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DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer needs to run a transformation on streaming data using SQL-like queries without managing servers, and the output must be written to Amazon S3 in near real time. The source is an Amazon Kinesis Data Stream. Which AWS service is the MOST appropriate to perform the transformation?

⚠ Common exam trap

The trap here is treating AWS Lambda or a scheduled Glue job as equivalent to a managed streaming SQL engine, when only Flink provides continuous SQL processing without server management.

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

✓

Amazon Managed Service for Apache Flink with a SQL application.

Amazon Managed Service for Apache Flink provides a managed environment for running SQL-based stream processing applications against Kinesis Data Streams and can deliver results to Amazon S3 in near real time. Because AWS operates the underlying infrastructure, the engineer avoids cluster management while still getting continuous, low-latency transformation, which is precisely what the scenario requires.

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 with a Python shell job triggered hourly by Amazon EventBridge.

    Why it's wrong here

    A Python shell Glue job is intended for lightweight batch tasks and is not a streaming engine. Triggering it hourly introduces batch latency rather than near-real-time processing, and Python shell jobs have limited libraries and no native Kinesis stream consumption model. This does not satisfy the continuous streaming transformation requirement.

  • ✗

    AWS Lambda with a function triggered by Kinesis records and writing directly to S3.

    Why it's wrong here

    Lambda can process Kinesis records and write to S3, but it executes per-invocation logic rather than SQL-like continuous queries. Complex stateful transformations and windowed aggregations are difficult to express, and the scenario calls specifically for SQL-like querying. Lambda is better suited to lightweight per-record handling than to the streaming SQL transformation described.

  • ✓

    Amazon Managed Service for Apache Flink with a SQL application.

    Why this is correct

    Amazon Managed Service for Apache Flink supports SQL-based stream processing on Kinesis Data Streams and can write results to Amazon S3, while the service manages the underlying infrastructure. It is designed for continuous, near-real-time transformations and removes the burden of provisioning and scaling servers, which matches the SQL-like, serverless-management requirement and the S3 output destination.

  • ✗

    Amazon EMR running Apache Spark Structured Streaming on a persistent cluster.

    Why it's wrong here

    EMR with Spark Structured Streaming can process Kinesis streams, but it requires managing a cluster, including provisioning, scaling, and patching. The scenario explicitly asks for a solution without server management. While technically capable, it imposes operational overhead that the managed Flink option avoids, making it a poorer fit for the stated constraints.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint

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