SAA-C03 · Question #166
A company has a large fleet of vehicles that are equipped with internet connectivity to send telemetry to the company. The company receives over 1 million data points every 5 minutes from the…
The correct answer is A. Use Amazon Timestream for LiveAnalytics to store the data points. Grant Amazon SageMaker. Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics. Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows. Amazon QuickSight: Efficiently generates interactive, real-time visual reports…
Question
A company has a large fleet of vehicles that are equipped with internet connectivity to send telemetry to the company. The company receives over 1 million data points every 5 minutes from the vehicles. The company uses the data in machine learning (ML) applications to predict vehicle maintenance needs and to preorder parts. The company produces visual reports based on the captured data. The company wants to migrate the telemetry ingestion, processing, and visualization workloads to AWS. Which solution will meet these requirements?
Options
- AUse Amazon Timestream for LiveAnalytics to store the data points. Grant Amazon SageMaker
- BUse Amazon DynamoDB to store the data points. Use DynamoDB Connector to ingest data from
- CUse Amazon Neptune to store the data points. Use Amazon Kinesis Data Streams to ingest data
- DUse Amazon Timestream to for LiveAnalytics to store the data points. Grant Amazon SageMaker
How the community answered
(55 responses)- A78% (43)
- B5% (3)
- C4% (2)
- D13% (7)
Explanation
Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics. Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows. Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.
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