Quick Answer
The answer is to use an AWS Glue ETL job to convert JSON to Parquet and load into Redshift. This is the correct approach because AWS Glue is a serverless, managed service that efficiently transforms semi-structured JSON into the columnar Parquet format, which is optimized for Redshift’s analytical queries and compression, while its pay-per-use pricing makes it cost-effective for nightly batch processing of small 10 MB files with minimal operational overhead. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of choosing the right serverless ETL service for cost-sensitive, scheduled transformations—a common trap is selecting Amazon EMR or manual EC2 instances, which add unnecessary complexity and cost for lightweight workloads. Remember the memory tip: “Glue for the small, Parquet for the column, Redshift for the query”—when you see nightly JSON files under 100 MB, think Glue’s serverless pricing and Parquet’s columnar efficiency.
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.
An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?
Use AWS Glue ETL job to convert to Parquet and load into Redshift.
Serverless and minimal overhead.
Use Amazon Redshift COPY command to load JSON directly.
Why wrong: Does not transform to columnar format.
Use Amazon EMR with Spark to transform and load data.
Why wrong: Requires cluster management.
Use AWS Lambda to transform each file and write to Redshift.
Why wrong: Timeout and scalability issues.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use AWS Glue ETL job to convert to Parquet and load into Redshift.
AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use AWS Glue ETL job to convert to Parquet and load into Redshift.
Why this is correct
Serverless and minimal overhead.
Related concept
Read the scenario before looking for a memorised answer.
Use Amazon Redshift COPY command to load JSON directly.
Why it's wrong here
Does not transform to columnar format.
Use Amazon EMR with Spark to transform and load data.
Why it's wrong here
Requires cluster management.
Use AWS Lambda to transform each file and write to Redshift.
Why it's wrong here
Timeout and scalability issues.
Common exam traps
The trap here is that candidates may choose Amazon EMR or Lambda because they are familiar with Spark or serverless functions, but they overlook the operational overhead of EMR and the execution limits of Lambda for batch workloads, while Glue provides a balanced, managed solution for this specific use case.
Detailed technical explanation
AWS Glue uses Apache Spark under the hood to perform distributed transformations, but for small files like 10 MB, it can run in a single-node mode to minimize costs. The conversion to Parquet leverages columnar storage, which reduces I/O and improves query performance in Redshift by enabling predicate pushdown and compression. In real-world scenarios, Glue's built-in crawlers and Data Catalog can automatically infer schemas from JSON, simplifying the ETL pipeline.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: Use AWS Glue ETL job to convert to Parquet and load into Redshift. — AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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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