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NEW QUESTION: 1
You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.
What should you do?
A. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
B. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
C. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
D. Create a local web service deployment configuration and deploy the model to a local Docker container.
E. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
Answer: A
Explanation:
Explanation
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
1. Register the model in the workspace model registry.
2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).

NEW QUESTION: 2
Which of the following content best outlines the scope of the Integration Test?
Please choose the correct answer.
Response:
A. a) Solution baseline
b) Delta/gap solutions
c) Data flows with non-SAP application
B. a) Solution baseline
b) Delta configuration
c) Data flows between SAP solutions
C. a) Prototype
b) Delta/gap solutions
c) Data flows with non-SAP application
D. a) Prototype
b) Delta configuration
c) Data flows between SAP solutions
Answer: A

NEW QUESTION: 3

A. Option A
B. Option B
C. Option D
D. Option C
Answer: B
Explanation:
Explanation
* Cross-origin resource sharing (CORS) is a mechanism that allows Javascript on a web page to make XMLHttpRequests to another domain, not the domain the Javascript originated from. Such "cross-domain" requests would otherwise be forbidden by web browsers, per the same origin security policy. CORS defines a way in which the browser and the server can interact to determine whether or not to allow the cross-origin request.[2] It is more powerful than only allowing same-origin requests, but it is more secure than simply allowing all such cross-origin requests.
* You must use Cross Origin Resource Sharing
It's not as complicated as it sounds...simply set your request headers appropriately...in Python it would look like:
self.response.headers.add_header('Access-Control-Allow-Origin', '*');
self.response.headers.add_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS'); self.response.headers.add_header('Access-Control-Allow-Headers', 'X-Requested-With'); self.response.headers.add_header('Access-Control-Max-Age', '86400');