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.)
• Passing Score: 700 / 1000
• Length of Examination: 50 mins
• Language: English, Japanese, Chinese (Simplified), Korean.
• Type of Questions: This test format is multiple choice.
• This is beta exam.

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

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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 processing architecture for a solution
• Identify components and technologies required to connect service endpoints
• Select the appropriate AI models and services
• Select the appropriate data processing technologies

Map security requirements to tools, technologies, and processes

• Identify which users and groups have access to information and interfaces
• Identify appropriate tools for a solution
• Identify auditing requirements
• Identify processes and regulations needed to conform with data privacy, protection, and regulatory 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 the integration point between multiple workflows and 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
• 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

• Integrate bots with Azure app services and Azure Application Insights
• Integrate bots and AI solutions
• Design bot services that use Language Understanding (LUIS)
• Design bots that integrate with channels

Design the compute infrastructure to support a solution

• Select a compute solution that meets cost constraints
• Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
• Identify whether to create a GPU, FPGA, or CPU-based solution

Design for data governance, compliance, integrity, and security

• Ensure that data adheres to compliance requirements defined by your organization
• Ensure appropriate governance of data
• Design strategies to ensure that the solution meets data privacy regulations and industry standards
• Define how users and applications will authenticate to AI services
• Design a content moderation strategy for data usage within an AI solution

3. Implement and monitor AI solutions (25-30%)

Implement an AI workflow

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

Integrate AI services with solution components

• Implement Azure Search in a solution
• 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

Monitor and evaluate the AI environment

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

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