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NEW QUESTION: 1
A. DirectQuery
B. In-Memory with DirectQuery
C. DirectQuery with In-Memory
D. In-Memory
Answer: B
NEW QUESTION: 2
You plan to deploy two new Microsoft Azure SQL Database instances. Once instance will support a data entry application. The other instance will support the company's business intelligence efforts. The databases will be accessed by mobile applications from public IP addresses.
You need to ensure that the database instances meet the following requirements:
* The database administration team must receive alerts for any suspicious activity in the data entry database, including potential SQL injection attacks.
* Executives around the world must have access to the business intelligence application.
* Sensitive data must never be transmitted. Sensitive data must not be stored in plain text in the database.
In the table below, identify the feature that you must implement for each database.
NOTE: Make only one selection in each column. Each correct selection is work one point.
Answer:
Explanation:
Explanation
Data entry: Threat Detection
SQL Threat Detection provides a new layer of security, which enables customers to detect and respond to potential threats as they occur by providing security alerts on anomalous activities. Users receive an alert upon suspicious database activities, potential vulnerabilities, and SQL injection attacks, as well as anomalous database access patterns.
Business intelligence: Dynamic Data Masking
Dynamic data masking limits (DDM) sensitive data exposure by masking it to non-privileged users. It can be used to greatly simplify the design and coding of security in your application.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-threat-detection
https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking
NEW QUESTION: 3
アカウントチームはProfessionalEditionでは利用できません
A. 偽
B. 本当
Answer: B
NEW QUESTION: 4
You need to set up the traffic prediction system. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
To create this credit risk assessment solution, we follow these steps:
* Create a Machine Learning workspace
* Upload existing data
* Create an experiment
* Train and evaluate the models
* Deploy the web service
* Access the web service
From scenario:
Predictive Routing:
* An Azure solution must be used for prediction systems.
* Predictive analytics must be published as a web service and accessible by using the REST API.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-walkthrough-develop-predictive-solu