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Skills measured

  • Implement natural language processing solutions (20-25%)
  • Implement knowledge mining solutions (15-20%)
  • Plan and manage an Azure Cognitive Services solution (15-20%)
  • Implement conversational AI solutions (15-20%)
  • Implement Computer Vision solutions (20-25%)

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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 appropriate data processing technologies
  • 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

Map security requirements to tools, technologies, and processes

  • Identify which users and groups have access to information and interfaces
  • Identify appropriate tools for a solution
  • Identify auditing requirements
  • Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements

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

  • Design pipelines that use AI apps
  • Design the integration point between multiple workflows and pipelines
  • Define an AI application workflow process
  • Design pipelines that call Azure Machine Learning models
  • Select an AI solution that meet cost constraints
  • Design a strategy for ingest and egress data

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
  • Design bot services that use Language Understanding (LUIS)
  • Integrate bots and AI solutions

Design the compute infrastructure to support a solution

  • Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
  • Identify whether to create a GPU, FPGA, or CPU-based solution
  • Select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

  • Ensure that data adheres to compliance requirements defined by your organization
  • Ensure appropriate governance of data
  • Design a content moderation strategy for data usage within an AI solution
  • Define how users and applications will authenticate to AI services
  • Design strategies to ensure that the solution meets data privacy regulations and industry standards

3. Implement and monitor AI solutions (25-30%)

Implement an AI workflow

  • Implement data logging processes
  • Manage the flow of data through the solution components
  • Define and construct interfaces for custom AI services
  • Develop AI pipelines
  • Create solution endpoints
  • Develop streaming solutions

Integrate AI services with solution components

  • Implement Azure Search in a solution
  • Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
  • Configure integration with Cognitive Services
  • 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
  • Identify the differences between expected and actual workflow throughput
  • Recommend changes to an AI solution based on performance data
  • Monitor AI components for availability
  • Maintain an AI solution for continuous improvement

Microsoft AI-102 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement computer vision solutions: This domain addresses image and video analysis using Azure Vision services, including object detection, text extraction, training custom vision models, and extracting insights from video streams.
Topic 2
  • Implement an agentic solution: This domain covers creating autonomous AI agents using Microsoft Foundry Agent Service and Framework, implementing multi-agent workflows, and testing and deploying intelligent agents.
Topic 3
  • Implement generative AI solutions: This domain focuses on building solutions with Microsoft Foundry and Azure OpenAI, including deploying models, implementing prompt flows and RAG patterns, generating content, and optimizing through parameter tuning and prompt engineering.
Topic 4
  • Implement knowledge mining and information extraction solutions: This domain covers extracting structured data from unstructured sources using Azure AI Search, Document Intelligence for form processing, and Content Understanding for OCR and document analysis.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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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

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