MLA-C01 · Question #100
MLA-C01 Question #100: Real Exam Question with Answer & Explanation
The correct answer is D: Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use. Option D correctly pairs Amazon MSK (a managed Kafka service) for high-throughput clickstream ingestion with a downstream service - most likely Amazon EMR - that natively satisfies both remaining requirements: SQL-based processing via Spark SQL or Hive, and interactive analysis v
Question
A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis. Which solution will meet these requirements?
Options
- AUse Amazon Data Firehose to ingest the data. Create an AWS Lambda function to process the
- BUse Amazon Kinesis Data Streams to ingest the data. Use Amazon Data Firehose to transform
- CUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use AWS
- DUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use
Explanation
Option D correctly pairs Amazon MSK (a managed Kafka service) for high-throughput clickstream ingestion with a downstream service - most likely Amazon EMR - that natively satisfies both remaining requirements: SQL-based processing via Spark SQL or Hive, and interactive analysis via Jupyter notebooks through EMR Studio.
Why the distractors fail:
- Option A (Firehose + Lambda): Lambda is stateless and event-driven - it cannot perform continuous SQL stream processing, and has no Jupyter notebook capability.
- Option B (Kinesis Data Streams + Firehose): Firehose is a delivery service that routes data to destinations like S3 or Redshift; it is not a processing engine and supports neither SQL queries nor notebooks.
- Option C (MSK + something else): While MSK is a valid ingestion layer, the paired service in option C likely addresses only one of the two required capabilities (SQL or Jupyter), not both.
Memory tip: Associate MSK → EMR as the "full stack" real-time pattern - MSK handles durable, high-throughput Kafka ingestion, and EMR is the multi-tool that covers SQL processing and Jupyter notebooks in one managed cluster. When you see both SQL and notebooks required together, EMR is the strong signal for the correct answer.
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