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Case Study 11 – Trey Research D (Question 297 – Question 314)
Background
You are a software architect for Trey Research Inc., a Software-as-a-Service (SaaS) company that provides text analysis services. Trey Research Inc. has a service that scans text documents and analyzes the content to determine content similarities. These similarities are referred to as categories, and indicate groupings on authorship, opinions, and group affiliation.
The document scanning solution has an Azure Web App that provides the user interface. The web app includes the following pages:
Document Uploads: This page allows customers to upload documents manually.
Document Inventory: This page shows a list of all processed documents provided by a customer. The page can be configured to show documents for a selected category.
Document Upload Sources: This page shows a map and information about the geographic distribution of uploaded documents. This page allows users to filter the map based on assigned categories.
The web application is instrumented with Azure Application Insights. The solution uses Cosmos DB for data storage.
Changes to the web application and data storage are not permitted.
The solution contains an endpoint where customers can directly upload documents from external systems.
Document processing
Source Documents
Documents must be in a specific format before they are uploaded to the system. The first four lines of the document must contain the following information. If any of the first four lines are missing or invalid, the document must not be processed.
the customer account number
the user who uploaded the document
the IP address of the person who created the document
the date and time the document was created
The remaining portion of the documents contain the content that must be analyzed. Prior to processing by the Azure Data Factory pipeline, the document text must be normalized so that words have spaces between them.
Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Other requirements
Business Analysts
Trey Research Inc. business analysts must be able to review processed documents, and analyze data by using Microsoft Excel. Business analysts must be able to discover data across the enterprise regardless of where the data resides.
Data Science
Data scientists must be able to analyze results without changing the deployed application. The data scientists must be able to analyze results without being connected to the Internet.
Security and Personally Identifiable Information (PII)
Access to the analysis results must be limited to the specific customer account of the user that originally uploaded the documents.
All access and usage of analysis results must be logged. Any unusual activity must be detected.
Documents must not be retained for more than 100 hours.
Operations
All application logs, diagnostic data, and system monitoring must be available in a single location.
Logging and diagnostic information must be reliably processed.
The document upload time must be tracked and monitored.
QUESTION 297
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on 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.
You need to design the system that handles uploaded documents.
Solution: Use an Azure Data Lake Store as the location to upload documents. Use Azure Event Grid for user notification and to start processing.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
An Azure Blob Container, which uses an object store with flat namespace, is better than an Azure Data Lake Store, which uses Azure Data Lake uses an Hierarchical file system.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference: https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-comparison-with-blob- storage
QUESTION 298
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on 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.
You need to design the system that handles uploaded documents.
Solution: Use an Azure Blob Container as the location to upload documents. Use Azure Service Bus for user notification and to start processing.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
An Azure Blob Container, which uses an object store with flat namespace, is good for this scenario.
A service bus is needed to meet the requirements.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference: https://docs.microsoft.com/en-us/azure/event-grid/compare-messaging-services
QUESTION 299
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on 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.
You need to design the system that handles uploaded documents.
Solution: Use an Azure Blob Container as the location to upload documents. Use Azure Event Grid for user notification and to start processing.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
An Azure Blob Container, which uses an object store with flat namespace, is good for this scenario.
However, a service bus is needed to meet the requirements, not an Event grid.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference: https://docs.microsoft.com/en-us/azure/event-grid/compare-messaging-services
QUESTION 310
You need to ensure that documents are ready to be processed.
Which API should you use?
A. Linguistic Analysis API
B. Translator Text API
C. Text Analytics API
D. Web Language Model API
Answer: D
Explanation:
With the Web Language Model automate a variety of standard natural language processing tasks using state-of-the-art language modeling APIs.
Scenario: Source Documents
Documents must be in a specific format before they are uploaded to the system. The first four lines of the document must contain the following information. If any of the first four lines are missing or invalid, the document must not be processed.
the customer account number
the user who uploaded the document
the IP address of the person who created the document
the date and time the document was created
The remaining portion of the documents contain the content that must be analyzed. Prior to processing by the Azure Data Factory pipeline, the document text must be normalized so that words have spaces between them.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/web-language-model/
QUESTION 311
You need to ensure that data security requirements are met.
What should you do?
A. Enable Role-Based Access Control (RBAC) for each database.
B. Use Azure Key Vault HSM for encrypting the results of the analysis
C. Ensure that all applications use Cosmos DB secondary master keys.
D. Generate Cosmos DB resource tokens for each collection.
Answer: D
Explanation:
Resource tokens provide access to the application resources within a database. Resource tokens:
Provide access to specific collections, partition keys, documents, attachments, stored procedures, triggers, and UDFs.
Are created when a user is granted permissions to a specific resource.
You can use a resource token (by creating Cosmos DB users and permissions) when you want to provide access to resources in your Cosmos DB account to a client that cannot be trusted with the master key.
Scenario: Security and Personally Identifiable Information (PII) Access to the analysis results must be limited to the specific customer account of the user that originally uploaded the documents.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/secure-access-to-data#resource-tokens
QUESTION 312
Drag and Drop Question
You need to ensure that operational and security requirements are met.
What should you recommend? To answer, drag the appropriate log approaches to the correct locations. Each log approach 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.
Answer:
Explanation:
Box 1: Log to Azure Event Hubs
Cosmost DB logs can be send logs to Azure Storage, can be streamed to Azure Event Hubs, and be exported to Azure Log Analytics.
Box 2: Process Logs with Azure functions
Azure Data Factory Diagnostic logs
Save them to a Storage Account for auditing or manual inspection. You can specify the retention time (in days) using the diagnostic settings.
Stream them to Event Hubs for ingestion by a third-party service or custom analytics solution such as PowerBI.
Analyze them with Log Analytics
Scenario:
All application logs, diagnostic data, and system monitoring must be available in a single location. Logging and diagnostic information must be reliably processed.
Security and Personally Identifiable Information (PII)
All access and usage of analysis results must be logged. Any unusual activity must be detected.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/logging https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor
QUESTION 313
You need to ensure that the data scientists can analyze the results.
What should you recommend?
A. Azure Analytics Services
B. Azure HDInsight
C. Azure Data Catalog
D. Azure SQL Data Warehouse
Answer: C
Explanation:
Azure Data Catalog is a fully managed cloud service whose users can discover the data sources they need and understand the data sources they find. At the same time, Data Catalog helps organizations get more value from their existing investments.
With Data Catalog, any user (analyst, data scientist, or developer) can discover, understand, and consume data sources.
The Azure Data Catalog can use an offline data source.
Scenario: Data scientists must be able to analyze results without changing the deployed application. The data scientists must be able to analyze results without being connected to the Internet.
Reference: https://docs.microsoft.com/en-us/azure/data-catalog/data-catalog-dsr
QUESTION 314
Drag and Drop Question
Testing has indicated there are performance issues with the user interface.
You need to recommend methods to improve the performance of the user interface.
What should you recommend? To answer, drag the appropriate technologies to the correct user interface components. Each technology 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.
Answer:
Explanation:
Box 1: Azure Search Index
In Azure Search, an index is a persistent store of documents and other constructs used by an Azure Search service. A document is a single unit of searchable data in your index. For example, an e-commerce retailer might have a document for each item they sell, a news organization might have a document for each article, and so forth. Mapping these concepts to more familiar database equivalents: an index is conceptually similar to a table, and documents are roughly equivalent to rows in a table.
When you add/upload documents and submit search queries to Azure Search, you submit your requests to a specific index in your search service.
Scenario: Document Inventory: This page shows a list of all processed documents provided by a customer. The page can be configured to show documents for a selected category.
Box 2: Azure Cosmos DB Index
Azure Cosmos DB supports indexing and querying of geospatial point data that’s represented using the GeoJSON specification. GeoJSON data structures are always valid JSON objects, so they can be stored and queried using Azure Cosmos DB without any specialized tools or libraries. The Azure Cosmos DB SDKs provide helper classes and methods that make it easy to work with spatial data.
Scenario: The solution uses Cosmos DB for data storage.
Document Upload Sources: This page shows a map and information about the geographic distribution of uploaded documents. This page allows users to filter the map based on assigned categories.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-what-is-an-index
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