MLS-C01 AWS Glue job scheduling Practice Question
A startup is building a data pipeline that ingests data from multiple sources into an Amazon S3 data lake. The data includes CSV files from legacy systems, JSON from web APIs, and Avro from mobile apps. The data must be transformed into Parquet format and cataloged for querying with Amazon Athena. The pipeline must be serverless and minimize operational overhead. The team has decided to use AWS Glue for ETL and cataloging. However, they are concerned about the cost of running Glue jobs continuously. The data arrives in small batches every 10 minutes. Which approach should the team use to minimize cost while meeting the requirements?
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 scheduled Glue jobs to process the data every hour, consolidating multiple batches
Using scheduled Glue jobs every hour to process accumulated data reduces the number of job runs and associated costs, while still providing near-real-time processing (within the hour). Option A is wrong because Lambda functions have limited execution time and memory, making them unsuitable for large-scale transformations. Option B is wrong because Kinesis Data Firehose can directly deliver streaming data to S3, but it does not handle all source formats natively (e.g., CSV, Avro) and additional transformation may be needed. Option D is wrong because a single daily Glue job introduces too much latency for batch arrivals every 10 minutes.
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 AWS Lambda functions to transform each file upon arrival and store as Parquet
Why it's wrong here
Lambda has limitations on execution time and memory for large transformations.
- ✗
Use Amazon Kinesis Data Firehose to stream data directly into S3 and use Glue to catalog it
Why it's wrong here
Firehose can deliver to S3 but does not transform to Parquet for all source types.
- ✓
Use scheduled Glue jobs to process the data every hour, consolidating multiple batches
Why this is correct
Hourly batch processing balances cost and latency.
- ✗
Use a single daily Glue job to process all data at once
Why it's wrong here
Daily processing may not meet latency requirements of data arriving every 10 minutes.
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.