← DEA-C01·Amazon Web Services

Question 550 of 1,786

Data Ingestion and Transformation →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON. This works because Firehose can invoke a Lambda function on each incoming record batch, allowing you to parse and fix issues like missing commas or extra brackets before the data is delivered to the main S3 bucket, while the existing retry and failed-record S3 bucket handle any records that still fail. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Firehose’s built-in transformation capability versus other services: a common trap is choosing Kinesis Data Analytics for record-level fixes, but that service is for streaming analytics, not record mutation. Remember that Lambda is the serverless, high-throughput way to clean data mid-stream without managing clusters. A helpful memory tip: “Lambda fixes the JSON, Firehose delivers the lesson.”

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 retail company uses Amazon Kinesis Data Firehose to ingest clickstream data from its website into an Amazon S3 bucket. The data includes fields: user_id, event_type, timestamp, page_url. Recently, the data engineering team noticed that some records have malformed JSON (missing commas, extra brackets) causing delivery failures to S3. The Firehose delivery stream is configured to retry failed records for 300 seconds, after which the records are sent to an S3 bucket for failed records. The team wants to transform the data to correct malformed JSON before delivery to the main S3 bucket. They need a solution that does not require managing servers and can handle high throughput. What should the team do?

Question 1mediummultiple choice

Full question →

A
Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.
Firehose supports Lambda transformations for record-level processing; it scales automatically.
B
Set up an Amazon EMR cluster with Apache Spark to process the data in micro-batches and fix JSON errors.
Why wrong: EMR requires cluster management and is overkill for simple JSON correction.
C
Use an AWS Glue streaming ETL job to read from Firehose and write corrected data to S3.
Why wrong: Glue streaming adds latency and cost; Firehose's built-in Lambda transformation is simpler and more cost-effective.
D
Use Amazon Kinesis Data Analytics with a SQL application to parse and fix JSON.
Why wrong: Kinesis Data Analytics is designed for real-time analytics, not for individual record transformation.

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

✓

Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.

Option B is correct: Lambda transformation in Firehose can process each record and fix JSON errors. Option A (Kinesis Data Analytics) is for real-time analytics, not record-level transformation. Option C (Glue streaming) adds complexity and latency. Option D (EMR) requires cluster management.

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.

✓
Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.
Why this is correct
Firehose supports Lambda transformations for record-level processing; it scales automatically.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set up an Amazon EMR cluster with Apache Spark to process the data in micro-batches and fix JSON errors.
Why it's wrong here
EMR requires cluster management and is overkill for simple JSON correction.
✗
Use an AWS Glue streaming ETL job to read from Firehose and write corrected data to S3.
Why it's wrong here
Glue streaming adds latency and cost; Firehose's built-in Lambda transformation is simpler and more cost-effective.
✗
Use Amazon Kinesis Data Analytics with a SQL application to parse and fix JSON.
Why it's wrong here
Kinesis Data Analytics is designed for real-time analytics, not for individual record transformation.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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.

What to study next

Got this wrong? Here's your next step.

Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

Related DEA-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Ingestion and Transformation practice questions

Practise DEA-C01 questions linked to Data Ingestion and Transformation.

Data Operations and Support practice questions

Practise DEA-C01 questions linked to Data Operations and Support.

Data Security and Governance practice questions

Practise DEA-C01 questions linked to Data Security and Governance.

Data Store Management practice questions

Practise DEA-C01 questions linked to Data Store Management.

DEA-C01 fundamentals practice questions

Practise DEA-C01 questions linked to DEA-C01 fundamentals.

DEA-C01 scenario practice questions

Practise DEA-C01 questions linked to DEA-C01 scenario.

DEA-C01 troubleshooting practice questions

Practise DEA-C01 questions linked to DEA-C01 troubleshooting.

Practice this exam

Start a free DEA-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

DEA-C01 practice-test guide →Study guide →Browse all practice tests

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: Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON. — Option B is correct: Lambda transformation in Firehose can process each record and fix JSON errors. Option A (Kinesis Data Analytics) is for real-time analytics, not record-level transformation. Option C (Glue streaming) adds complexity and latency. Option D (EMR) requires cluster management.

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

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

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.