Mastering Microsoft Azure AI Solutions
Table of Contents
- Introduction
- Question 1: Custom Vision Service Metrics
- Question 2: Text Analytics REST API Endpoint
- Question 3: Responsible AI Principle
- Question 4: Language Understanding Model Training
- Question 5: Entity for Billing Addresses
- Question 6: Enabling Speech Capabilities for a Chatbot
- Question 7: Translating Language Understanding Model
- Question 8: Enrichment Pipeline for Optical Character Recognition
- Question 9: Creating Knowledge Bases
- Question 10: Deploying a Bot to Azure
- Conclusion
Article
Introduction
In the world of AI, having the skills to design and implement a Microsoft Azure AI solution is highly valuable. The AI-102 exam is the perfect opportunity to showcase your abilities and test your knowledge. In this article, we will explore some of the updated Microsoft AI-102 exam questions to help you prepare for this important step in your career advancement.
Question 1: Custom Vision Service Metrics
One of the functionalities of the Custom Vision Service is building a classifier. After training the classifier, it is crucial to evaluate its performance. This leads us to the question: Which two metrics are available for reviewing the classifier?
The two metrics available for evaluating the classifier are:
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Recall: This metric measures the fraction of actual positive cases that were correctly identified as positive by the classifier. It helps assess the classifier's ability to identify all positive cases.
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F-score: The F-score is the harmonic mean of precision and recall. It provides a balanced measure of the classifier's performance, considering both false positives and false negatives.
Question 2: Text Analytics REST API Endpoint
Generating a word cloud Based on a company's product reviews requires utilizing the appropriate Text Analytics REST API endpoint. Which endpoint should You use for this task?
To generate a word cloud based on product reviews, you should use the "IC Phrases" endpoint of the Text Analytics REST API. This endpoint helps extract key phrases from the reviews, allowing you to analyze and Visualize the most commonly Mentioned terms.
Question 3: Responsible AI Principle
When developing a new sales system that processes user data from a public-facing Website, it is essential to uphold responsible AI principles. In this Context, which principle does notifying users about their data being processed by the sales system help meet?
By notifying users about the processing of their data, you adhere to the principle of transparency. Transparency ensures that users are aware of how their data is being used, fostering trust and accountability.
Question 4: Language Understanding Model Training
Training a language understanding model for a user support system requires careful consideration. Let's explore how to decrease the likelihood of a false positive in this context.
To decrease the likelihood of a false positive in a language understanding model, you should add examples to the "Get Contact Details" intent. By providing additional examples, you enhance the model's understanding of this specific intent, reducing the chances of misinterpreting user input.
Question 5: Entity for Billing Addresses
When building a language understanding model for an e-commerce platform, capturing billing addresses is crucial. Which entity Type should you use for this purpose?
To capture billing addresses effectively, you should utilize the "Geography v2" entity type. This entity type is specifically designed to recognize and extract geographical information, making it ideal for identifying addresses.
Question 6: Enabling Speech Capabilities for a Chatbot
Enabling speech capabilities for a chatbot enhances user interaction and accessibility. Which three actions should you perform to achieve this?
To enable speech capabilities for a chatbot, you need to:
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Enable websockets for the chatbot app: This ensures real-time communication between the chatbot and the client, enabling speech functionality.
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Create a speech service: Setting up a speech service allows the chatbot to convert text to speech and vice versa, enabling natural language understanding and interaction.
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Register a Direct Line Speech Channel: This channel allows integration with various speech-enabled platforms and devices, expanding the chatbot's reach.
Question 7: Translating Language Understanding Model
When building a bot using the Microsoft Bot Framework on a local computer, utilizing an existing language understanding model requires some additional steps. What should you do first to translate the language understanding model locally?
To translate the language understanding model locally using the Bot Framework CLI, the first step is to export the model as an NLU (Natural Language Understanding) file. This file can then be used for local translation with the CLI.
Question 8: Enrichment Pipeline for Optical Character Recognition
Making scanned documents' text available through Azure Cognitive Search requires configuring an enrichment pipeline. This pipeline should perform optical character recognition (OCR) and text analytics while minimizing costs. What should you attach to the skill set to achieve this?
To set up the enrichment pipeline for OCR and text analytics with minimal costs, you should attach a "Free limited enrichments cognitive services" set to the skill set. This ensures cost-effective utilization of cognitive services while performing the necessary OCR and text analytics tasks.
Question 9: Creating Knowledge Bases
Building an Azure web lab that creates knowledge bases from URLs necessitates developing a suitable method. Let's explore the necessary actions to create knowledge bases.
To create knowledge bases in the Azure web lab, you should include the following two actions in the method:
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Create a list of File DTO objects representing data from the web job: This list will contain the necessary information from the web job to populate the knowledge bases.
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Call the client.KnowledgeBase.CreateAsync
method: This step invokes the method from the QnA Maker client object, creating the knowledge bases based on the provided data.
Question 10: Deploying a Bot to Azure
Deploying a bot to Azure involves registering the bot using the Bot Channels Registration service. What values are required to complete the deployment?
To complete the deployment process, the following two values are required:
-
Bot ID (b o t l d
): This is the unique identifier for the bot, ensuring its proper registration and integration with Azure services.
-
App password (a p p l d d
): The app password acts as a security measure, authenticating the bot and allowing it to access the desired resources on Azure.
Conclusion
Preparing for the Microsoft AI-102 exam requires a thorough understanding of various concepts and skills related to Azure AI solutions. By exploring these updated exam questions and their solutions, you can enhance your knowledge and readiness for the exam. Remember to practice and familiarize yourself with the Relevant topics to ensure success.
Highlights
- Explore updated Microsoft AI-102 exam questions for comprehensive preparation.
- Understand the available metrics for evaluating a classifier in the Custom Vision Service.
- Discover the appropriate Text Analytics REST API endpoint for generating a word cloud based on product reviews.
- Learn about responsible AI principles and how transparency can be achieved in the context of a sales system.
- Decrease the likelihood of false positives in language understanding model training using specific strategies.
- Identify the suitable entity type for capturing billing addresses in an e-commerce platform.
- Enable speech capabilities for chatbots by following the recommended actions.
- Translate language understanding models locally using the Bot Framework CLI.
- Configure an enrichment pipeline for OCR and text analytics while minimizing costs in Azure Cognitive Search.
- Develop a method for creating knowledge bases in an Azure web lab.
- Register a bot and deploy it to Azure with the required values.
FAQ
Q: How can I evaluate the performance of a classifier in the Custom Vision Service?
A: You can use metrics like recall and F-score, which measure the classifier's ability to identify positive cases accurately.
Q: Which Text Analytics REST API endpoint should I use to generate a word cloud based on product reviews?
A: The "IC Phrases" endpoint allows you to extract key phrases from reviews for analysis and visualization.
Q: How does notifying users about their data being processed by a sales system Align with responsible AI principles?
A: This practice promotes transparency, informing users about the handling of their data to foster trust and accountability.
Q: What steps can I take to decrease the likelihood of false positives in a language understanding model?
A: Adding more examples to the specific intent you want to improve, such as the "Get Contact Details" intent, can reduce false positives.
Q: Which entity type should I use to capture billing addresses in an e-commerce platform language understanding model?
A: The "Geography v2" entity type is ideal for recognizing and extracting geographical information such as addresses.
Q: How can I enable speech capabilities for a chatbot?
A: You need to perform actions like enabling websockets, creating a speech service, and registering a Direct Line Speech channel.
Q: What is the first step to translate a language understanding model locally using the Bot Framework CLI?
A: Export the model as an NLU file from the language understanding portal.
Q: How can I configure an enrichment pipeline for OCR and text analytics in Azure Cognitive Search while minimizing costs?
A: You should attach a "Free limited enrichments cognitive services" set to the skill set.
Q: What actions should I include in a method to create knowledge bases in an Azure web lab?
A: Create a list of File DTO objects from the web job and call the "client.KnowledgeBase.CreateAsync" method.
Q: What values are required for deploying a bot to Azure using the Bot Channels Registration service?
A: The required values include the Bot ID and App password.