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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 a local web service deployment configuration and deploy the model to a local Docker container.
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 an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
E. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
Answer: D
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
In a Web Commerce environment, a "/shoppingcart" junction with four back-end application servers is created on the IBM Security Access Manager V9.0 Web Reverse Proxy. The back-end servers are not using session replication.
Which action is appropriate for maintaining session state when creating the junction?
A. Configure the junction to insert user session data "user_session_id" into the HTTP headers
B. Configure the junction so that the "least-busy" algorithm
C. Configure the junction so that it is configured to be a stateful junction
D. Create a WebSEAL-to WebSEAL_junction
Answer: A
Explanation:
Explanation/Reference:
Reference: https://publib.boulder.ibm.com/tividd/td/ITAME/GC23-4682-00/en_US/HTML/ws-agmst87.htm

NEW QUESTION: 3
ベースラインが適切に適用されていない場合、または不正な変更が行われた場合に発生する可能性がある脅威のタイプは次のうちどれですか？
A. 未検証のリダイレクトと転送
B. セキュリティの構成ミス
C. 機密データの漏洩
D. 安全でない直接オブジェクト参照
Answer: B
Explanation:
Security misconfigurations occur when applications and systems are not properly configured or maintained in a secure manner. This can be due to a shortcoming in security baselines or configurations, unauthorized changes to system configurations, or a failure to patch and upgrade systems as the vendor releases security patches. Insecure direct object references occur when code references aspects of the infrastructure, especially internal or private systems, and an attacker can use that knowledge to glean more information about the infrastructure. Unvalidated redirects and forwards occur when an application has functions to forward users to other sites, and these functions are not properly secured to validate the data and redirect requests, allowing spoofing for malware or phishing attacks. Sensitive data exposure occurs when an application does not use sufficient encryption and other security controls to protect sensitive application data.