MLS-C01 Data Engineering Practice Question
A machine learning team is preparing a large dataset for training. The dataset consists of 10,000 CSV files, each about 100 MB, stored in Amazon S3. The team wants to transform the data using AWS Glue ETL jobs. The transformation involves filtering rows, adding new columns, and joining with a small reference table (100 KB). The team is concerned about job performance and cost. They currently have a Glue job with 10 DPU (Data Processing Units) and it takes about 2 hours to complete. The team wants to reduce the runtime and cost. Which approach should they take?
⚠ Common exam trap
AWS often tests the misconception that simply adding more compute resources (DPUs) will linearly improve performance, ignoring the critical impact of data format and partitioning on I/O and shuffle efficiency.
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
✓
Convert the CSV files to Parquet format and partition the data by a column.
Converting the CSV files to Parquet format and partitioning the data by a column significantly reduces the amount of data scanned and processed by AWS Glue. Parquet is a columnar storage format that allows Glue to read only the necessary columns, and partitioning enables predicate pushdown to skip irrelevant partitions. This directly reduces I/O and compute requirements, leading to faster job runtime and lower cost without increasing DPU count.
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 Athena to transform the data.
Why it's wrong here
Athena is for querying, not ETL transformations.
- ✗
Increase the number of DPUs to 100.
Why it's wrong here
More DPUs may improve speed but increase cost proportionally.
- ✗
Use Amazon EMR with Spot Instances instead of AWS Glue.
Why it's wrong here
EMR can be cheaper but requires more management.
- ✓
Convert the CSV files to Parquet format and partition the data by a column.
Why this is correct
Parquet reduces I/O and partitioning reduces data scanned.
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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