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Data Ingestion and TransformationhardMultiple SelectObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

A company ingests IoT sensor data into Amazon Kinesis Data Streams. The data must be enriched with device metadata from Amazon DynamoDB and then stored in Amazon S3 in Apache Parquet format. The solution must minimize latency and cost. Which THREE steps should a data engineer implement? (Choose three.)

⚠ Common exam trap

It's easy for candidates to assume Lambda is the simplest and cheapest option for stream enrichment, but they overlook Lambda's concurrency limits, execution duration constraints, and lack of native Parquet conversion, which increases both latency and cost compared to using Kinesis Data Firehose with Flink for enrichment.

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

Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.

The correct three steps are: using Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB (null), performing a DynamoDB lookup in the Flink application for each record (E), and delivering the enriched data to Amazon Kinesis Data Firehose with Parquet conversion enabled (A). Kinesis Data Analytics for Apache Flink reads from Kinesis Data Streams, enriches each record via DynamoDB lookups, and outputs the enriched stream to Kinesis Data Firehose. Firehose automatically converts data to Apache Parquet before writing to S3, minimizing latency and cost by leveraging managed services without custom code or additional infrastructure.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.

    Why this is correct

    This step is correct because Kinesis Data Firehose is a fully managed service that can automatically convert incoming data to Parquet format before delivering to S3, reducing latency and operational overhead.

  • Configure an AWS Lambda function to read from the stream, enrich, and write to S3.

    Why it's wrong here

    Lambda has concurrency limits and may be more expensive per record for high throughput.

  • Use AWS Glue streaming ETL to enrich and convert data to Parquet.

    Why it's wrong here

    Glue streaming ETL adds latency and cost compared to native Kinesis integrations.

  • Use Amazon EMR with Spark Streaming to process and store the data.

    Why it's wrong here

    EMR adds operational overhead and is not as cost-effective for this simple enrichment.

  • Perform a DynamoDB lookup in the Flink application for each record.

    Why this is correct

    This step is correct because performing a DynamoDB lookup per record in the Flink application is the recommended pattern to enrich streaming data with metadata, enabling real-time enrichment without additional services.

  • Use Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB.

    Why this is correct

    Kinesis Data Analytics for Flink can perform real-time enrichment with low latency.

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 by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.