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

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

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 appropriate tools for a solution
  • Identify auditing requirements

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

  • Identify integration points with other Microsoft services
  • Identify appropriate services and tools for a solution
  • 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

  • Design a strategy for ingest and egress data
  • Design pipelines that call Azure Machine Learning models
  • Select an AI solution that meet cost constraints
  • Design pipelines that use AI apps
  • 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

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

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

  • 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
  • Design a content moderation strategy for data usage within an AI solution

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

Implement an AI workflow

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

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
  • Configure prerequisite components to allow connectivity to the Bot Framework
  • Implement Azure Search in a solution

Monitor and evaluate the AI environment

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