MLS-C01 Data Engineering Practice Question
A company is building a data pipeline that ingests data from multiple sources into a centralized data lake on Amazon S3. The data must be transformed before it is available for analysis. The pipeline should be event-driven, automatically triggering transformation jobs when new data arrives. Which combination of AWS services should be used?
⚠ Common exam trap
Candidates often choose AWS Step Functions (Option D) because it is a powerful orchestrator, but they overlook that it is not directly event-driven from S3 without an intermediary like Lambda or EventBridge, and it does not perform the actual transformation.
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 S3 event notifications to invoke AWS Lambda, which triggers an AWS Glue job
Amazon S3 event notifications can be configured to invoke an AWS Lambda function when new objects are created in an S3 bucket. The Lambda function can then trigger an AWS Glue job to perform the necessary data transformations. This creates an event-driven, serverless pipeline that automatically processes data as it arrives, meeting the requirements for a centralized data lake on S3.
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 Kinesis Data Analytics for transformation
Why it's wrong here
Kinesis Data Analytics is for real-time streaming, not batch transformation.
- ✓
Amazon S3 event notifications to invoke AWS Lambda, which triggers an AWS Glue job
Why this is correct
S3 events trigger Lambda, which starts a Glue ETL job; this is event-driven and serverless.
- ✗
Amazon EMR with automatic scaling
Why it's wrong here
EMR requires cluster management and is less event-driven.
- ✗
AWS Step Functions to orchestrate the pipeline
Why it's wrong here
Step Functions can orchestrate but is not directly triggered by S3 events without Lambda.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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