How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam
Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam
Introduction
Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.
This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.
For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.
Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.
In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
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 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 integration points with other Microsoft services
- Identify storage required to store logging, bot state data, and Cognitive Services output
- Identify appropriate services and tools for a solution
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Design pipelines that use AI apps
- Define an AI application workflow process
- Design a strategy for ingest and egress data
- Design pipelines that call Azure Machine Learning models
- Design the integration point between multiple workflows and pipelines
- 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
- Integrate bots and AI solutions
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
- Design bot services that use Language Understanding (LUIS)
Design the compute infrastructure to support a solution
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Select a compute solution that meets cost constraints
- 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
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure appropriate governance of data
- Ensure that data adheres to compliance requirements defined by your organization
- Define how users and applications will authenticate to AI services
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Manage the flow of data through the solution components
- Create solution endpoints
- Implement data logging processes
- Develop streaming solutions
- Define and construct interfaces for custom AI services
- Develop AI pipelines
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
- Recommend changes to an AI solution based on performance data
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Maintain an AI solution for continuous improvement
- Identify the differences between expected and actual workflow throughput
- Monitor AI components for availability
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