- A
Use Amazon Kinesis Data Firehose with dynamic partitioning
Firehose can dynamically partition data based on the timestamp and deliver to S3 partitioned prefixes.
- B
Use AWS Glue crawlers to infer schema and create partitions
Why wrong: Glue crawlers create table metadata, they do not physically partition data in S3.
- C
Use AWS Lambda to process each object and copy to the appropriate prefix
Why wrong: Lambda would require custom code and additional cost.
- D
Use Amazon Athena to create partitions on the existing data
Why wrong: Athena is for querying, not for moving data into partitions.
Question 445 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 data engineer is designing a pipeline that ingests JSON logs from an application into Amazon S3. The logs contain a timestamp field. The pipeline must partition the data by date in S3 (e.g., year=2024/month=10/day=01). Which approach minimizes transformation effort?
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 Amazon Kinesis Data Firehose with dynamic partitioning
Amazon Kinesis Data Firehose with dynamic partitioning can automatically partition incoming JSON data based on the timestamp field without requiring custom transformation code. It evaluates the timestamp using a JQ expression or inline parsing, then writes records directly to S3 prefixes like year=2024/month=10/day=01. This minimizes transformation effort because the partitioning logic is configured declaratively in the Firehose delivery stream, eliminating the need for Lambda functions or post-ingestion processing.
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.
- ✓
Use Amazon Kinesis Data Firehose with dynamic partitioning
Why this is correct
Firehose can dynamically partition data based on the timestamp and deliver to S3 partitioned prefixes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Glue crawlers to infer schema and create partitions
Why it's wrong here
Glue crawlers create table metadata, they do not physically partition data in S3.
- ✗
Use AWS Lambda to process each object and copy to the appropriate prefix
Why it's wrong here
Lambda would require custom code and additional cost.
- ✗
Use Amazon Athena to create partitions on the existing data
Why it's wrong here
Athena is for querying, not for moving data into partitions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse metadata partitioning (e.g., using Glue crawlers or Athena) with physical partitioning in S3, assuming that catalog operations alone reorganize the data, when in fact only ingestion-time partitioning (like Firehose dynamic partitioning) creates the folder structure without extra transformation effort.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose dynamic partitioning uses a built-in Apache Hive-style partition key extraction engine that supports JQ expressions or inline timestamp parsing (e.g., using strptime). Under the hood, Firehose buffers incoming records, evaluates the partition keys on each record, and writes to the corresponding S3 prefix in near real-time. A real-world scenario where this matters is ingesting high-volume application logs with varying timestamps; without dynamic partitioning, you would need a Lambda function to parse each record and write to the correct prefix, which adds latency and cost.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Use Amazon Kinesis Data Firehose with dynamic partitioning — Amazon Kinesis Data Firehose with dynamic partitioning can automatically partition incoming JSON data based on the timestamp field without requiring custom transformation code. It evaluates the timestamp using a JQ expression or inline parsing, then writes records directly to S3 prefixes like year=2024/month=10/day=01. This minimizes transformation effort because the partitioning logic is configured declaratively in the Firehose delivery stream, eliminating the need for Lambda functions or post-ingestion processing.
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: Jul 4, 2026
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