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

Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate

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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam

• Passing Score: 700 / 1000
• This is beta exam.
• Length of Examination: 50 mins
• Type of Questions: This test format is multiple choice.
• Language: English, Japanese, Chinese (Simplified), Korean.
• Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)

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

Map security requirements to tools, technologies, and processes

• Identify which users and groups have access to information and interfaces
• Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
• Identify auditing requirements
• 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 the integration point between multiple workflows and pipelines
• Design a strategy for ingest and egress data
• Design pipelines that use AI apps
• Design pipelines that call Azure Machine Learning models
• Select an AI solution that meet cost constraints
• 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

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

Design for data governance, compliance, integrity, and security

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

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

Implement an AI workflow

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

Integrate AI services with solution components

• Configure integration with Cognitive Services
• Configure prerequisite components to allow connectivity to the Bot Framework
• Implement Azure Search in a solution
• Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs

Monitor and evaluate the AI environment

• Monitor AI components for availability
• Identify the differences between expected and actual workflow throughput
• 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

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

Prep Options to Choose

The preparation process for the exam is a vital step on your way of passing the test, that’s why you need to find the most actual and reliable resources. Microsoft offers two options for you to choose from online free training or instructor-led, which is a paid one. For example, the free training represents a collection of learning paths each of which contains the different number of modules, from one to five. Thus, you can choose which ones to follow: Prepare for AI engineering (1 module), Process and Translate Speech with Azure Cognitive Speech Services (2 modules), Create computer vision solutions with Azure Cognitive Services (3 modules), to name a few. The paid course is known to be Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution and lasts for 4 days. It is intended for software developers interested in developing skills to build AI infused apps that use Azure Cognitive Search and Services, and Microsoft Bot Framework.

In addition, you can check the Amazon website to find the books on the topics included in the exam to ace it from the first attempt. Only after the successful passing the AI-102 exam you will earn the Microsoft Certified: Azure AI Engineer Associate certification. So, good luck!

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