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.
Microsoft AI-102日本語 Exam Syllabus Topics:
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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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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 processing architecture for a solution
- Identify components and technologies required to connect service endpoints
- Select the appropriate AI models and services
Map security requirements to tools, technologies, and processes
- Identify appropriate tools for a solution
- 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 auditing requirements
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 the integration point between multiple workflows and pipelines
- Select an AI solution that meet cost constraints
- Design pipelines that call Azure Machine Learning models
- Design pipelines that use AI apps
- Design a strategy for ingest and egress data
- Define an AI application workflow process
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 bot services that use Language Understanding (LUIS)
- Integrate bots and AI solutions
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
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 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
- Implement data logging processes
- Develop streaming solutions
- Manage the flow of data through the solution components
- Define and construct interfaces for custom AI services
- Create solution endpoints
- Develop AI pipelines
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
- Recommend changes to an AI solution based on performance data
- Identify the differences between expected and actual workflow throughput
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Monitor AI components for availability
- Maintain an AI solution for continuous improvement
