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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. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
D. Create a local web service deployment configuration and deploy the model to a local Docker container.
E. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
Answer: A
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 programming language is characterized as client-side, dynamic and weakly typed?
A. C#
B. ASP.NET
C. JavaScript
D. HTML
Answer: C
Explanation:
Explanation/Reference:
JavaScript is characterized as a dynamic, weakly typed, prototype-based language with first-class functions. It is primarily used in the form of client-side JavaScript for the development of dynamic websites.

NEW QUESTION: 3
Which three options describe ways in which the Cisco Compatible Extensions S36 message is used in Cisco WLC v7.0? (Choose three.)
A. by Cisco Compatible Extensions v4 and above
B. for location tracking of wireless tags
C. by Cisco Compatible Extensions v2 and above
D. on 802.11a/b/g/n
E. by Cisco WCS for calibration
F. only on 802.11b/g/n
G. for WGB location
Answer: C,D,E

NEW QUESTION: 4
An engineering team is developing and deploying AWS Lambda functions.
The team needs to create roles and manage policies in AWS IAM to configure the permissions of the Lambda functions.
How should the permissions for the team be configured so they also adhere to the concept of least privilege?
A. Create an IAM role with a managed policy attached that has permission boundaries specific to the Lambda functions.
Allow the engineering team to assume this role.
B. Create an execution role for the Lambda functions.
Attach a managed policy that has permission boundaries specific to these Lambda functions
C. Create an IAM role with a managed policy attached.
Allow the engineering team and the Lambda functions to assume this role
D. Create an IAM group for the engineering team with an lAMFullAccess policy attached.
Add all the users from the team to this IAM group
Answer: A