- A
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
This step is correct because Kinesis Data Firehose is a fully managed service that can automatically convert incoming data to Parquet format before delivering to S3, reducing latency and operational overhead.
- B
Configure an AWS Lambda function to read from the stream, enrich, and write to S3.
Why wrong: Lambda has concurrency limits and may be more expensive per record for high throughput.
- C
Use AWS Glue streaming ETL to enrich and convert data to Parquet.
Why wrong: Glue streaming ETL adds latency and cost compared to native Kinesis integrations.
- D
Use Amazon EMR with Spark Streaming to process and store the data.
Why wrong: EMR adds operational overhead and is not as cost-effective for this simple enrichment.
- E
Perform a DynamoDB lookup in the Flink application for each record.
This step is correct because performing a DynamoDB lookup per record in the Flink application is the recommended pattern to enrich streaming data with metadata, enabling real-time enrichment without additional services.
- F
Use Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB.
Kinesis Data Analytics for Flink can perform real-time enrichment with low latency.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 ingests IoT sensor data into Amazon Kinesis Data Streams. The data must be enriched with device metadata from Amazon DynamoDB and then stored in Amazon S3 in Apache Parquet format. The solution must minimize latency and cost. Which THREE steps should a data engineer implement? (Choose three.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
The correct three steps are: using Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB (null), performing a DynamoDB lookup in the Flink application for each record (E), and delivering the enriched data to Amazon Kinesis Data Firehose with Parquet conversion enabled (A). Kinesis Data Analytics for Apache Flink reads from Kinesis Data Streams, enriches each record via DynamoDB lookups, and outputs the enriched stream to Kinesis Data Firehose. Firehose automatically converts data to Apache Parquet before writing to S3, minimizing latency and cost by leveraging managed services without custom code or additional infrastructure.
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.
- ✓
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
Why this is correct
This step is correct because Kinesis Data Firehose is a fully managed service that can automatically convert incoming data to Parquet format before delivering to S3, reducing latency and operational overhead.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure an AWS Lambda function to read from the stream, enrich, and write to S3.
Why it's wrong here
Lambda has concurrency limits and may be more expensive per record for high throughput.
- ✗
Use AWS Glue streaming ETL to enrich and convert data to Parquet.
Why it's wrong here
Glue streaming ETL adds latency and cost compared to native Kinesis integrations.
- ✗
Use Amazon EMR with Spark Streaming to process and store the data.
Why it's wrong here
EMR adds operational overhead and is not as cost-effective for this simple enrichment.
- ✓
Perform a DynamoDB lookup in the Flink application for each record.
Why this is correct
This step is correct because performing a DynamoDB lookup per record in the Flink application is the recommended pattern to enrich streaming data with metadata, enabling real-time enrichment without additional services.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB.
Why this is correct
Kinesis Data Analytics for Flink can perform real-time enrichment with low latency.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Lambda is the simplest and cheapest option for stream enrichment, but they overlook Lambda's concurrency limits, execution duration constraints, and lack of native Parquet conversion, which increases both latency and cost compared to using Kinesis Data Firehose with Flink for enrichment.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Firehose uses a configurable buffer interval (60 seconds default) and buffer size (5 MB default) to batch records before converting to Parquet via a schema provided by AWS Glue or a custom schema; this batching minimizes S3 PUT costs and optimizes compression. The enrichment step using Kinesis Data Analytics for Apache Flink allows stateful processing with exactly-once semantics, performing DynamoDB lookups per record via the Flink Async I/O API to avoid blocking the stream, which is critical for maintaining low latency in high-throughput IoT scenarios.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion. — The correct three steps are: using Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB (null), performing a DynamoDB lookup in the Flink application for each record (E), and delivering the enriched data to Amazon Kinesis Data Firehose with Parquet conversion enabled (A). Kinesis Data Analytics for Apache Flink reads from Kinesis Data Streams, enriches each record via DynamoDB lookups, and outputs the enriched stream to Kinesis Data Firehose. Firehose automatically converts data to Apache Parquet before writing to S3, minimizing latency and cost by leveraging managed services without custom code or additional infrastructure.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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