Prerequisites
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
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For more info read reference:
microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
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
- Identify automation requirements
- Select the appropriate data processing technologies
- Select the appropriate AI models and services
- Identify components and technologies required to connect service endpoints
- Select the processing architecture for a solution
Map security requirements to tools, technologies, and processes
- Identify appropriate tools for a solution
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify auditing requirements
- Identify which users and groups have access to information and interfaces
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
- Design a strategy for ingest and egress data
- Define an AI application workflow process
- Design pipelines that call Azure Machine Learning models
- Design the integration point between multiple workflows and pipelines
- Design pipelines that use AI apps
- Select an AI solution that meet cost constraints
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
- Design bots that integrate with channels
- Integrate bots with Azure app services and Azure Application Insights
- Integrate bots and AI solutions
- 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 use a cloud-based, on-premises, or hybrid compute infrastructure
- Identify whether to create a GPU, FPGA, or CPU-based solution
Design for data governance, compliance, integrity, and security
- Ensure appropriate governance of data
- 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
- 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
- Manage the flow of data through the solution components
- Implement data logging processes
- Develop AI pipelines
- Create solution endpoints
- Develop streaming solutions
- Define and construct interfaces for custom AI services
Integrate AI services with solution components
- Configure integration with Cognitive Services
- Configure prerequisite components to allow connectivity to the Bot Framework
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
- Implement Azure Search in a solution
Monitor and evaluate the AI environment
- Identify the differences between expected and actual workflow throughput
- Monitor AI components for availability
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Recommend changes to an AI solution based on performance data
- Maintain an AI solution for continuous improvement
Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions |
| Topic 2: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Topic 3: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Customize and deploy NLP models - Build conversational AI and chatbots |
| Topic 4: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Build and deploy custom vision models - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images |
| Topic 5: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback |
| Topic 6: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
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