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NEW QUESTION: 1
A. Option C
B. Option A
C. Option E
D. Option B
E. Option D
Answer: E
NEW QUESTION: 2
You plan to create a real-time monitoring app that alerts users when a device travels more than 200 meters away from a designated location.
You need to design an Azure Stream Analytics job to process the data for the planned app. The solution must minimize the amount of code developed and the number of technologies used.
What should you include in the Stream Analytics job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Input type: Stream
You can process real-time IoT data streams with Azure Stream Analytics.
Input source: Azure IoT Hub
In a real-world scenario, you could have hundreds of these sensors generating events as a stream. Ideally, a gateway device would run code to push these events to Azure Event Hubs or Azure IoT Hubs.
Function: Geospatial
With built-in geospatial functions, you can use Azure Stream Analytics to build applications for scenarios such as fleet management, ride sharing, connected cars, and asset tracking.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices
https://docs.microsoft.com/en-us/azure/stream-analytics/geospatial-scenarios
NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to ensure that a web service endpoint can receive image data and use an object recognition model to return the expected object and the confidence level of the model.
The solution must minimize the effort required to generate the client code to access the web service.
Which resource should you use?
A. the Data Science Virtual Machine
B. the edX Data Science Learning Dashboard
C. Azure Machine Learning Studio
D. Cortana Intelligence Gallery
Answer: C
Explanation:
Explanation/Reference:
Explanation:
There are two ways to add an endpoint to a Web service.
Programmatically
Through the Azure Machine Learning Web Services portal
Adding an endpoint using the Azure Machine Learning Web Services portal
1. In Machine Learning Studio, on the left navigation column, click Web Services.
2. At the bottom of the Web service dashboard, click Manage endpoints. The Azure Machine Learning Web Services portal opens to the endpoints page for the Web service.
3. Click New.
4. Type a name and description for the new endpoint.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-endpoint
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