Study with AI-102 Actual Test Questions

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

Map security requirements to tools, technologies, and processes

• 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
• Identify auditing requirements

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

• Identify appropriate services and tools for a solution
• Identify storage required to store logging, bot state data, and Cognitive Services output
• Identify integration points with other Microsoft services

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

• Select an AI solution that meet cost constraints
• Design pipelines that use AI apps
• Design pipelines that call Azure Machine Learning models
• Design a strategy for ingest and egress data
• 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)
• Integrate bots with Azure app services and Azure Application Insights
• Integrate bots and AI solutions
• 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

• Design a content moderation strategy for data usage within an AI solution
• 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
• Ensure that data adheres to compliance requirements defined by your organization

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

Implement an AI workflow

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

Integrate AI services with solution components

• 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
• Configure prerequisite components to allow connectivity to the Bot Framework

Monitor and evaluate the AI environment

• 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 expected and actual workflow throughput
• Identify the differences between KPIs, reported metrics, and root causes of the differences

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

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AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary

The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in

• Europe - 90,547 EURO
• United State - 120,000 USD
• England - 90,532 POUND
• India - 20,00,327 INR

Microsoft AI-102 Exam Syllabus Topics: