- A
AWS Glue
Provides schema discovery, catalog, and batch ETL.
- B
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database, not for data lake ingestion.
- C
Amazon Athena
Why wrong: Athena is for querying, not ingestion.
- D
Amazon S3
Central storage for the data lake.
- E
Amazon Kinesis Data Firehose
Ingests streaming data into S3.
Question 901 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
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. The data sources include relational databases, streaming data, and log files. The data engineer needs to ensure that the data ingestion pipeline can handle schema evolution, support both batch and streaming, and provide a unified metadata catalog. Which THREE services should the engineer use? (Choose three.)
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 unified metadata catalog (the AWS Glue Data Catalog) that stores schema information for data stored in Amazon S3. It supports schema evolution by allowing you to update the catalog schema as data formats change, and it integrates with both batch (AWS Glue ETL jobs) and streaming (AWS Glue Streaming ETL) ingestion pipelines, making it the central service for metadata management in a data lake.
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
Provides schema discovery, catalog, and batch ETL.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database, not for data lake ingestion.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying, not ingestion.
- ✓
Amazon S3
Why this is correct
Central storage for the data lake.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Ingests streaming data into S3.
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 confuse Amazon Athena as a metadata catalog or ingestion service, but it is only a query engine that reads from S3 and relies on Glue for metadata, so it does not fulfill the ingestion or catalog requirements.
Detailed technical explanation
How to think about this question
AWS Glue Data Catalog is Apache Hive Metastore-compatible, meaning it can be used by other engines like Athena, EMR, and Redshift Spectrum. Schema evolution in Glue is managed through crawlers that detect changes in data formats (e.g., new columns in Parquet or JSON) and update the catalog schema, or through manual schema versioning. In a real-world scenario, if a streaming source adds a new field, Glue can update the catalog without breaking existing batch ETL jobs, ensuring downstream queries remain consistent.
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: AWS Glue — AWS Glue is correct because it provides a unified metadata catalog (the AWS Glue Data Catalog) that stores schema information for data stored in Amazon S3. It supports schema evolution by allowing you to update the catalog schema as data formats change, and it integrates with both batch (AWS Glue ETL jobs) and streaming (AWS Glue Streaming ETL) ingestion pipelines, making it the central service for metadata management in a data lake.
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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