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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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The benefit of obtaining the AI-102: Designing and Implementing an Azure AI Solution Exam Certification

• AI-102 Certification provides practical experience to candidates from all the aspects to be a proficient worker in the organization.
• AI-102 Certifications provide opportunities to get a job easily in which they are interested in instead of wasting years and ending without getting any experience.
• AI-102 certification has more useful and relevant networks that help them in setting career goals for themselves. AI-102 networks provide them with the correct career guidance than non certified generally are unable to get.
• AI-102 credential delivers higher earning potential and increased promotion opportunities because it shows a good understanding of designing and implementing an Azure AI Solution
• AI-102 Exam provide proven knowledge to use the tools to complete the task efficiently and cost effectively than the other non-certified professionals lack in doing so.
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• This certification credential will give you edge over other counterparts. Apart from knowledge from AI-102: Designing and Implementing an Azure AI Solution Exam.
• AI-102 certified candidates will be confident and stand different from others as their skills are more trained than non-certified professionals.
• This certification will be judging your skills and knowledge on your understanding Designing and Implementing an Azure AI Solution concepts & Understanding of how to operate on Planning and Administering Designing and Implementing an Azure AI Solution .

Topics Covered

Exam AI-102 contains five topics each of which is intended to check specific skills.

1. Plan and Manage an Azure Cognitive Services Solution

This topic implies your ability to choose the suitable Cognitive Services resource, create it, plan and design security for a Cognitive Services solution, and apply Cognitive Services containers. This means that you should be competent in selecting the appropriate cognitive service for solutions that refer to language analysis, speech, decision support, and vision. You also should possess skills to operate costs of Cognitive Services, create a Cognitive Services resource, and monitor a cognitive service. This part also checks how well you can operate Cognitive Services account keys, and protect Cognitive Services. Your knowledge of using Face API, Computer Vision, Speech, Text Analysis, and ability to integrate Cognitive Services Containers in Microsoft Azure will also be assessed.

2. Implement Computer Vision Solutions

The second topic is designed to check your skills in using the Computer Vision API to get image descriptions, define landmarks, find brands, edit content in images, and create thumbnails. In this part, you are expected to be able to detect faces and recognize them in images, analyze facial features, and match similar faces with the help of the Face API. Being competent in utilizing the Custom Vision service, you should demonstrate your skills in applying image classification and implementing an object detection solution. Besides, your ability to analyze video by implementing Azure Video Analyzer for Media will be measured.

3. Implement Natural Language Processing Solutions

In the third topic, candidates are required to show their skills in analyzing text by utilizing the Text Analytics service, control speech by implementing the Speech service, translate the text with the help of the Translator service. This domain also checks your proficiency in creating and optimizing an initial language model by utilizing LUIS, and finally, managing it.

4. Implement Knowledge Mining Solutions

In this domain, you will be required to have expertise related to applying a Cognitive Search solution, which implies creating data sources, identifying an index, running an indexer, and using synonyms. This topic also aims to evaluate your ability to apply an enrichment pipeline, use a knowledge store, operate a Cognitive Search solution and indexing.

5. Implement Conversational AI Solutions

This domain will evaluate your capacity in utilizing QnA Maker to make a knowledge base, creating and implementing conversation flow, creating a bot by utilizing either the Bot Framework Composer or the Bot Framework SDK. Finally, you will need to demonstrate your skills in integrating Cognitive Services into a bot.

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

Map security requirements to tools, technologies, and processes

• Identify appropriate tools for a solution
• Identify auditing requirements
• 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

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

• Select an AI solution that meet cost constraints
• 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
• 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 and AI solutions
• Design bots that integrate with channels
• Design bot services that use Language Understanding (LUIS)
• Integrate bots with Azure app services and Azure Application Insights

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 appropriate governance of data
• Design strategies to ensure that the solution meets data privacy regulations and industry standards
• Design a content moderation strategy for data usage within an AI solution
• 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

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

• Maintain an AI solution for continuous improvement
• 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

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

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