- A
AWS Glue
Can be used for ETL jobs triggered by S3 events.
- B
Amazon Kinesis Data Firehose
Can ingest streaming data and transform before delivery to S3.
- C
Amazon Athena
Why wrong: Query service, not ingestion.
- D
AWS Step Functions
Why wrong: Orchestration service, not for data ingestion.
- E
Amazon Simple Queue Service (SQS)
Why wrong: Message queue, not for transformation.
Question 1,432 of 1,786
Near-Real-Time Data Ingestion and Transformation with AWS Glue and 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 building a data lake on Amazon S3 and needs to ingest data from multiple sources. Which of the following AWS services can be used to ingest and transform data in near real-time? (Select 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
✓
AWS Glue
AWS Glue is correct because it provides a serverless ETL (Extract, Transform, Load) service that can ingest data from various sources and transform it in near real-time using its streaming ETL capabilities. Glue can consume data from Amazon Kinesis Data Streams or Apache Kafka, apply transformations using Apache Spark, and write the results to Amazon S3 or other destinations, making it suitable for near real-time data ingestion and transformation.
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.
- ✓
AWS Glue
Why this is correct
Can be used for ETL jobs triggered by S3 events.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Can ingest streaming data and transform before delivery to S3.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Athena
Why it's wrong here
Query service, not ingestion.
- ✗
AWS Step Functions
Why it's wrong here
Orchestration service, not for data ingestion.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
Message queue, not for transformation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Amazon Athena (a query engine) with an ingestion service, or assume SQS alone can perform transformations, when neither service is designed for near real-time data ingestion and transformation into a data lake.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue streaming ETL uses Apache Spark Structured Streaming to process data in micro-batches, enabling near real-time transformations with exactly-once semantics when writing to S3. Kinesis Data Firehose can invoke a Lambda function for dynamic data transformation before delivery, and it automatically compresses and partitions data in S3, reducing downstream query costs. A real-world scenario is ingesting clickstream data from a website: Firehose can buffer and transform events, while Glue can join streams with reference data for enrichment.
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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Data Ingestion and Transformation — study guide chapter
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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: AWS Glue — AWS Glue is correct because it provides a serverless ETL (Extract, Transform, Load) service that can ingest data from various sources and transform it in near real-time using its streaming ETL capabilities. Glue can consume data from Amazon Kinesis Data Streams or Apache Kafka, apply transformations using Apache Spark, and write the results to Amazon S3 or other destinations, making it suitable for near real-time data ingestion and transformation.
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.
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 →
Same concept, more angles
1 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a data lake on Amazon S3. They need to ingest data from multiple sources, including relational databases, streaming data, and log files. Which THREE AWS services can be used to ingest data into the data lake?
medium- ✓ A.Amazon Kinesis Data Firehose
- ✓ B.AWS Database Migration Service (DMS)
- C.Amazon Athena
- D.Amazon Redshift Spectrum
- ✓ E.AWS Glue
Why A: Amazon Kinesis Data Firehose is a fully managed service for streaming data ingestion that can capture, transform, and load streaming data into Amazon S3 in near real-time. It supports sources like Amazon CloudWatch Logs, AWS IoT, and custom producers via the Kinesis Agent, making it ideal for log files and streaming data.
Keep practising
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Last reviewed: Jul 4, 2026
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.
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