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NEW QUESTION: 1
Customerという名前のテーブルを含むAzure SQLデータベースがあります。 Customerには、次の表に示す列が含まれています。

次の表に示すように、マスキングルールを適用します。

どのユーザーが顧客のメールアドレスを表示できますか？
A. サーバー管理者のみ。
B. サーバー管理者と、Customer_Email列に対するUNMASK権限のみが付与されているすべてのユーザー。
C. サーバー管理者と、Customer_Email列に対するSELECT権限のみが付与されているすべてのユーザー。
D. Customer_Email列に対するUNMASK権限のみが付与されているすべてのユーザー。
Answer: D
Explanation:
Explanation
Grant the UNMASK permission to a user to enable them to retrieve unmasked data from the columns for which masking is defined.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking

NEW QUESTION: 2
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to ensure that a web service endpoint can receive image data and use an object recognition model to return the expected object and the confidence level of the model.
The solution must minimize the effort required to generate the client code to access the web service.
Which resource should you use?
A. the edX Data Science Learning Dashboard
B. Azure Machine Learning Studio
C. the Data Science Virtual Machine
D. Cortana Intelligence Gallery
Answer: B
Explanation:
Explanation/Reference:
Explanation:
There are two ways to add an endpoint to a Web service.
Programmatically

Through the Azure Machine Learning Web Services portal

Adding an endpoint using the Azure Machine Learning Web Services portal
1. In Machine Learning Studio, on the left navigation column, click Web Services.
2. At the bottom of the Web service dashboard, click Manage endpoints. The Azure Machine Learning Web Services portal opens to the endpoints page for the Web service.
3. Click New.
4. Type a name and description for the new endpoint.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-endpoint

NEW QUESTION: 3
In a Small Community Network (SCN) with Distributed Voicemail, which two are required? (Choose two.)
A. SMTP unblocked between Voicemail servers
B. IIS installed on all Voicemail Pro servers.
C. Preferred Edition Licenses on all IP Office systems
D. Port 80 supported.
E. Local Host file
Answer: A,B