DEA-C01 Data Ingestion and Transformation Practice Question
A company is ingesting real-time clickstream data into Amazon S3 using Amazon Kinesis Data Firehose. The data is semi-structured and the company wants to transform the data into Parquet format and partition it by year, month, day, and hour. Which TWO steps should be taken to achieve this? (Choose TWO.)
⚠ Common exam trap
AWS often tests the misconception that dynamic partitioning alone handles format conversion, but in reality, dynamic partitioning only manages the S3 prefix structure, while Parquet conversion requires a separate Lambda transformation or the use of Firehose's built-in Parquet conversion with a compatible input format.
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
✓
Enable dynamic partitioning in Kinesis Data Firehose and specify the partition keys as year, month, day, hour extracted from the data.
Kinesis Data Firehose's dynamic partitioning feature allows you to specify partition keys (year, month, day, hour) extracted from the incoming data, and Firehose will automatically create the corresponding S3 prefix structure (e.g., year=2024/month=01/day=15/hour=10/) during delivery. Option D is correct because to convert semi-structured data to Parquet format, you can attach an AWS Lambda function as a data transformation to Firehose, which converts each record to Parquet before delivery to S3. Option E is incorrect because while Kinesis Data Firehose does support converting data to Parquet format using a schema from the AWS Glue Data Catalog, this approach requires a pre-defined Glue schema and is less flexible for semi-structured data. Moreover, the question does not mention any existing Glue Data Catalog, and the Lambda transformation in option D is a more direct and customizable method for the transformation needed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up an Amazon S3 event notification to trigger an AWS Lambda function that partitions the data after delivery.
Why it's wrong here
Incorrect. Setting up an S3 event notification to trigger a Lambda function after delivery would add latency and complexity. Dynamic partitioning can be done during delivery, not after.
- ✓
Enable dynamic partitioning in Kinesis Data Firehose and specify the partition keys as year, month, day, hour extracted from the data.
Why this is correct
Correct. Dynamic partitioning extracts partition keys from the data and creates S3 prefixes accordingly.
- ✗
Use an AWS Glue Crawler to infer the schema and automatically partition the data in S3.
Why it's wrong here
Incorrect. An AWS Glue Crawler is used for schema inference and cataloging, not for real-time partitioning during ingestion.
- ✓
Create an AWS Lambda function that transforms incoming records to Parquet and attach it to the Firehose delivery stream as a data transformation.
Why this is correct
Correct. A Lambda function attached as a data transformation can convert each record to Parquet format before Firehose delivers it to S3.
- ✗
Configure Kinesis Data Firehose to convert the data to Parquet format using a schema from the AWS Glue Data Catalog.
Why it's wrong here
Kinesis Data Firehose can convert incoming data to Parquet using a schema from the Glue Data Catalog, but this action only handles format conversion; it does not create the required partitioning by year, month, day, and hour. Partitioning in Firehose requires configuring dynamic partitioning with custom prefix expressions for S3 destination paths. This option is tempting because Glue schemas provide a straightforward way to define the structure needed for Parquet conversion, and it would be correct if only format conversion were needed without custom time-based partitioning.
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.