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This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.

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

• 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 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.
• AI-102 Certification is distinguished among competitors. AI-102 certification can give them an edge at that time easily when candidates appear for employment interview, employers are very fascinated to note one thing that differentiates the individual from all other candidates.
• 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 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 .
• 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 Certification provides practical experience to candidates from all the aspects to be a proficient worker in the organization.

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!

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 processing architecture for a solution
• Select the appropriate data processing technologies
• Select the appropriate AI models and services

Map security requirements to tools, technologies, and processes

• 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
• 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 a strategy for ingest and egress data
• Define an AI application workflow process
• Design the integration point between multiple workflows and pipelines
• Design pipelines that use AI apps
• Select an AI solution that meet cost constraints
• Design pipelines that call Azure Machine Learning models

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 and AI solutions
• Integrate bots with Azure app services and Azure Application Insights
• Design bots that integrate with channels

Design the compute infrastructure to support a solution

• Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
• Select a compute solution that meets cost constraints
• 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
• Define how users and applications will authenticate to AI services
• Design a content moderation strategy for data usage within an AI solution
• 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
• Create solution endpoints
• Manage the flow of data through the solution components
• Implement data logging processes
• 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
• Configure integration with Cognitive Services
• Implement Azure Search in a solution
• Configure prerequisite components to allow connectivity to the Bot Framework

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
• Maintain an AI solution for continuous improvement
• Monitor AI components for availability
• Recommend changes to an AI solution based on performance data