Quick Answer
The answer is AWS Glue ETL jobs and Amazon EMR with Spark. Both services natively read JSON, CSV, and Avro formats and write to Parquet with built-in partitioning by date and source, requiring only a concise PySpark or Scala script to load, partition, and output the data to S3. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of serverless versus managed Spark options for converting CSV JSON Avro to Parquet in an AWS data lake, with a common trap being to select AWS Data Pipeline or Lambda—neither of which efficiently handles large-scale format conversion with minimal custom code. Remember that Glue offers a visual ETL job option for even less coding, while EMR gives you more control over cluster configuration; both leverage Spark’s native Parquet writer and partition discovery. A useful memory tip: think “Glue for no-cluster fuss, EMR for custom muscle”—both get the Parquet job done.
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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. Data arrives from multiple sources in JSON, CSV, and Avro formats. The data must be transformed to Parquet and partitioned by date and source. Which TWO services can perform this transformation with minimal custom code? (Choose TWO.)
Amazon EMR with Spark
EMR can run Spark for large-scale transformations.
AWS Lake Formation
Why wrong: Lake Formation is for data lake management, not transformation.
Amazon Athena CTAS queries
Why wrong: Athena is for querying, not transformation pipelines.
AWS Glue ETL jobs
Glue provides built-in transforms and can write Parquet.
Amazon Kinesis Data Firehose
Why wrong: Firehose is for streaming, not batch transformation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon EMR with Spark
Amazon EMR with Spark is correct because Spark natively supports reading JSON, CSV, and Avro formats and writing Parquet with built-in partitioning by date and source. You can achieve this with a concise PySpark or Scala script that loads the data, applies partitioning logic, and writes to S3, requiring minimal custom code beyond the transformation logic.
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.
Amazon EMR with Spark
Why this is correct
EMR can run Spark for large-scale transformations.
Related concept
Read the scenario before looking for a memorised answer.
AWS Lake Formation
Why it's wrong here
Lake Formation is for data lake management, not transformation.
Amazon Athena CTAS queries
Why it's wrong here
Athena is for querying, not transformation pipelines.
AWS Glue ETL jobs
Why this is correct
Glue provides built-in transforms and can write Parquet.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose is for streaming, not batch transformation.
Common exam traps
The trap here is that candidates often confuse AWS Lake Formation's data catalog and permission features with actual data transformation capabilities, or they assume Kinesis Data Firehose can transform existing S3 objects when it only processes streaming data in transit.
Detailed technical explanation
Under the hood, Spark uses its Catalyst optimizer to push down partition pruning when writing to S3, allowing efficient directory-based partitioning (e.g., s3://bucket/date=2025-03-01/source=web/). In real-world scenarios, you might need to handle schema evolution or nested Avro schemas, which Spark's DataFrame API handles gracefully with `spark.read.format('avro')` and `df.write.partitionBy('date', 'source').parquet('s3://output')`.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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Data Ingestion and Transformation — study guide chapter
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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: Amazon EMR with Spark — Amazon EMR with Spark is correct because Spark natively supports reading JSON, CSV, and Avro formats and writing Parquet with built-in partitioning by date and source. You can achieve this with a concise PySpark or Scala script that loads the data, applies partitioning logic, and writes to S3, requiring minimal custom code beyond the transformation logic.
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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