MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?
⚠ Common exam trap
Test-takers frequently choose SageMaker Processing (Option A) because it is associated with ML, but they overlook that Glue ETL is the designated AWS service for serverless data preparation and transformation, especially when combining disparate data sources like RDS and S3.
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 Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.
AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.
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 a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.
Why it's wrong here
Processing jobs require manual resource management and are not serverless.
- ✗
Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.
Why it's wrong here
Athena is not designed for complex ETL transformations like text vectorization.
- ✓
Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.
Why this is correct
Glue provides a serverless Spark environment capable of handling both sources and complex transformations.
- ✗
Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.
Why it's wrong here
Kinesis Data Analytics is for streaming, not batch ETL of large datasets.
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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