AI-102 EXAM QUESTIONS 2024 MICROSOFT AZURE AI 102 CERTIFICATION COURSE PART-11

The speaker begins by congratulating Harit Singh for successfully clearing the AI1 exam with a great score. They emphasize the importance of paying attention to the content covered in previous videos as the questions in the AI exams are rapidly changing. The speaker introduces a new topic about a method using Azure AI Vision client library for OCR in images. A problem is identified where the 'get read results' method is called before the OCR operation is complete, causing errors. The solution involves adding code to verify the status of the read results, ensuring the operation is complete before proceeding, and placing the 'get read result' call within a loop that checks for completion.

The speaker discusses building a text-to-speech app using a custom neural voice and the creation of an SSML file. They explain that SSML, or Speech Synthesis Markup Language, allows for control over various aspects of speech synthesis, including pronunciation, volume, speed, and emphasis. The app must meet certain requirements, such as expressing a calm tone and imitating the voice of a young adult female. The speaker provides guidance on using the 'voice' element in SSML with attributes like 'name' for selecting a pre-built voice, 'effect' for optimizing audio quality in specific scenarios, 'style' for expressing emotions, and 'role' for imitating different ages or genders.

The speaker continues the discussion on SSML, focusing on optimizing the quality of synthesized voice output for use in motor vehicles. They mention the use of the 'effect' attribute within the 'voice' element, specifically setting it to 'EQ car' to adjust the audio for car speaker environments. The speaker provides examples and clarifies the importance of understanding the SSML attributes for optimizing speech output in different scenarios.

The speaker addresses the development of an app using Azure AI Vision API to analyze images and determine if they are clip art or line drawings. They discuss the correct use of HTTP methods for API requests, suggesting the POST method for sending image data to the server for analysis. The speaker also explains how to configure the request to identify image types by setting the 'visualFeatures' parameter to 'imageType'.

The speaker talks about ensuring the ability to search across various data sources using Azure AI Search API. They suggest migrating on-premises data to Azure-based storage services to leverage cloud indexing and search capabilities. The focus is on migrating finance data to Azure Data Lake Storage and sales data to Cosmos DB, as these services integrate well with Azure Search for efficient data retrieval.

The speaker provides instructions for deploying an Azure resource for sentiment analysis in the East US region. They detail the parameters needed to call the 'create_resource' method, including the resource name, kind, account tier, and location. The speaker emphasizes choosing the 'Text Analytics' kind and the 'S0' tier for a balance between cost and capabilities, and correctly formatting the region as 'eastus'.

The speaker discusses the implementation of responsible AI principles in a new sales system that processes video and text data. They highlight the importance of fairness and inclusiveness to ensure equitable results regardless of user location or background. The speaker explains that while transparency, reliability, safety, privacy, and security are important, the focus for this system is on fairness and inclusiveness to avoid bias in the results.

The speaker concludes with a scenario involving 50,000 scanned documents that need to be made text-searchable through cognitive search with an emphasis on minimizing costs. They suggest attaching a 'Free limited enrichment cognitive services' skill set to the enrichment pipeline for performing OCR and text analytics, acknowledging the limitations of the free version but its suitability for the task given the size of the document collection.