070-475 無料問題集「Microsoft Design and Implement Big Data Analytics Solutions」
You need to ingest data from various data stores into a Microsoft Azure SQL data warehouse by using PolyBase.
You create an Azure Data Factory.
Which three components should you create next? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You create an Azure Data Factory.
Which three components should you create next? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、D
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You plan to deploy Microsoft Azure HDInsight clusters for business analytics and data pipelines. The clusters must meet the following requirements:
* Business users must use a language that is similar to SQL.
* The authoring of data pipelines must occur in a dataflow language.
You need to identify which language must be used for each requirement.
Which languages should you identify? To answer, drag the appropriate languages to the correct requirements.
Each language 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.
* Business users must use a language that is similar to SQL.
* The authoring of data pipelines must occur in a dataflow language.
You need to identify which language must be used for each requirement.
Which languages should you identify? To answer, drag the appropriate languages to the correct requirements.
Each language 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.
正解：
Explanation
You are using a Microsoft Azure Data Factory pipeline to copy data to an Azure SQL database.
You need to prevent the insertion of duplicate data for a given dataset slice.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You need to prevent the insertion of duplicate data for a given dataset slice.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、E
解答を投票する
You need to implement rls_table1.
Which code should you execute? To answer, drag the appropriate values to the correct targets. Each value 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.
Which code should you execute? To answer, drag the appropriate values to the correct targets. Each value 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.
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.
You have an Apache Hive cluster in Microsoft Azure HDInsight. The cluster contains 10 million data files.
You plan to archive the data.
The data will be analyzed monthly.
You need to recommend a solution to move and store the data. The solution must minimize how long it takes to move the data and must minimize costs.
Which two services should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You plan to archive the data.
The data will be analyzed monthly.
You need to recommend a solution to move and store the data. The solution must minimize how long it takes to move the data and must minimize costs.
Which two services should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、D
解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
You plan to deploy a storage solution to store the output of stream analytics.
You plan to store the data for the following three types of data streams:
* Unstructured JSON data
* Exploratory analytics
* Pictures
You need to implement a storage solution for the data stream types.
Which storage solution should you implement for each data stream type? To answer, drag the appropriate storage solutions to the correct data stream types. Each storage solution may be used once, more than once, or not at all. You may need to drag the split bar between the panes or scroll to view content.
NOTE: Each correct selection is worth one point.
You plan to store the data for the following three types of data streams:
* Unstructured JSON data
* Exploratory analytics
* Pictures
You need to implement a storage solution for the data stream types.
Which storage solution should you implement for each data stream type? To answer, drag the appropriate storage solutions to the correct data stream types. Each storage solution may be used once, more than once, or not at all. You may need to drag the split bar between the panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
Explanation
Box 1: Azure Data Lake Store
Stream Analytics supports Azure Data Lake Store. Azure Data Lake Store is an enterprise-wide hyper-scale repository for big data analytic workloads. Data Lake Store enables you to store data of any size, type and ingestion speed for operational and exploratory analytics. Stream Analytics has to be authorized to access the Data Lake Store.
Box 2: Azure Cosmos DB
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.
Box 3: Azure Blob Storage
Blob storage offers a cost-effective and scalable solution for storing large amounts of unstructured data in the cloud.
Incorrect Asnwers:
Azure SQL Database:
Azure SQL Database can be used as an output for data that is relational in nature or for applications that depend on content being hosted in a relational database. Stream Analytics jobs write to an existing table in an Azure SQL Database.
Azure Service Bus Queue:
Service Bus Queues offer a First In, First Out (FIFO) message delivery to one or more competing consumers.
Typically, messages are expected to be received and processed by the receivers in the temporal order in which they were added to the queue, and each message is received and processed by only one message consumer.
Azure Table Storage
Azure Table storage offers highly available, massively scalable storage, so that an application can automatically scale to meet user demand. Table storage is Microsoft's NoSQL key/attribute store, which one can leverage for structured data with fewer constraints on the schema. Azure Table storage can be used to store data for persistence and efficient retrieval.
References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs
You have a web app that accepts user input, and then uses a Microsoft Azure Machine Learning model to predict a characteristic of the user.
You need to perform the following operations:
* Track the number of web app users from month to month.
* Track the number of successful predictions made during the last minute.
* Create a dashboard showcasing the analytics tor the predictions and the web app usage.
Which lambda layer should you query for each operation? To answer, drag the appropriate layers to the correct operations. Each layer 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.
You need to perform the following operations:
* Track the number of web app users from month to month.
* Track the number of successful predictions made during the last minute.
* Create a dashboard showcasing the analytics tor the predictions and the web app usage.
Which lambda layer should you query for each operation? To answer, drag the appropriate layers to the correct operations. Each layer 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.
正解：
Explanation
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data.
The two view outputs may be joined before presentation
Box 1: Speed
The speed layer processes data streams in real time and without the requirements of fix-ups or completeness.
This layer sacrifices throughput as it aims to minimize latency by providing real-time views into the most recent data.
Box 2: Batch
The batch layer precomputes results using a distributed processing system that can handle very large quantities of data. The batch layer aims at perfect accuracy by being able to process all available data when generating views.
Box 3: Serving
Output from the batch and speed layers are stored in the serving layer, which responds to ad-hoc queries by returning precomputed views or building views from the processed data.