070-475日本語 無料問題集「Microsoft Design and Implement Big Data Analytics Solutions (070-475日本語版)」
正解：
Explanation
References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-factory/v1/data-factory-use-custom-activ
正解：
Explanation
Box 1: Security
Security Policy
Example: After we have created Predicate function, we have to bind it to the table, using Security Policy. We will be using CREATE SECURITY POLICY command to set the security policy in place.
CREATE SECURITY POLICY DepartmentSecurityPolicy
ADD FILTER PREDICATE dbo.DepartmentPredicateFunction(UserDepartment) ON dbo.Department WITH(STATE = ON) Box 2: Filter
[ FILTER | BLOCK ]
The type of security predicate for the function being bound to the target table. FILTER predicates silently filter the rows that are available to read operations. BLOCK predicates explicitly block write operations that violate the predicate function.
Box 3: Block
Box 4: Block
Box 5: Filter
Topic 2, Litware, Inc
Overview
General Overview
Litware, Inc. is a company that manufactures personal devices to track physical activity and other health-related data.
Litware has a health tracking application that sends health-related data horn a user's personal device to Microsoft Azure.
Physical Locations
Litware has three development and commercial offices. The offices are located in the Untied States, Luxembourg, and India.
Litware products are sold worldwide. Litware has commercial representatives in more than 80 countries.
Existing Environment
Environment
In addition to using desktop computers in all of the offices. Litware recently started using Microsoft Azure resources and services for both development and operations.
Litware has an Azure Machine Learning Solution.
Litware Health Tracking Application
Litware recently extended its platform to provide third-party companies with the ability to upload data from devices to Azure. The data can be aggregated across multiple devices to provide users with a comprehensive view of their global health activity.
While the upload from each device is small, potentially more than 100 million devices will upload data daily by using an Azure event hub.
Each health activity has a small amount of data, such as activity type, start date/time, and end date/time. Each activity is limited to a total of 3 KB and includes a customer Identification key.
In addition to the Litware health tracking application, the users' activities can be reported to Azure by using an open API.
Machine Learning Experiments
The developers at Litware perform Machine Learning experiments to recommend an appropriate health activity based on the past three activities of a user.
The Litware developers train a model to recommend the best activity for a user based on the hour of the day.
Requirements
Planned Changes
Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
Business Goals
Minimize the costs associated with transferring data from the event hub to Azure Storage.
Technical Requirements
Litware identities the following technical requirements:
Data from the devices must be stored from three years in a format that enables the fast processing of data fields and Filtering.
The third-party companies must be able to use the Litware Machine learning models to generate recommendations to their users by using a third-party application.
Any changes to the health tracking application must ensure that the Litware developers can run the experiments without interrupting or degrading the performance of the production environment.
Privacy Requirements
Activity tracking data must be available to all of the Litware developers for experimentation. The developers must be prevented from accessing the private information of the users.
Other Technical Requirements
When the Litware health tracking application asks users how they feel, their responses must be reported to Azure.
正解：
Explanation
From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId, SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.
