MLA-C01 Data Preparation for Machine Learning Practice Question
A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?
⚠ Common exam trap
Watch out — candidates often confuse 'file format' with 'query engine' (e.g., Hive) or choose a format like JSON that is human-readable but inefficient for analytics, missing that Parquet is the industry standard for performance and cost in data lakes.
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 DynamicFrame to repartition the data and write it as Parquet.
AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.
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 convert the data to JSON format and store it in S3.
Why it's wrong here
Athena is a query service, not a data transformation service.
- ✓
Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.
Why this is correct
DynamicFrame supports efficient partitioning and columnar format conversion.
- ✗
Use AWS Glue to convert the data to Apache Hive format.
Why it's wrong here
Hive format is not a standard file format; Parquet is columnar and efficient.
- ✗
Use Apache Spark DataFrame to write the data as CSV with Snappy compression.
Why it's wrong here
CSV is not columnar and does not optimize query performance as well as Parquet.
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 MLA-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 MLA-C01 exam.