Exam Details

Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.

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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

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

Map security requirements to tools, technologies, and processes

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

Select the software, services, and storage required to support a solution

• Identify appropriate services and tools for a solution
• Identify integration points with other Microsoft services
• 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
• 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 pipelines that use AI apps

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

Design the compute infrastructure to support a solution

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

Design for data governance, compliance, integrity, and security

• Design a content moderation strategy for data usage within an AI solution
• Define how users and applications will authenticate to AI services
• Ensure appropriate governance of data
• 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

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

Implement an AI workflow

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

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
• Implement Azure Search in a solution
• Configure integration with Cognitive Services

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
• Maintain an AI solution for continuous improvement
• Identify the differences between expected and actual workflow throughput
• Monitor AI components for availability

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

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