- A
AWS Glue
Glue provides serverless ETL capabilities.
- B
Amazon EMR
Why wrong: EMR is not serverless; it requires cluster management.
- C
Amazon Athena
Why wrong: Athena is for querying, not transforming.
- D
AWS Lambda
Why wrong: Lambda is not designed for large-scale ETL transformations.
Question 1,365 of 1,786
Using AWS Glue for Serverless ETL Transformation
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 needs to transform JSON data from an S3 bucket into a structured format for Amazon Redshift. The transformation should be done serverlessly. Which service should be used?
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 the correct choice because it is a fully managed, serverless ETL service designed specifically for transforming and preparing data for analytics, including converting JSON to structured formats like Parquet or ORC for Amazon Redshift. It can crawl the S3 source, infer schemas, and run Spark-based transformation jobs without provisioning any infrastructure, aligning perfectly with the serverless requirement.
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
Glue provides serverless ETL capabilities.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon EMR
Why it's wrong here
EMR is not serverless; it requires cluster management.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying, not transforming.
- ✗
AWS Lambda
Why it's wrong here
Lambda is not designed for large-scale ETL transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Amazon Athena's serverless SQL querying capability with ETL transformation, but Athena cannot transform or write data into a different format for Redshift—it only reads and queries data in place.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood to perform distributed transformations, and its DynamicFrame abstraction handles semi-structured data like JSON natively, automatically resolving schema inconsistencies. For Redshift, Glue can output data in columnar formats like Parquet, which Redshift Spectrum can query directly, or load into Redshift via COPY commands, optimizing storage and query performance. A real-world scenario involves Glue's built-in transform 'Relationalize' that flattens nested JSON into normalized tables, a common requirement for Redshift's relational model.
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 the correct choice because it is a fully managed, serverless ETL service designed specifically for transforming and preparing data for analytics, including converting JSON to structured formats like Parquet or ORC for Amazon Redshift. It can crawl the S3 source, infer schemas, and run Spark-based transformation jobs without provisioning any infrastructure, aligning perfectly with the serverless requirement.
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
4 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. Refer to the exhibit. A data engineer runs this CLI command on an S3 bucket. The data is ingested from multiple sources. Which AWS service would be best to process these files in a single batch transformation?
easy- A.AWS Lambda
- B.Amazon Kinesis Data Analytics
- C.Amazon Athena
- ✓ D.AWS Glue
Why D: Option D is correct because AWS Glue is designed for batch ETL processing, capable of handling multiple files of varying sizes in a single transformation job. Option A is wrong because Lambda has limitations on execution time (15 minutes) and memory (10 GB), making it unsuitable for large-scale batch transforms. Option B is wrong because Kinesis Data Analytics is for real-time stream processing, not batch. Option C is wrong because Athena is an interactive query service for ad-hoc analysis of data in S3, not for batch transformations.
Variation 2. A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?
easy- ✓ A.AWS Glue ETL job
- B.AWS Glue DataBrew
- C.AWS Glue Workflow
- D.AWS Glue Crawler
Why A: Option A (AWS Glue ETL job) is the best suited because it can convert CSV to Parquet and load into Redshift daily. Option B (DataBrew) is a visual data preparation tool, not ideal for automated daily jobs. Option C (Workflow) orchestrates multiple jobs but does not perform transformation. Option D (Crawler) only discovers schema and catalogs data, not transform.
Variation 3. A data engineer needs to transform JSON data from an S3 bucket into Parquet format and load it into Amazon Redshift. The transformation must be performed incrementally as new data arrives. Which AWS service is BEST suited for this task?
easy- A.Use AWS Lambda to transform the data on the fly and write to Redshift.
- B.Use Amazon EMR with Apache Spark to transform the data and load it into Redshift.
- ✓ C.Use AWS Glue to create an ETL job that runs on a schedule or trigger.
- D.Use Amazon Kinesis Data Firehose to transform and load data into Redshift in real time.
Why C: Option C is correct because AWS Glue provides a serverless ETL service that can run jobs triggered by S3 events to transform data incrementally and load into Redshift. Option A (AWS Lambda) can be used for simple transformations but may hit time limits for complex transformations. Option B (Amazon EMR) is more suited for large-scale big data processing but requires cluster management. Option D (Amazon Kinesis Data Firehose) is for streaming data, not for batch transformation of existing S3 objects.
Variation 4. A data engineer needs to transform JSON data from an S3 bucket into Parquet format for efficient querying with Amazon Athena. The transformation must be serverless and event-driven. Which approach meets these requirements?
easy- A.Use AWS Glue with a scheduled crawler to convert the data.
- B.Use Amazon Athena to convert JSON to Parquet on the fly.
- ✓ C.Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data.
- D.Use Amazon EMR with a long-running cluster to process S3 data.
Why C: S3 event notifications can trigger a Lambda function that launches an AWS Glue ETL job. Glue ETL jobs run PySpark code natively to convert JSON to Parquet, satisfying the serverless and event-driven requirements. Athena only queries existing data, Glue crawlers do not perform transformations, and EMR clusters are not serverless.
Keep practising
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Last reviewed: Jul 4, 2026
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