AWS Certified Machine Learning – Specialty — Question 244

Each morning, a data scientist at a rental car company creates insights about the previous day’s rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company’s locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.

Which solution will meet these requirements with the LEAST development time?

Answer options

• A. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
• B. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
• C. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
• D. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Correct answer: A

Explanation

Option A is correct because it uses Amazon Kinesis Data Firehose, which is designed for easy data streaming to Amazon S3, and leverages Amazon QuickSight ML Insights for quick analysis of high-demand outliers. The other options either utilize Amazon Kinesis Data Streams, which requires more development effort, or rely on more complex models in SageMaker, increasing the development time.