🌊 Amazon Kinesis - DEA-C01 Practice Questions

Amazon Kinesis enables real-time data streaming. Study Kinesis Data Streams, Data Firehose, Data Analytics, shard management, consumers, and streaming ingestion patterns.

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DEA-C01 Kinesis Question Bank (12 Questions)

Browse all 12 practice questions covering Amazon Kinesis for the DEA-C01 certification exam. Answers are intentionally hidden on this page so you can self-test first before checking results in quiz mode.

  1. Question 1Data Ingestion and Transformation

    A data pipeline needs to process exactly-once delivery of records from Amazon Kinesis Data Streams. Which consumer type provides this capability?

    AClassic fan-out consumer
    BEnhanced fan-out consumer
    CKinesis Client Library with checkpointing
    DLambda event source mapping with bisect on error

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  2. Question 2Data Ingestion and Transformation

    What is the difference between Kinesis Data Streams and Kinesis Data Firehose?

    ASame service
    BData Streams: real-time streaming with custom consumers and retention; Firehose: fully managed delivery to S3/Redshift/OpenSearch with automatic scaling and no consumer management
    CFirehose is real-time
    DData Streams is for delivery only

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  3. Question 3Data Ingestion and Transformation

    A data pipeline processes streaming records that arrive out of order due to network delays. The team needs to process records within a 60-second window and handle late arrivals. Which Kinesis Data Analytics feature addresses this?

    ATumbling windows
    BSliding windows
    CWindowed queries with late arrival grace period
    DSESSION windows

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  4. Question 4Data Ingestion and Transformation

    Which Amazon Kinesis service should be used for real-time analytics on streaming data with SQL queries?

    AKinesis Data Streams
    BKinesis Data Firehose
    CKinesis Data Analytics
    DKinesis Video Streams

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  5. Question 5Data Ingestion and Transformation

    A data engineer needs to deduplicate records in a streaming pipeline before writing to S3. The data arrives via Kinesis Data Streams. What is the recommended approach?

    AUse Kinesis Data Firehose deduplication feature
    BUse AWS Glue Streaming ETL with a window function
    CEnable enhanced fan-out on the stream
    DUse Amazon SQS FIFO as an intermediary

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  6. Question 6Data Operations and Support

    Which approach BEST handles late-arriving data in a streaming pipeline using Amazon Kinesis Data Analytics?

    AIncrease the shard count
    BUse watermarks and allowed lateness in Flink
    CEnable enhanced fan-out
    DReduce the batch window

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  7. Question 7Data Ingestion and Transformation

    A team needs to stream real-time clickstream data from a website to Amazon S3 and Amazon Redshift simultaneously. Which service provides fully managed streaming delivery with built-in transformation?

    AAmazon Kinesis Data Streams
    BAmazon Kinesis Data Firehose
    CAmazon MSK
    DAWS Data Pipeline

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  8. Question 8Data Ingestion and Transformation

    A company wants to stream data changes from an Amazon RDS MySQL database to Amazon S3 in near real-time. Which service should they use?

    AAWS DMS with CDC
    BAWS Glue Streaming ETL
    CAmazon Kinesis Data Firehose
    DAWS Lambda with RDS Proxy

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  9. Question 9Data Operations and Support

    Which CloudWatch metric should be monitored to detect when an Amazon Kinesis Data Stream is losing data due to insufficient capacity?

    AGetRecords.IteratorAgeMilliseconds
    BWriteProvisionedThroughputExceeded
    CIncomingBytes
    DGetRecords.Latency

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  10. Question 10Data Ingestion and Transformation

    Which Kinesis service delivers streaming data to S3, Redshift, or Elasticsearch with built-in transformation?

    AKinesis Data Streams
    BKinesis Data Firehose
    CKinesis Data Analytics
    DKinesis Video Streams

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  11. Question 11Data Operations and Support

    What is the recommended approach for handling failed records in a Kinesis stream?

    AIgnore failures
    BUse Kinesis error handling with dead-letter queues (SQS) and Lambda retry policies to capture and reprocess failed records
    CStop the entire stream
    DDelete the stream and recreate it

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  12. Question 12Data Ingestion and Transformation

    What are Amazon Kinesis Data Firehose delivery destinations?

    AOnly S3
    BAmazon S3, Amazon Redshift, Amazon OpenSearch Service, Splunk, Datadog, HTTP endpoints, and custom destinations via Lambda transformation
    COnly Redshift
    DOnly OpenSearch

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Key Kinesis Concepts for DEA-C01

kinesisdata streamsfirehosedata analyticsshardstreamreal-timeconsumerproducerenhanced fan-out

DEA-C01 Kinesis Exam Tips

Amazon Kinesis questions in DEA-C01 are typically scenario-based. Focus on data ingestion, transformation, storage optimization, and governance. Priority concepts: kinesis, data streams, firehose, data analytics, shard, stream.

What DEA-C01 Expects

  • Anchor your answer in choose scalable data pipeline patterns with clear data quality and security controls.
  • Kinesis scenarios for DEA-C01 are frequently mapped to Domain 1 (34%), Domain 3 (22%), so read the objective carefully before picking controls or architecture.
  • Expect multi-topic scenarios where Kinesis interacts with IAM, networking, storage, or observability patterns rather than appearing as an isolated question.
  • When two options are both technically valid, prefer the choice that best aligns with the exam's operational scope (Associate) and vendor best practices.

High-Value Kinesis Concepts

  • Know the core Kinesis building blocks cold: kinesis, data streams, firehose, data analytics.
  • Review the edge-case features and limits for shard, stream; these details are commonly used to differentiate answer choices.
  • Practice service-integration reasoning: how Kinesis pairs with AWS Glue, Lambda, MSK, Redshift in real deployment patterns.
  • For DEA-C01, explain why the chosen Kinesis design meets reliability, security, and cost expectations better than the alternatives.

Common DEA-C01 Traps

  • Watch for ignoring partitioning, schema evolution, or query efficiency.
  • Questions in Data Ingestion and Transformation often include distractors that look correct for Kinesis but violate least-privilege, durability, or availability requirements.
  • Avoid picking options purely by feature name; validate data path, failure handling, and governance impact before answering.
  • If the prompt hints at automation or repeatability, eliminate manual-only operational answers first.

Fast Review Checklist

  • Can you compare at least two Kinesis implementation paths and justify which one best fits the scenario?
  • Can you map the chosen answer back to Data Ingestion and Transformation (34%) outcomes for DEA-C01?
  • Can you explain security and access boundaries for Kinesis without relying on default-open assumptions?
  • Can you describe how Kinesis integrates with AWS Glue and Lambda during failure, scaling, and monitoring events?

Exam Domains Covering Kinesis

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