Topics of AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates should apprehend the examination topics before they begin of preparation. because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:
1. Analyze solution requirements (25-30%)
Recommend Cognitive Services APIs to meet business requirements
- Select the appropriate AI models and services
- Identify components and technologies required to connect service endpoints
- Identify automation requirements
- Select the processing architecture for a solution
- Select the appropriate data processing technologies
Map security requirements to tools, technologies, and processes
- Identify auditing requirements
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify which users and groups have access to information and interfaces
- Identify appropriate tools for a solution
Select the software, services, and storage required to support a solution
- Identify integration points with other Microsoft services
- Identify appropriate services and tools for a solution
- Identify storage required to store logging, bot state data, and Cognitive Services output
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Select an AI solution that meet cost constraints
- Design pipelines that use AI apps
- Define an AI application workflow process
- Design a strategy for ingest and egress data
- Design the integration point between multiple workflows and pipelines
- Design pipelines that call Azure Machine Learning models
Design solutions that uses Cognitive Services
- Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs
Design solutions that implement the Bot Framework
- Integrate bots and AI solutions
- Design bots that integrate with channels
- Integrate bots with Azure app services and Azure Application Insights
- Design bot services that use Language Understanding (LUIS)
Design the compute infrastructure to support a solution
- Select a compute solution that meets cost constraints
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
Design for data governance, compliance, integrity, and security
- Ensure that data adheres to compliance requirements defined by your organization
- Design a content moderation strategy for data usage within an AI solution
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure appropriate governance of data
- Define how users and applications will authenticate to AI services
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Implement data logging processes
- Create solution endpoints
- Develop AI pipelines
- Manage the flow of data through the solution components
- Define and construct interfaces for custom AI services
- Develop streaming solutions
Integrate AI services with solution components
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Configure integration with Cognitive Services
- Implement Azure Search in a solution
- Configure prerequisite components to allow connectivity to the Bot Framework
Monitor and evaluate the AI environment
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Maintain an AI solution for continuous improvement
- Monitor AI components for availability
- Recommend changes to an AI solution based on performance data
- Identify the differences between expected and actual workflow throughput
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers |
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Select suitable AI models |
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Process and index video content - Extract text and handwriting from images - Analyze images and detect objects/features - Integrate vision capabilities into applications |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Build conversational AI and chatbots |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
