Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 113 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01

Question #: 113
Topic #: 1

[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

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?

A. Use Amazon Data Firehose to ingest the data. Create an AWS Lambda function to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
B. Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Data Firehose to transform the data. Use Amazon Athena to process the data. Use Amazon QuickSight to visualize the data.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use AWS Glue with PySpark to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
D. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data.

Show Suggested Answer

Suggested Answer: D 🗳️

by chris_spencer at March 13, 2025, 8:43 a.m.

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eesa

Highly Voted 5 months ago

Selected Answer: D

✅ Why Option D is correct: Requirements Recap: Real-time ingestion of high-volume clickstream data SQL-based data processing Jupyter notebooks for interactive analysis Near real-time visualization Amazon MSK + Managed Flink (Apache Flink): MSK is ideal for handling high-throughput, real-time event streams like clickstream data. Amazon Managed Service for Apache Flink: Provides stream processing with low latency Supports SQL via Apache Flink SQL Can integrate with Jupyter Notebooks via Apache Zeppelin or SageMaker notebooks (for interactive analysis) Flink dashboard allows for real-time data visualization and monitoring This stack is purpose-built for real-time streaming analytics, supports SQL for processing, and integrates well with notebooks and dashboards.

upvoted 5 times

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snna4

Most Recent 3 months, 1 week ago

Selected Answer: D

Or D option may be correct also. Amazon Managed Service for Apache Flink supports: * Flink SQL: Amazon Managed Service for Apache Flink supports standard SQL syntax along with streaming-specific extensions for time-based operations and windowing. * Studio Notebooks: Use SQL, Python, and Scala in the same development environment. Syntax highlighting, validation, and context-sensitive suggestions guide you within the notebook to interact with your data.

upvoted 1 times

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snna4

3 months, 3 weeks ago

Selected Answer: C

AWS Glue with PySpark, which does support Jupyter notebooks through AWS Glue Studio notebooks and can process data with SQL through Spark SQL

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ygn4ei

5 months ago

Selected Answer: B

correct

upvoted 1 times

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chris_spencer

5 months, 1 week ago

Selected Answer: C

Should be C. AWS Glue with PySpark supports SQL-like transformations and can be integrated with Jupyter notebooks. D is incorrect because Apache Flink for processing does not natively support SQL for data processing.

upvoted 1 times

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