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Question 599 of 1,786

Data Ingestion and Transformation →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3. This solution is correct because Apache Flink natively supports event-time processing and stateful operations, allowing it to perform real-time aggregation and deduplication of streaming data using Kinesis Data Analytics without external state stores. The tumbling window groups records per vehicle per minute, computing average speed and min/max coordinates, while Flink’s keyed state or a unique identifier check ensures duplicate GPS payloads are not double-counted. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of serverless stream processing with Flink versus alternatives like Lambda or Glue, which lack built-in windowing or add latency. A common trap is choosing Firehose with Lambda, but that requires custom state management for deduplication. Memory tip: think “Flink for Flinkering windows” — Flink handles both the window and the deduplication in one serverless job.

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 logistics company ingests real-time GPS location data from thousands of delivery vehicles into Amazon Kinesis Data Streams. Each vehicle sends a JSON payload every 10 seconds containing vehicle_id, latitude, longitude, timestamp, and speed. The data must be stored in Amazon S3 for historical analysis, but the company wants to first aggregate the data per vehicle per minute (average speed, min/max coordinates) to reduce storage costs. The solution must be serverless and handle potential duplicate records without double-counting. What should the engineer do?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

Question 1mediummultiple choice

Read the full NAT/PAT explanation →

A
Use Amazon EMR with Spark Streaming to perform the aggregation and write to S3.
Why wrong: EMR requires cluster management and is not serverless.
B
Use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.
Flink supports windowed aggregations and stateful deduplication; Firehose delivers to S3.
C
Use Kinesis Data Firehose with a Lambda transformation to aggregate records in a 1-minute window.
Why wrong: Lambda in Firehose processes each record individually; it cannot maintain state across records for windowed aggregation.
D
Use an AWS Glue streaming ETL job with Spark Structured Streaming to aggregate and deduplicate.
Why wrong: Glue streaming ETL is serverless but adds latency and cost; Flink is more efficient for real-time aggregation.

Full breakdown with real-world context →

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 Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.

Option D is correct: Kinesis Data Analytics for Flink can aggregate data in real-time using tumbling windows and deduplication. Option A (Firehose with Lambda) would require per-record processing and state management. Option B (Glue streaming) adds latency. Option C (EMR) is not serverless.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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 EMR with Spark Streaming to perform the aggregation and write to S3.
Why it's wrong here
EMR requires cluster management and is not serverless.
✓
Use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.
Why this is correct
Flink supports windowed aggregations and stateful deduplication; Firehose delivers to S3.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Kinesis Data Firehose with a Lambda transformation to aggregate records in a 1-minute window.
Why it's wrong here
Lambda in Firehose processes each record individually; it cannot maintain state across records for windowed aggregation.
✗
Use an AWS Glue streaming ETL job with Spark Structured Streaming to aggregate and deduplicate.
Why it's wrong here
Glue streaming ETL is serverless but adds latency and cost; Flink is more efficient for real-time aggregation.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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.

What to study next

Got this wrong? Here's your next step.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.

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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 — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3. — Option D is correct: Kinesis Data Analytics for Flink can aggregate data in real-time using tumbling windows and deduplication. Option A (Firehose with Lambda) would require per-record processing and state management. Option B (Glue streaming) adds latency. Option C (EMR) is not serverless.

What should I do if I get this DEA-C01 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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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.