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MLS-C01 Kinesis Data Streams consumer Practice Question

A company uses Amazon Kinesis Data Streams to ingest clickstream data. They need to archive raw data to S3 every hour and also enable real-time processing with sub-second latency. Which TWO actions should they take? (Choose two.)

⚠ Common exam trap

Candidates often overlook that Kinesis Data Firehose has a maximum buffer interval of 900 seconds, making it unsuitable for hourly archives, and they may also underestimate Kinesis Data Analytics (Flink) as an archiving solution, assuming it only processes data rather than writing raw data to S3.

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

Use Kinesis Data Analytics to write output to S3.

AWS Lambda can be configured as a consumer of a Kinesis Data Stream using event source mapping, enabling real-time processing with sub-second latency. Option A is correct because Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) can read from a Kinesis stream and write output to S3, making it suitable for archiving raw data every hour—e.g., using a Flink sink with a tumbling window of 1 hour. Option D is incorrect because Kinesis Data Firehose has a maximum buffer interval of 900 seconds (15 minutes), not 3600 seconds; thus it cannot archive data exactly every hour as specified. Option C is incorrect because S3 events trigger Lambda on object creation in S3, not on real-time stream data. Option E is incorrect because the Kinesis Agent is used to send data from EC2 to Kinesis, not to S3 directly.

Answer analysis

Option-by-option breakdown

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

  • Use Kinesis Data Analytics to write output to S3.

    Why this is correct

    Correct. Kinesis Data Analytics (Flink) can read raw data from the stream and write it to S3 with a customizable window, such as every hour, serving as an archival mechanism.

  • Configure a Lambda function as a consumer of the stream for real-time processing.

    Why this is correct

    Correct. Lambda as a stream consumer provides real-time processing with sub-second latency, meeting the latency requirement.

  • Use S3 events to trigger a Lambda function that reads from the stream.

    Why it's wrong here

    Incorrect. S3 events are triggered after data is already in S3, not for real-time stream consumption; this would not meet the sub-second latency requirement for processing.

  • Create a Kinesis Data Firehose delivery stream with S3 as destination and set a buffer interval of 3600 seconds.

    Why it's wrong here

    Incorrect. Kinesis Data Firehose supports a maximum buffer interval of 900 seconds; setting it to 3600 seconds is not feasible, so it cannot archive every hour as stated.

  • Install the Kinesis Agent on an EC2 instance to write data to S3.

    Why it's wrong here

    Incorrect. The Kinesis Agent is designed to push data from EC2 into Kinesis streams, not to read from streams or write to S3 directly.

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 MLS-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 MLS-C01 exam.