Master Microsoft Azure AI with Real Questions!

Master Microsoft Azure AI with Real Questions!

Table of Contents:

1. Introduction
2. Implementing Autocompletion in the Smart Ecommerce Project
3. Processing Wiki Content with Azure Cognitive Search
4. Extracting Information from Receipts with Azure Cognitive Services
5. Enabling Optical Character Recognition and Text Analytics with Azure Cognitive Search
6. Building a Natural Language Model with Active Learning
7. Meeting the Knowledge Base Requirements with Azure Video Analyzer
8. Measuring Public Perception with Text Analytics
9. Adding Components to the Chatbot with QnA Maker and Azure Bot Framework
10. Searching Equivalent Terms in the Knowledge Base with Azure Cognitive Search
11. Enabling Speech Capabilities for Chatbots

Introduction

In this article, we will explore various topics related to Microsoft Azure AI solutions. We will discuss how to implement autocompletion in a smart ecommerce project, process wiki content using Azure Cognitive Search, extract information from receipts with Azure Cognitive Services, enable optical character recognition and text analytics, build a natural language model with active learning, meet the requirements of a knowledge base using Azure Video Analyzer, measure public perception with text analytics, add components to a chatbot using QnA Maker and Azure Bot Framework, search for equivalent terms in a knowledge base with Azure Cognitive Search, and enable speech capabilities for chatbots.

Implementing Autocompletion in the Smart Ecommerce Project

To enhance the search functionality in a smart ecommerce project, autocompletion can be implemented as part of the cognitive search solution. The following actions should be performed:

1. Make API queries to the autocomplete endpoint and include the suggester name in the body.
2. Add a suggester that has the three product name fields as source fields.
3. Set the search analyzer property for the three product name variants.

By following these steps, autocompletion can be successfully implemented in the smart ecommerce project, improving the search experience for users.

Processing Wiki Content with Azure Cognitive Search

When developing a knowledge base using Azure Cognitive Search, it is important to process wiki content to meet the technical requirements. The solution should include:

1. An indexer for Azure Cosmos DB attached to a skill set that contains the document extraction skill and the text translation skill. This allows for efficient processing and translation of the wiki content.

By including this solution, the wiki content can be effectively processed and made available for search in the knowledge base.

Extracting Information from Receipts with Azure Cognitive Services

To reduce the time it takes for employees to log receipts in expense reports, it is essential to extract top-level information from the receipts. Azure Cognitive Services offers several services that can be used for this purpose. The recommended service to use in this Scenario is Form Recognizer. By applying Form Recognizer, the vendor and the transaction total can be extracted from the receipts, minimizing development effort.

Enabling Optical Character Recognition and Text Analytics with Azure Cognitive Search

To make text from scanned documents available through Azure Cognitive Search, an enrichment pipeline needs to be configured. This pipeline should include optical character recognition (OCR) and text analytics. By attaching a new computer vision resource to the skill set, OCR and text analytics can be effectively performed during the enrichment process. This solution minimizes costs while providing accurate and enriched search results.

Building a Natural Language Model with Active Learning

Creating a robust natural language model is essential for various AI applications. To improve the model's performance, active learning techniques can be employed. One way to enable active learning is by enabling speech priming. By incorporating speech priming, the model can learn from user interactions and adapt its responses over time.

Meeting the Knowledge Base Requirements with Azure Video Analyzer

When developing a knowledge base, it is crucial to ensure that the solution meets the specific requirements. If the knowledge base includes transcripts of webinars obtained through Azure Video Analyzer, it is recommended to configure audio indexing for videos only. This ensures that only Relevant content is included in the knowledge base, enhancing its accuracy and usability.

Measuring Public Perception with Text Analytics

To measure the public perception of a brand on social media messages, Azure Cognitive Services' Text Analytics service can be utilized. Text Analytics provides sentiment analysis, allowing companies to gauge customer sentiments towards their brand. By analyzing social media messages, businesses can Gather valuable insights and make informed decisions to improve their brand reputation.

Adding Components to the Chatbot with QnA Maker and Azure Bot Framework

Developing a chatbot involves various components that enhance its functionalities. To meet technical and chatbot requirements, it is important to add an additional component. In this case, the recommended component to add is Dispatch. Dispatch helps in routing user queries to different services, improving the overall chatbot experience.

Searching Equivalent Terms in the Knowledge Base with Azure Cognitive Search

To provide accurate search results in a knowledge base, it is important to consider equivalent terms. To enable searching for equivalent terms, a synonym map should be included in the solution. By utilizing a synonym map, users can find relevant information even if they use different terms or synonyms.

Enabling Speech Capabilities for Chatbots

Enabling speech capabilities enhances the user experience of chatbots. To enable speech capabilities for a chatbot, the following actions should be performed:

1. Enable websockets for the chatbot app.
2. Create a speech service.
3. Register a Direct Line Speech Channel.

By following these steps, the chatbot will be able to communicate with users through voice interactions, expanding its usability and accessibility.

Highlights:

1. Implement autocompletion in a smart ecommerce project for improved search functionality.
2. Process wiki content using Azure Cognitive Search for an efficient knowledge base.
3. Extract information from receipts using Azure Cognitive Services to streamline expense reporting.
4. Enable optical character recognition and text analytics with Azure Cognitive Search for enriched search results.
5. Build a natural language model with active learning techniques for enhanced performance.
6. Meet knowledge base requirements with Azure Video Analyzer for accurate and relevant content.
7. Measure public perception with Text Analytics to gather insights on brand reputation.
8. Add components to a chatbot using QnA Maker and Azure Bot Framework for improved functionalities.
9. Search equivalent terms in a knowledge base with Azure Cognitive Search for accurate results.
10. Enable speech capabilities for chatbots to enhance user experience.

FAQ:

Q: What is autocompletion in a smart ecommerce project? A: Autocompletion refers to the feature that suggests search terms as users start typing, enhancing the search experience by providing relevant suggestions.

Q: How does Azure Cognitive Search process wiki content? A: Azure Cognitive Search processes wiki content by indexing it and applying various skills, such as language detection and text translation, to make it searchable.

Q: What information can be extracted from receipts using Azure Cognitive Services? A: Azure Cognitive Services can extract information such as the vendor and transaction total from receipts, making it easier to log expenses.

Q: How does active learning improve natural language models? A: Active learning techniques, such as speech priming, allow natural language models to learn from user interactions and adapt their responses over time for improved performance.

Q: How does Dispatch enhance a chatbot? A: Dispatch helps route user queries to different services, allowing chatbots to provide more accurate and specialized responses.

Q: What is a synonym map in the Context of knowledge bases? A: A synonym map allows users to search for equivalent terms or synonyms and find relevant information in a knowledge base.

Q: How can speech capabilities be enabled for chatbots? A: Speech capabilities can be enabled by incorporating websockets, creating a speech service, and registering a Direct Line Speech channel for voice interactions with chatbots.