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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 processing architecture for a solution
- Identify components and technologies required to connect service endpoints
- Select the appropriate AI models and services
- Identify automation requirements
- Select the appropriate data processing technologies
Map security requirements to tools, technologies, and processes
- Identify auditing requirements
- Identify which users and groups have access to information and interfaces
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify appropriate tools for a solution
Select the software, services, and storage required to support a solution
- Identify storage required to store logging, bot state data, and Cognitive Services output
- Identify appropriate services and tools for a solution
- Identify integration points with other Microsoft services
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 call Azure Machine Learning models
- Design a strategy for ingest and egress data
- Design pipelines that use AI apps
- Define an AI application workflow process
- Design the integration point between multiple workflows and pipelines
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 with Azure app services and Azure Application Insights
- Design bot services that use Language Understanding (LUIS)
- Design bots that integrate with channels
- Integrate bots and AI solutions
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
- Design a content moderation strategy for data usage within an AI solution
- Ensure appropriate governance of data
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Define how users and applications will authenticate to AI services
- Ensure that data adheres to compliance requirements defined by your organization
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Develop streaming solutions
- Implement data logging processes
- Define and construct interfaces for custom AI services
- Manage the flow of data through the solution components
- Develop AI pipelines
- Create solution endpoints
Integrate AI services with solution components
- Configure prerequisite components to allow connectivity to the Bot Framework
- 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
Monitor and evaluate the AI environment
- Maintain an AI solution for continuous improvement
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Identify the differences between expected and actual workflow throughput
- Recommend changes to an AI solution based on performance data
- Monitor AI components for availability
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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary
The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
- United State - 120,000 USD
- England - 90,532 POUND
- India - 20,00,327 INR
- Europe - 90,547 EURO
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 2: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 4: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Deploy and manage generative models |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Topic 6: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Integrate vision capabilities into applications - Analyze images and detect objects/features - Build and deploy custom vision models - Process and index video content |
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