AI-102 EXAM QUESTIONS 2024 MICROSOFT AZURE AI 102 CERTIFICATION COURSE PART-11
TLDRThis episode of the AI 102 question series congratulates Harit Singh for clearing the AI1 exam and discusses Azure AI Vision client Library's OCR method issues. It provides solutions for ensuring the method retrieves results only after the read operation is complete. The video also covers creating an SSML file for a text-to-speech app, optimizing voice output for motor vehicles, and configuring API requests for image analysis. It concludes with advice on deploying Azure resources for sentiment analysis and ensuring equitable AI results, wrapping up with a guide on configuring an enrichment pipeline for OCR and text analytics to minimize costs.
Takeaways
- π Congratulating Harit Singh for clearing the AI1 exam with a great score and emphasizing the importance of watching the latest videos for updated content.
- π Discussing the issue of calling 'get read results' before the OCR operation is complete and the need to check the status of the read operation to prevent errors.
- π οΈ Suggesting two actions to ensure the 'get read result' method only proceeds after the read operation is complete, which are verifying the read result status value and checking the operation location URL.
- π Explaining the use of SSML (Speech Synthesis Markup Language) for controlling aspects of speech synthesis like pronunciation, volume, speed, and emphasis in a text-to-speech app.
- ποΈ Describing how to create an SSML file for a text-to-speech app that expresses a calm tone and imitates the voice of a young adult female using specific attributes.
- π Advising on optimizing the quality of synthesized voice output for use in motor vehicles by configuring the 'effect' attribute in the voice element of SSML.
- πΌοΈ For an app using Azure AI Vision API, explaining how to configure a request to identify whether an image is clip art or a line drawing using the appropriate HTTP method and visual features.
- π Discussing the migration of on-premises data to Azure data storage services to enable cloud-based indexing and search capabilities for Azure AI Search.
- π€ Highlighting the importance of deploying Azure resources correctly by selecting the right service, tier, and region for sentiment analysis in a new sales system.
- π Outlining the six guiding principles of responsible AI and emphasizing the principles of fairness and inclusiveness for monitoring a sales system to ensure equitable results.
- π For a large collection of scanned documents, suggesting the configuration of an enrichment pipeline with a cognitive service that minimizes cost while performing OCR and text analytics.
Q & A
What is the significance of the congratulatory message for Harit Singh at the beginning of the video?
-The congratulatory message for Harit Singh is significant as it acknowledges his achievement of clearing the AI1 exam with a great score. It serves to motivate other students and emphasizes the effectiveness of the study materials and methods discussed in the video series.
Why is it important to watch the latest videos in the playlist when preparing for the AI exam?
-It is important to watch the latest videos in the playlist because the questions for the AI exam are constantly being updated and shuffled. The instructor adds new questions to the playlist, and watching the latest videos ensures that the students are well-prepared with the most current information.
What is the issue with the method performing OCR in images using the Azure AI Vision client library as described in the script?
-The issue with the method is that the call to get read results occurs before the read operation is complete, which may cause errors or issues in the code. This is because the method is attempting to retrieve the results before the OCR process has finished.
What are the two actions that should be performed to prevent the 'get read result' method from proceeding until the read operation is complete?
-The two actions that should be performed are: 1) Add code to verify the read results do status value to ensure the operation has reached a succeeded state before proceeding, and 2) Call to read 'get read result' within a loop that contains a delay, ensuring that the code execution only proceeds after the read result status is equal to 'succeeded'.
What is SSML and why is it used in the context of the text-to-speech app discussed in the video?
-SSML, or Speech Synthesis Markup Language, is a powerful markup language used to control various aspects of speech synthesis, including pronunciation, volume, speed, and emphasis. In the context of the text-to-speech app, SSML is used to ensure that the voice profile meets specific requirements, such as expressing a calm tone and imitating the voice of a young adult female.
How can the SSML 'voice' element be utilized to meet the requirement of expressing a calm tone in a text-to-speech app?
-The 'voice' element in SSML can be utilized by setting the 'name' attribute to the desired pre-built voice and using the 'style' attribute to specify the speaking style, such as 'calm'. Additionally, the 'style degree' attribute can be adjusted to control the intensity of the speaking style.
What should be the value of the 'role' attribute in the SSML 'voice' element to imitate the voice of a young adult female?
-The value of the 'role' attribute in the SSML 'voice' element should be set to 'YoungAdultFemale' to imitate the voice of a young adult female.
What is the correct HTTP method to use when configuring a request to analyze an image using the Azure AI Vision API?
-The correct HTTP method to use for sending data to the server for analysis is POST, as it is used to create or update resources on the server.
What should be the value of the 'visualFeatures' parameter when configuring a request to identify whether an image is clip art or a line drawing using the Azure AI Vision API?
-The value of the 'visualFeatures' parameter should be 'imageType' to analyze the type of image and determine if it is clip art or a line drawing.
What should be done to ensure that all data sources can be searched using Azure AI Search (Cognitive Search)?
-To ensure all data sources can be searched using Azure AI Search, the data from on-premises SQL Server databases should be migrated to Azure data storage services like Cosmos DB or Table Storage, which can be easily indexed and searched by the cloud-based search service.
What should be attached to the skillset in an enrichment pipeline to minimize cost when processing a large collection of scanned documents for OCR and text analytics?
-To minimize cost, a 'free limited enrichment cognitive Services source' should be attached to the skillset, which provides limited access to cognitive search capabilities suitable for handling large volumes of documents.
Outlines
π Congratulating Harit Singh on AI1 Exam Success
The script starts by congratulating Harit Singh for successfully clearing the AI1 exam with a great score. It emphasizes the importance of watching the latest videos in the playlist for the AI 102 question series, as questions are updated frequently. The speaker mentions that the content covered includes open AI content moderator and other relevant topics, and advises viewers to watch the videos in sequence for the best understanding.
π Troubleshooting Azure AI Vision Client Library for OCR
The second paragraph discusses a technical issue encountered while using Azure AI Vision client library for performing OCR on images. The problem is that the 'get read results' method is called before the OCR operation is complete, causing errors. The speaker suggests two corrective actions: verifying the status of the read results before proceeding and ensuring that the 'get read result' method is only called after the OCR operation has completed successfully.
π Building a Text-to-Speech App with SSML
In this paragraph, the script addresses the creation of a text-to-speech app using a custom neural voice. It explains the importance of SSML (Speech Synthesis Markup Language) for controlling pronunciation, volume, speed, and emphasis in speech synthesis. The focus is on creating an SSML file that meets the requirement of expressing a calm tone with the voice of a young adult female, including how to use voice elements and attributes to customize the voice profile.
π Optimizing Speech Output for Motor Vehicles with SSML
The script continues with the topic of optimizing the quality of synthesized voice output for use in motor vehicles. It explains the use of the 'effect' attribute within the voice element in SSML to tailor the auditory experience for different scenarios, such as car speakers, which might require specific audio effects to compensate for environmental factors like room reverberation and background noise.
πΌοΈ Analyzing Images with Azure AI Vision API
This paragraph discusses the development of an app that uses the Azure AI Vision API to analyze images and identify whether they are clip art or line drawings. It covers the correct use of HTTP methods (specifically POST) for sending image data to the API for analysis and the configuration of the request to utilize the 'image type' feature of the API to determine the type of the image.
π Enabling Azure AI Search Across Diverse Data Sources
The focus of this paragraph is on ensuring that all data from various sources can be searched using Azure AI Search API. It suggests migrating on-premises data to cloud-based storage services like Azure Data Lake Storage to enable cloud-based indexing and search capabilities, integrating well with Azure Search and facilitating the search process across different data sources.
π€ Deploying Azure Resources for Sentiment Analysis
The script provides guidance on deploying an Azure resource for sentiment analysis in the East US region. It outlines the correct parameters for calling the 'create resource' method in Python, including the resource name, kind, account tier (S0), and location (EastUs), emphasizing the importance of selecting the appropriate tier and region for the task.
π Applying Responsible AI Principles for Equitable Results
The final paragraph discusses the importance of applying responsible AI principles to ensure equitable results from a sales system that processes video and text data. It highlights the principles of fairness and inclusiveness as key to providing consistent results regardless of user location or background, and briefly touches on other principles such as transparency, reliability, privacy, and security.
π Configuring Enrichment Pipeline for OCR and Text Analytics
The script concludes with a scenario of making scanned documents text-searchable through cognitive search, with an emphasis on minimizing costs. It suggests attaching a 'Free limited enrichment cognitive services' skill set to the enrichment pipeline for performing OCR and text analytics on a large collection of documents, balancing cost-effectiveness with the required processing capabilities.
Mindmap
Keywords
π‘Azure AI
π‘OCR (Optical Character Recognition)
π‘SSML (Speech Synthesis Markup Language)
π‘API (Application Programming Interface)
π‘Cognitive Search
π‘Enrichment Pipeline
π‘Content Moderator
π‘Sentiment Analysis
π‘Responsible AI
π‘Pricing Tier
Highlights
Congratulations to Harit Singh for successfully clearing the AI1 exam with a great score.
The importance of watching the latest videos in the playlist for updated questions.
Explanation of an issue where the get read results method is called before the read operation is complete in Azure AI Vision client Library.
Two actions to prevent the get read result method from proceeding until the read operation is complete.
The significance of verifying the read result status value to ensure the operation has reached a succeeded state.
Discussion on creating an SSML file for a text to speech app using a custom neural voice.
SSML's role in controlling various aspects of speech synthesis including pronunciation, volume, speed, and emphasis.
How to use the 'voice' element in SSML to select a specific voice for text to speech conversion.
The use of 'effect' attribute in SSML to optimize the auditory experience in specific scenarios like car speakers.
The 'style' attribute in SSML to express certain emotions like calmness in the synthesized speech.
Optimizing the quality of synthesized voice output in motor vehicles using SSML attributes.
The correct use of HTTP methods like POST for sending image data to Azure AI Vision API for analysis.
Identifying the correct visual features to configure in a request to Azure AI Vision API to analyze image types.
Migrating on-premises data to Azure data services to enable cloud-based indexing and search capabilities.
The process of deploying an Azure resource for sentiment analysis in the East US region using a Python method.
The six guiding principles of Microsoft AI and their role in ensuring equitable results in AI systems.
Configuring an enrichment pipeline for OCR and text analytics with cost minimization in mind.
The selection of the appropriate cognitive service in the skill set for processing a large collection of scanned documents.