- A
Set up an Amazon S3 event notification to trigger an AWS Lambda function that partitions the data after delivery.
Why wrong: 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.
- B
Enable dynamic partitioning in Kinesis Data Firehose and specify the partition keys as year, month, day, hour extracted from the data.
Correct. Dynamic partitioning extracts partition keys from the data and creates S3 prefixes accordingly.
- C
Use an AWS Glue Crawler to infer the schema and automatically partition the data in S3.
Why wrong: Incorrect. An AWS Glue Crawler is used for schema inference and cataloging, not for real-time partitioning during ingestion.
- D
Create an AWS Lambda function that transforms incoming records to Parquet and attach it to the Firehose delivery stream as a data transformation.
Correct. A Lambda function attached as a data transformation can convert each record to Parquet format before Firehose delivers it to S3.
- E
Configure Kinesis Data Firehose to convert the data to Parquet format using a schema from the AWS Glue Data Catalog.
Why wrong: Incorrect. While Firehose can convert to Parquet using a Glue Data Catalog schema, this requires a pre-existing schema and is less flexible for semi-structured data. The more suitable approach for this scenario is to use a Lambda transformation as in option D.
How to Enable Dynamic Partitioning and Parquet Conversion in Kinesis Firehose
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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
Incorrect. While Firehose can convert to Parquet using a Glue Data Catalog schema, this requires a pre-existing schema and is less flexible for semi-structured data. The more suitable approach for this scenario is to use a Lambda transformation as in option D.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Scenario analysis trap
Incorrect. While Firehose can convert to Parquet using a Glue Data Catalog schema, this requires a pre-existing schema and is less flexible for semi-structured data. The more suitable approach for this scenario is to use a Lambda transformation as in option D.
Detailed technical explanation
How to think about this question
Dynamic partitioning in Firehose uses JQ expressions or inline parsing to extract partition keys from records, and the resulting S3 prefix is built using custom prefix expressions like 'year=!{partitionKeyFromQuery:year}/'. The Lambda transformation for Parquet conversion must output records in the Parquet format using libraries like PyArrow or fastparquet, and the transformed data must be returned as a base64-encoded blob within the Firehose response contract. A real-world scenario is ingesting JSON clickstream events where each event has a timestamp; you extract year, month, day, hour from that timestamp for partitioning and convert the JSON to Parquet for efficient querying with Athena or Redshift Spectrum.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable dynamic partitioning in Kinesis Data Firehose and specify the partition keys as year, month, day, hour extracted from the data. — Option B is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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