AI-102 Microsoft Azure AI Solution Certification Practice Exam is a comprehensive and reliable tool designed to help individuals prepare for the Microsoft Azure AI Solution Certification exam. This practice exam offers a range of benefits to users, including the opportunity to assess their knowledge and skills in the field of AI, gain confidence in their abilities, and identify areas for improvement.
With a focus on practical application and real-world scenarios, the AI-102 practice exam provides users with a realistic and challenging experience that closely mirrors the actual certification exam. This allows individuals to become familiar with the exam format, question types, and time constraints, enabling them to perform at their best on exam day.
In addition to its practical benefits, the AI-102 practice exam is also an excellent resource for individuals seeking to enhance their professional credentials and advance their careers in the field of AI. By earning the Microsoft Azure AI Solution Certification, individuals can demonstrate their expertise and proficiency in designing and implementing AI solutions using Microsoft Azure technologies, opening up new opportunities for career growth and advancement.
Microsoft Azure AI Solution Exam Summary:
Exam Name : Microsoft Azure AI Solution
Exam Code : AI-102
Exam Price : 165 (USD)
Number of Questions: Maximum of 40-60 questions,
Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,
Length of Test: 130 Minutes. The exam is available in English and Japanese languages.
Passing Score: 700 / 1000
Languages : English at launch. Japanese
Schedule Exam : Pearson VUE
Microsoft AI-102 Exam Syllabus Topics:
Plan and manage an Azure AI solution (25–30%)
Select the appropriate Azure AI service
Select the appropriate service for a vision solution
Select the appropriate service for a language analysis solution
Select the appropriate service for a decision support solution
Select the appropriate service for a speech solution
Select the appropriate Applied AI services
Plan and configure security for Azure AI services
Manage account keys
Manage authentication for a resource
Secure services by using Azure Virtual Networks
Plan for a solution that meets Responsible AI principles
Create and manage an Azure AI service
Create an Azure AI resource
Configure diagnostic logging
Manage costs for Azure AI services
Monitor an Azure AI resource
Deploy Azure AI services
Determine a default endpoint for a service
Create a resource by using the Azure portal
Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline
Plan a container deployment
Implement prebuilt containers in a connected environment
Create solutions to detect anomalies and improve content
Create a solution that uses Anomaly Detector, part of Cognitive Services
Create a solution that uses Azure Content Moderator, part of Cognitive Services
Create a solution that uses Personalizer, part of Cognitive Services
Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services
Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services
Implement image and video processing solutions (15–20%)
Analyze images
Select appropriate visual features to meet image processing requirements
Create an image processing request to include appropriate image analysis features
Interpret image processing responses
Extract text from images
Extract text from images or PDFs by using the Computer Vision service
Convert handwritten text by using the Computer Vision service
Extract information using prebuilt models in Azure Form Recognizer
Build and optimize a custom model for Azure Form Recognizer
Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services
Choose between image classification and object detection models
Specify model configuration options, including category, version, and compact
Label images
Train custom image models, including classifiers and detectors
Manage training iterations
Evaluate model metrics
Publish a trained iteration of a model
Export a model to run on a specific target
Implement a Custom Vision model as a Docker container
Interpret model responses
Process videos
Process a video by using Azure Video Indexer
Extract insights from a video or live stream by using Azure Video Indexer
Implement content moderation by using Azure Video Indexer
Integrate a custom language model into Azure Video Indexer
Implement natural language processing solutions (25–30%)
Analyze text
Retrieve and process key phrases
Retrieve and process entities
Retrieve and process sentiment
Detect the language used in text
Detect personally identifiable information (PII)
Process speech
Implement and customize text-to-speech
Implement and customize speech-to-text
Improve text-to-speech by using SSML and Custom Neural Voice
Improve speech-to-text by using phrase lists and Custom Speech
Implement intent recognition
Implement keyword recognition
Translate language
Translate text and documents by using the Translator service
Implement custom translation, including training, improving, and publishing a custom model
Translate speech-to-speech by using the Speech service
Translate speech-to-text by using the Speech service
Translate to multiple languages simultaneously
Build and manage a language understanding model
Create intents and add utterances
Create entities
Train evaluate, deploy, and test a language understanding model
Optimize a Language Understanding (LUIS) model
Integrate multiple language service models by using Orchestrator
Import and export language understanding models
Create a question answering solution
Create a question answering project
Add question-and-answer pairs manually
Import sources
Train and test a knowledge base
Publish a knowledge base
Create a multi-turn conversation
Add alternate phrasing
Add chit-chat to a knowledge base
Export a knowledge base
Create a multi-language question answering solution
Create a multi-domain question answering solution
Use metadata for question-and-answer pairs
Implement knowledge mining solutions (5–10%)
Implement a Cognitive Search solution
Provision a Cognitive Search resource
Create data sources
Define an index
Create and run an indexer
Query an index, including syntax, sorting, filtering, and wildcards
Manage knowledge store projections, including file, object, and table projections
Apply AI enrichment skills to an indexer pipeline
Attach a Cognitive Services account to a skillset
Select and include built-in skills for documents
Implement custom skills and include them in a skillset
Implement incremental enrichment
Implement conversational AI solutions (15–20%)
Design and implement conversation flow
Design conversational logic for a bot
Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot
Build a conversational bot
Create a bot from a template
Create a bot from scratch
Implement activity handlers, dialogs or topics, and triggers
Implement channel-specific logic
Implement Adaptive Cards
Implement multi-language support in a bot
Implement multi-step conversations
Manage state for a bot
Integrate Cognitive Services into a bot, including question answering, language understanding,
and Speech service
Test, publish, and maintain a conversational bot
Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app
Test a bot in a channel-specific environment
Troubleshoot a conversational bot
Deploy bot logic
Overall, the AI-102 Microsoft Azure AI Solution Certification Practice Exam is an essential tool for anyone seeking to achieve certification in this rapidly growing field. With its comprehensive coverage, practical focus, and numerous benefits, this practice exam is an invaluable resource for individuals looking to take their AI skills and knowledge to the next level.