Microsoft AI-102 : Designing and Implementing a Microsoft Azure AI Solution

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AI-102: Designing and Implementing an Azure AI Solution Certification Path

The Microsoft Designing and Implementing an Azure AI Solution Certification includes only one AI-100 Exam.

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Topics of AI-102: Designing and Implementing an Azure AI Solution Exam

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1. Analyze solution requirements (25-30%)

Recommend Cognitive Services APIs to meet business requirements

• Identify components and technologies required to connect service endpoints
• Select the appropriate AI models and services
• Select the appropriate data processing technologies
• 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 auditing requirements
• Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
• Identify appropriate tools for a solution

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

• Define an AI application workflow process
• Design pipelines that use AI apps
• Select an AI solution that meet cost constraints
• 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

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 bot services that use Language Understanding (LUIS)
• Design bots that integrate with channels

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

• Define how users and applications will authenticate to AI services
• Ensure that data adheres to compliance requirements defined by your organization
• 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

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

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 prerequisite components to allow connectivity to the Bot Framework
• Configure integration with Cognitive Services

Monitor and evaluate the AI environment

• Recommend changes to an AI solution based on performance data
• Monitor AI components for availability
• Identify the differences between KPIs, reported metrics, and root causes of the differences
• Identify the differences between expected and actual workflow throughput
• Maintain an AI solution for continuous improvement

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

Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:

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Microsoft AI-102 Exam Syllabus Topics:

Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions:

Question #1
You build a QnA Maker resource to meet the chatbot requirements.
Which RBAC role should you assign to each group? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question #2
You have a product knowledgebase that contains multiple PDF documents.
You need to build a chatbot that will provide responses based on data in the knowledgebase. The solution must minimize development effort and costs.
What should you include in the solution?

A. Azure Al Language custom question answering
B. Azure OpenAI
C. Azure Al language detection
D. Azure Al Language conversational language understanding (CLU)

Question #3
You have the following data sources:
Finance: On-premises Microsoft SQL Server database
Sales: Azure Cosmos DB using the Core (SQL) API
Logs: Azure Table storage
HR: Azure SQL database
You need to ensure that you can search all the data by using the Azure Cognitive Search REST API. What should you do?

A. Ingest the data in Logs into Azure Sentinel.
B. Configure multiple read replicas for the data in Sales.
C. Migrate the data in Sales to the MongoDB API.
D. Mirror Finance to an Azure SQL database.

Question #4
You have an Azure subscription that contains a Microsoft Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure Content Understanding in Foundry Tools project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?

A. Azure AI Developer
B. Cognitive Services OpenAI Contributor
C. Azure AI Administrator
D. Cognitive Services OpenAI User

Question #5
You have a SQL query that combines customer data and order data. The query includes calculated columns.
You need to create a database object that would allow other users to rerun the same SOL query. What should you create?

A. an Index
B. a table
C. a view
D. a scalar function

Solutions: