Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam
The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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 automation requirements
- Identify components and technologies required to connect service endpoints
- Select the appropriate data processing technologies
- Select the appropriate AI models and services
Map security requirements to tools, technologies, and processes
- Identify auditing requirements
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify appropriate tools for a solution
- 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
- Select an AI solution that meet cost constraints
- Design pipelines that call Azure Machine Learning models
- Design pipelines that use AI apps
- Design the integration point between multiple workflows and pipelines
- 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)
- Design bots that integrate with channels
- Integrate bots and AI solutions
- Integrate bots with Azure app services and Azure Application Insights
Design the compute infrastructure to support a solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Select a compute solution that meets cost constraints
- Identify whether to create a GPU, FPGA, or CPU-based solution
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 that data adheres to compliance requirements defined by your organization
- Define how users and applications will authenticate to AI services
- Ensure appropriate governance of data
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Define and construct interfaces for custom AI services
- Develop streaming solutions
- Implement data logging processes
- 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
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Recommend changes to an AI solution based on performance data
- Monitor AI components for availability
- Identify the differences between expected and actual workflow throughput
- Maintain an AI solution for continuous improvement
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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.
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