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Data EngineeringhardMultiple SelectObjective-mapped

MLS-C01 Data Engineering Practice Question

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose three.)

⚠ Common exam trap

Candidates often confuse Kinesis Data Firehose's built-in Lambda transformations with the custom real-time processing capabilities of Kinesis Data Streams, overlooking that Firehose does not allow direct consumer applications or sub-second latency.

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

The need for custom real-time processing logic using consumer applications.

Kinesis Data Streams supports custom real-time processing via consumer applications using the Kinesis Client Library (KCL) or AWS Lambda, enabling fine-grained control over record processing, checkpointing, and custom logic. This is a key differentiator from Kinesis Data Firehose, which only supports built-in transformations via Lambda and does not allow direct consumer access to the stream.

Answer analysis

Option-by-option breakdown

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

  • The need for built-in data transformation and analytics.

    Why it's wrong here

    Incorrect: Neither has built-in analytics; Firehose can invoke Lambda for transformation.

  • The need for custom real-time processing logic using consumer applications.

    Why this is correct

    Correct: Data Streams supports custom consumers; Firehose does not.

  • The required end-to-end latency (seconds vs. minutes).

    Why this is correct

    Correct: Data Streams has sub-second latency; Firehose buffers data, introducing minutes of delay.

  • The need to manually manage shard capacity and scaling.

    Why this is correct

    Correct: Data Streams requires manual shard management; Firehose auto-scales.

  • The requirement for exactly-once delivery semantics.

    Why it's wrong here

    Incorrect: Neither service guarantees exactly-once delivery.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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