Topic 1 Question 241
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(6)
- 正解だと思う選択肢: A
Keywords Near real-time, visualization with minimal dev efforts
👍 2SandeepGun2023/06/17 - 正解だと思う選択肢: A
It's should be Firehose, and then it's should be least development effort, SageMaker is complicated and require a lot of effort. so it's A.
👍 1ADVIT2023/07/07 - 正解だと思う選択肢: A
Letters B - D are wrong, because KDS has no load power, that is, directly saving the files in any other service (you would need, for example, a KDF coupled to KDS). Letter A is correct, as QuickSight has tools to identify outliers. Letter C would be correct, but it requires more development to use something we already have in QuickSight.
👍 1kaike_reis2023/08/17
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