MLS-C01 Data Engineering Practice Question
A data engineer is building a data pipeline to process user clickstream data. The data arrives as JSON files in an S3 bucket. The pipeline must transform the JSON into Parquet format and partition by date and event type, then make the data available for Amazon Athena queries. The engineer needs a fully managed, serverless solution with minimal operational overhead. Which combination of AWS services should the engineer use?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that AWS Glue is the only serverless ETL option, but the trap here is that Lambda with S3 Event Notifications is a simpler, fully serverless alternative for file-based transformations when the workload fits within Lambda's constraints.
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 S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.
AWS Lambda triggered by S3 Event Notifications provides a fully serverless, event-driven architecture with minimal operational overhead for converting JSON to Parquet and partitioning by date and event type. Lambda can process each new JSON file as it arrives, perform the transformation in memory (using libraries like PyArrow or Pandas), and write the Parquet output to a partitioned S3 path, which Athena can then query directly. This approach avoids managing any clusters or job scheduling, aligning with the requirement for a fully managed, serverless solution.
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 Amazon EMR with Spark to read JSON, convert to Parquet, and partition, then query with Athena.
Why it's wrong here
EMR requires cluster management and is not serverless, increasing operational overhead.
- ✗
Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet, and write to a partitioned S3 location, then use Athena.
Why it's wrong here
AWS Glue ETL jobs, while serverless and fully managed for batch processing, require defining and managing job scripts, which increases operational overhead compared to a fully automated, event-driven ingestion and transformation service. This option is tempting because Glue is excellent for complex data transformations, cataloguing, and preparing data for analytics, making it a suitable choice for scheduled or ad-hoc batch ETL operations where customisation and Spark capabilities are paramount.
- ✓
Use S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.
Why this is correct
Lambda is serverless, cost-effective for per-file processing, and can partition output easily.
- ✗
Use Amazon Kinesis Firehose to ingest data and convert to Parquet, then write to S3, and query with Athena.
Why it's wrong here
Kinesis Firehose is for streaming data, not batch files; it cannot handle existing S3 objects.
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
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.