Topic 1 Question 245
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?
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.
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.
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.
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.
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コメント(4)
- 正解だと思う選択肢: A
Duplicated again with 241
👍 2kaike_reis2023/08/17 - 正解だと思う選択肢: A
keywords near real time, streaming directly to S3 and least dev efforts
👍 1SandeepGun2023/06/17 - 正解だと思う選択肢: A
Amazon Kinesis Data Firehose is a fully managed service that makes it easy to stream data to Amazon S3. Amazon QuickSight ML Insights is a feature of Amazon QuickSight that allows you to detect outliers in your data using machine learning algorithms. Amazon QuickSight is a fully managed business intelligence service that allows you to visualize your data in dashboards.
👍 1Mickey3212023/08/22
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