Master AI-102 Exam: Real Questions & Answers

Master AI-102 Exam: Real Questions & Answers

Table of Contents

  1. Introduction
  2. Building a Chatbot
    1. Requirements
    2. Integrating the Chatbot
  3. Reducing Employee Effort in Expense Reports
    1. Receipt Extraction
    2. Azure Service for Minimizing Development Effort
  4. Ensuring Equitable Results in the Sales System
    1. Responsible AI Principles
    2. Monitoring the Sales System
  5. Containerized Anomaly Detector Deployments
    1. Requirements for Containerized Deployments
    2. Ensuring Secure and Controlled Deployments
  6. Training a Custom Form Recognizer Model
    1. Model Training Process
    2. Sample Files for Training
  7. Server-Side Encryption for Azure Cognitive Search
    1. Benefits of Server-Side Encryption
    2. Implications of Enabling Customer Managed Keys
  8. Predictive Maintenance with Anomaly Detector
    1. Monitoring Industrial Machines
    2. Choosing the Right Azure Service
  9. Streaming Speech-to-Text Solution
    1. Converting Speech to Text
    2. Method for Streaming MP3 Data
  10. Internet-Based Training Solution
    1. Language Support for Remote Learners
    2. Cognitive Service for Personalized Training
  11. Provisioning a Q&A Maker Service
    1. Resource Creation in a New Resource Group
    2. Azure Resources Created for Q&A Maker
  12. Building a Language Model with Language Understanding
    1. Adding Contributors to the Language Model
    2. Managing Access to Authoring Resources
  13. Throttling Reduction in Azure Cognitive Search
    1. Effective Solutions for Throttling Issues
    2. Migration to a High-Tier Cognitive Search Service
  14. Developing an Automated Call Handling System
    1. Recognizing Different Languages
    2. Verifying Learner Engagement
  15. Image Training for Custom Form Recognizer Model
    1. Training Data Set Criteria
    2. Selection of Files for Model Training
  16. Configuring Connectivity for Azure Cognitive Search
    1. Connecting Web App to Azure Virtual Network
    2. Ensuring Private Endpoint and Connectivity
  17. Language Model Development with Language Understanding
    1. Developing a Conversational Language Model
    2. Enabling Inclusiveness and Safety in AI Systems
  18. Cognitive Services Selection for Support Tickets
    1. Language Understanding for Incoming Emails
    2. Language Detection and Response in Multiple Languages
  19. Monitoring Factory Components with Cognitive Services
    1. Object Detection and Labeling for Safety Compliance
    2. Choosing the Right Azure Cognitive Service
  20. Smart Cropping Feature for Custom Vision Model
    1. Custom Vision Resource for Image Resizing
    2. Configuration of API URL for Smart Cropping
  21. Integrating Cognitive Insights and Video Player Widgets
    1. Keyword Search and Display of People
    2. Caption Language and Widget Integration
  22. Object Detection Model Deployment between Resources
    1. Moving Object Detection Model to Production Environment
    2. API Endpoints for Model Deployment
  23. Training Custom Vision Object Detection Model
    1. Actions to Perform for Model Training
    2. Sequence of Actions for Custom Model Deployment
  24. Optical Character Recognition in Images
    1. Performing OCR with Computer Vision
    2. Deployment Options for Sensitive Documents
  25. Adding Images to Custom Vision Classifier
    1. Process for Adding New Images to Classifier
    2. Extending and Retraining the Classifier
  26. Facial Recognition and Detection in Images
    1. Face API for Facial Recognition
    2. Detection of Inappropriate Images
  27. Performance Metrics for Custom Vision Model
    1. Evaluation Metrics for Model Performance
    2. Importance of Recall and Precision
  28. Performing OCR with Computer Vision Client Library
    1. Preventing Premature Execution of Read Operation
    2. Controlling Read Result Status and Operation
  29. Searching for Photos by Image Example
    1. Finding Similar Faces in Existing List
    2. Matching Faces and Person Verification
  30. Adding Multiple Images to Person Group
    1. Procedure for Adding Images to Person Group
    2. Creating Persons for Face Recognition
  31. Preventing Premature Execution of Read Operation
    1. Controlling Read Result Status
    2. Wrapping Get Read Result in a Loop for Delay
  32. Building OCR Solution for Scanning Sensitive Documents
    1. Options for an On-Premises Solution
    2. Hosting the Computer Vision Endpoint in a Container
  33. Updating Custom Vision Classifier with New Images
    1. Adding New Images to Existing Model
    2. Retraining the Model for Improved Accuracy
  34. Training Custom Vision Model with Image Data
    1. Efficient Training with Locally Grouped Images
    2. Manual Tagging and Review of Image Suggestions
  35. Mobile App Development with Custom Vision
    1. Retraining Model with Additional Images
    2. Extending the Model for Improved Accuracy
  36. Applying Optical Character Recognition in Images
    1. Extracting Text from Images Using Computer Vision
    2. Containerizing the Computer Vision Endpoint on an On-Premises Server

Building a Chatbot

Chatbots have become increasingly popular for providing automated customer support and interaction. In this section, we will explore the requirements for building a chatbot and how to integrate various services to meet those requirements.

Requirements

When building a chatbot, it is important to consider the following requirements:

  • Support for chit chat and a knowledge base
  • Multilingual capabilities
  • Sentiment analysis on user messages
  • Automatic language model selection

Integrating the Chatbot

To meet these requirements, it is recommended to integrate the following services into the chatbot:

  • Language Understanding: This service allows users to Interact with the chatbot using natural language.
  • Q&A Maker: A cloud-based natural language processing service that helps find the most appropriate answers from a knowledge base.
  • Text Analytics: This service can be used to mine insights from unstructured text, including sentiment analysis.
  • Translator: Translation service that enables multilingual capabilities.

By integrating Language Understanding, Q&A Maker, Text Analytics, and Translator into the chatbot, You can Create a comprehensive and intelligent conversational agent that can support chit chat, provide knowledge-based responses, perform sentiment analysis, and automatically select the best language model for communication.

These integrations will ensure that the chatbot meets the specified requirements and provides an enhanced user experience.

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