DEA-C01 Data Ingestion and Transformation Practice Question
A social media company ingests user activity data from multiple sources into Amazon S3. The data is in JSON format and includes fields: user_id, activity_type, timestamp, and metadata. The company wants to transform this data into a columnar format (Parquet) partitioned by date and activity_type for efficient querying with Amazon Athena. The pipeline must handle data that arrives up to 3 days late. Currently, a daily AWS Glue ETL job scans the entire S3 bucket for new files, transforms them, and writes to a separate output bucket. The job is taking longer as data volume grows, and the team wants to reduce processing time and cost. What should the engineer do?
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
✓
Set up S3 event notifications to invoke an AWS Lambda function that triggers a Glue job for each new object, passing the object key so the job processes only that file.
Using S3 event notifications with Lambda to trigger a Glue job for each new file allows incremental processing, reducing the time and cost of scanning the entire S3 bucket. Option A (increasing DPUs) does not address the root cause of scanning all files. Option B (partition projection) helps with query performance but not with the transformation process. Option C (replacing Glue with EMR) adds operational overhead and is not necessary for this use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of DPUs for the Glue job to process data faster.
Why it's wrong here
This increases cost and may not scale linearly; still scans all files.
- ✗
Use AWS Glue partition projection and schema inference to reduce scan time.
Why it's wrong here
Partition projection helps Athena queries but does not reduce Glue job processing time.
- ✗
Replace AWS Glue with Amazon EMR and use Spark to process data in parallel.
Why it's wrong here
EMR requires cluster management and is overkill for this use case.
- ✓
Set up S3 event notifications to invoke an AWS Lambda function that triggers a Glue job for each new object, passing the object key so the job processes only that file.
Why this is correct
This enables incremental processing, reduces scan time, and is cost-effective.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-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 DEA-C01 exam.