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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.
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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
- Type of Questions: This test format is multiple choice.
- This is beta exam.
- Passing Score: 700 / 1000
- Length of Examination: 50 mins
- Language: English, Japanese, Chinese (Simplified), Korean.
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 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
- Identify appropriate tools for a solution
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 integration points with other Microsoft services
- 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 call Azure Machine Learning models
- Define an AI application workflow process
- Design pipelines that use AI apps
- Select an AI solution that meet cost constraints
- Design the integration point between multiple workflows and pipelines
- 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 bot services that use Language Understanding (LUIS)
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
- Integrate bots and AI solutions
Design the compute infrastructure to support a solution
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- 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
- Define how users and applications will authenticate to AI services
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure appropriate governance of data
- Design a content moderation strategy for data usage within an AI solution
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Develop AI pipelines
- Define and construct interfaces for custom AI services
- Manage the flow of data through the solution components
- Create solution endpoints
- Develop streaming solutions
- Implement data logging processes
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
- Configure prerequisite components to allow connectivity to the Bot Framework
- Implement Azure Search in a solution
Monitor and evaluate the AI environment
- 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
- Identify the differences between KPIs, reported metrics, and root causes of the differences
How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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