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NEW QUESTION 38
A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-word scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?
- A. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.
- B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
- C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
- D. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
NEW QUESTION 39
An analytics software as a service (SaaS) provider wants to offer its customers business intelligence <BI) reporting capabilities that are self-service The provider is using Amazon QuickSight to build these reports The data for the reports resides in a multi-tenant database, but each customer should only be able to access their own data The provider wants to give customers two user role options
* Read-only users for individuals who only need to view dashboards
* Power users for individuals who are allowed to create and share new dashboards with other users Which QuickSight feature allows the provider to meet these requirements’?
- A. Table calculations
- B. SPICE
- C. Isolated namespaces
- D. Embedded dashboards
NEW QUESTION 40
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
* Station A, which has 10 sensors
* Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B.
Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
- A. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
- B. Modify the partition key to use the sensor ID instead of the station name.
- C. Reduce the number of sensors in Station A from 10 to 5 sensors.
- D. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
NEW QUESTION 41
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