this is correct
Lambda provides serverless compute triggered by S3 events, and can call SageMaker endpoints.
- ✗
Use AWS Glue jobs triggered by S3 events.
Why it's wrong here
Glue is designed for ETL, not real-time inference pipelines.
- ✗
Use Amazon SageMaker Processing jobs triggered by S3 events.
Why it's wrong here
SageMaker Processing jobs run on provisioned instances, not serverless.
- ✗
Use Amazon Kinesis Data Firehose to transform data and deliver to SageMaker.
Why it's wrong here
Firehose is for streaming data, not suitable for S3-triggered processing.
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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