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NEW QUESTION: 1
Which two tasks should you recommend? (Each correct answer presents part of the solution.) (Choose two.)
A. Install the Active Directory Management Gateway Service on a member server in fabrikam.com.
B. Install the Active Directory Management Gateway Service on a member server in blue yonder airlines.com.
C. Create an incoming, one-way trust in fabrikam.com.
D. Create an incoming, one-way trust in blueyonderairlines.com.
E. Install the Active Directory Management Gateway Service on a domain controller in fabrikam.com.
Answer: D,E
Explanation:
Explanation/Reference: You need to recommend a strategy to recover Active Directory if DC1 fails.

NEW QUESTION: 2

A. Option D
B. Option B
C. Option C
D. Option A
Answer: B

NEW QUESTION: 3
CPUベースのコンピューティングクラスターとAzureKubernetes Services（AKS）推論クラスターを含むAzure MachineLearningワークスペースがあります。分類モデルの作成に使用する予定のデータを含む表形式のデータセットを作成します。
Azure Machine Learningデザイナーを使用して、クライアントアプリケーションが新しいデータを送信し、応答として即時予測を取得することで分類モデルを利用できるWebサービスを作成する必要があります。
どの3つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序で配置します。

Answer:
Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
A company is developing a solution to manage inventory data for a group of automotive repair shops. The solution will use Azure SQL Data Warehouse as the data store.
Shops will upload data every 10 days.
Data corruption checks must run each time data is uploaded. If corruption is detected, the corrupted data must be removed.
You need to ensure that upload processes and data corruption checks do not impact reporting and analytics processes that use the data warehouse.
Proposed solution: Configure database-level auditing in Azure SQL Data Warehouse and set retention to 10 days.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Instead, create a user-defined restore point before data is uploaded. Delete the restore point after data corruption checks complete.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore