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NEW QUESTION: 1
DRAG DROP
Contoso. Ltd. provides an API to customers by using Azure API Management (ARM). The API authorizes users with a JWT token You must implement response caching for the APIM gateway. The caching mechanism must detect the user ID of the client that accesses data for a given location and cache the response for that user ID.
You need to add the following policies to the policies file:
* a set-variable policy to store the detected user identity
* a cache-lookup-value policy
* a cache-store-value policy
* a find-and-replace policy to update the response body with the user profile information To which policy section should you add the policies? To answer, drag the appropriate sections to the correct policies. Each section may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION: 2
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. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
B. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
C. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
D. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
E. Create a local web service deployment configuration and deploy the model to a local Docker container.
Answer: C
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: 3
示す：
プロセスは、例外をスローする前に、回復ブロック、ブロック3で処理を最大3回実行することを目的としています。
データ項目「試行」は、初期値が0の数値データ項目です
データ項目「最大試行回数」は、初期値が3の数値データ項目です。「増分試行」計算段階では、試行値に1を加算し、結果を「試行」データ項目に出力します。これを正しく機能させるには、再試行の決定段階の正しい表現は？
A. [Attempts] = [Max]
B. [Attempts] > [Max]
C. [Attempts] < [Max]
D. [Attempts] + 1
Answer: C
NEW QUESTION: 4
HOTSPOT
Answer:
Explanation: