AI-100 Exam Study Guide (Microsoft Azure AI Solution)

AI-100 Designing and Implementing an Azure AI Solution Certificate Exam Study Guide

Preparing for AI-100 Designing and Implementing an Azure AI Solution Certificate exam? Don’t know where to start? This post is the AI-100 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the AI-100 exam. I hope this article will help you to prepare for the AI-100 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

AI-100 Azure AI Practice Test and Lab

AI-100 Azure AI Reference E-book (PDF)

To view other Azure Certificate Study Guides, click here

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Analyze solution requirements (25-30%)

Recommend Cognitive Services APIs to meet business requirements

Select the processing architecture for a solution

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

Select the appropriate data processing technologies

https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview

Select the appropriate AI models and services

https://docs.microsoft.com/en-us/azure/cognitive-services/welcome

https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-and-machine-learning

Identify components and technologies required to connect service endpoints

https://docs.microsoft.com/en-us/rest/api/azure/

https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account

Identify automation requirements

https://docs.microsoft.com/en-us/azure/automation/automation-intro

 

Map security requirements to tools, technologies, and processes

Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements

https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/govern/policy-compliance/regulatory-compliance

https://docs.microsoft.com/en-us/microsoft-365/compliance/gdpr-dsr-azure

https://docs.microsoft.com/en-us/azure/security/fundamentals/protection-customer-data

https://azure.microsoft.com/en-in/blog/everything-you-ever-wanted-to-know-about-data-protection-and-more/

Identify which users and groups have access to information and interfaces

https://docs.microsoft.com/en-us/devops/organizations/security/permissions

https://docs.microsoft.com/en-us/azure/devops/organizations/security/view-permissions

Identify appropriate tools for a solution

https://docs.microsoft.com/en-us/azure/security/fundamentals/overview

https://docs.microsoft.com/en-us/microsoft-365/compliance/compliance-manager-overview

https://docs.microsoft.com/en-us/azure/information-protection/what-is-information-protection

Identify auditing requirements

https://docs.microsoft.com/en-us/azure/security/fundamentals/log-audit

https://docs.microsoft.com/en-us/azure/azure-monitor/log-query/log-query-overview

 

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

Identify appropriate services and tools for a solution

https://docs.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-ml

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/cognitive-services

Identify integration points with other Microsoft services

https://docs.microsoft.com/en-us/azure/event-grid/overview

https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-about

https://docs.microsoft.com/en-us/azure/logic-apps/logic-apps-overview

Identify storage required to store logging, bot state data, and Cognitive Services output

https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-comparison

https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/use-the-best-data-store

ai-100

Amazon link (affiliate)

Design AI solutions (40-45%)

Design solutions that include one or more pipelines

Define an AI application workflow process

https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

Design a strategy for ingest and egress data

https://azure.microsoft.com/en-in/blog/data-ingestion-into-azure-at-scale-made-easier-with-latest-enhancements-to-adf-copy-data-tool/

Design the integration point between multiple workflows and pipelines

Integrating Data in Microsoft Azure

Design pipelines that use AI apps

https://docs.microsoft.com/en-us/azure/machine-learning/studio/manage-new-webservice

Design pipelines that call Azure Machine Learning models

https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-endpoint

Select an AI solution that meet cost constraints

https://azure.microsoft.com/en-us/pricing/details/bot-service/

https://azure.microsoft.com/en-us/pricing/details/cognitive-services/

 

Design solutions that uses Cognitive Services

Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/csharp-sdk

https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/quickstarts/speech-to-text-from-microphone

https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-get-started-get-intent-from-rest

https://docs.microsoft.com/en-us/azure/cognitive-services/bing-autosuggest/quickstarts/csharp

https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/tutorials/batch-anomaly-detection-powerbi

 

Design solutions that implement the Bot Framework

Integrate bots and AI solutions

Building with Conversational AI

Design bot services that use Language Understanding (LUIS)

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-csharp-tutorial-bf-v4

Design bots that integrate with channels

https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels

Integrate bots with Azure app services and Azure Application Insights

https://docs.microsoft.com/en-in/learn/modules/build-chat-bot-with-azure-bot-service/create-web-app-bot

https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-telemetry

ai-100

Amazon link (affiliate)

Design the compute infrastructure to support a solution

Identify whether to create a GPU, FPGA, or CPU-based solution

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-fpga-web-service

Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure

https://azure.microsoft.com/en-in/overview/what-are-private-public-hybrid-clouds/

Select a compute solution that meets cost constraints

https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/compute-overview

https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/compute-decision-tree

 

Design for data governance, compliance, integrity, and security

Define how users and applications will authenticate to AI services

https://docs.microsoft.com/en-us/azure/cognitive-services/authentication

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

https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/overview

https://docs.microsoft.com/en-in/azure/cognitive-services/content-moderator/quick-start

Ensure that data adheres to compliance requirements defined by your organization

https://docs.microsoft.com/en-us/azure/governance/policy/how-to/get-compliance-data

https://docs.microsoft.com/en-us/microsoft-365/compliance/compliance-manager-overview

Ensure appropriate governance for data

https://docs.microsoft.com/en-us/azure/governance/

Design strategies to ensure that the solution meets data privacy regulations and industry standards

https://azure.microsoft.com/en-us/support/legal/cognitive-services-compliance-and-privacy/

https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-retention-privacy

 

Implement and monitor AI solutions (25-30%)

Implement an AI workflow

Develop AI pipelines

https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines

Manage the flow of data through solution components

https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/advanced-analytics-on-big-data

https://azure.microsoft.com/en-gb/solutions/architecture/advanced-analytics-on-big-data/

https://docs.microsoft.com/en-us/azure/iot-edge/about-iot-edge

https://docs.microsoft.com/en-us/azure/iot-fundamentals/iot-introduction

Implement data logging processes

https://docs.microsoft.com/en-us/azure/cognitive-services/diagnostic-logging

Define and construct interfaces for custom AI services

https://docs.microsoft.com/en-us/azure/cognitive-services/bing-custom-search/tutorials/custom-search-web-page

https://docs.microsoft.com/en-us/azure/cognitive-services/bing-custom-search/define-your-custom-view

Create solution endpoints

https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-for-kafka-ecosystem-overview

Develop streaming solutions

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-solution-patterns

https://docs.microsoft.com/en-us/learn/paths/implement-data-streaming-with-asa/

Check the FAQs on Azure Certification

Integrate AI services with solution components

Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs

https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/build-training-data-set

Configure integration with Cognitive Services

https://docs.microsoft.com/en-us/azure/active-directory/develop/quickstart-configure-app-expose-web-apis

Configure prerequisite components to allow connectivity to the Bot Framework

https://docs.microsoft.com/en-us/azure/bot-service/abs-quickstart

https://docs.microsoft.com/en-us/azure/bot-service/bot-service-debug-emulator

Implement Azure Cognitive Search in a solution

https://docs.microsoft.com/en-us/azure/cognitive-services/bing-web-search/quickstarts/csharp

 

Monitor and evaluate the AI environment

Identify the differences between KPIs, reported metrics, and root causes of the differences

https://docs.microsoft.com/en-us/azure/azure-monitor/learn/tutorial-app-dashboards

https://docs.microsoft.com/en-us/azure/azure-monitor/platform/data-platform

Identify the differences between expected and actual workflow throughput

https://docs.microsoft.com/en-us/azure/cognitive-services/personalizer/concepts-scalability-performance

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

Maintain an AI solution for continuous improvement

https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/ci-cd-flask

Monitor AI components for availability

https://docs.microsoft.com/en-us/azure/azure-monitor/platform/resource-logs-collect-workspace

https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model

Recommend changes to an AI solution based on performance data

https://azure.microsoft.com/en-in/blog/getting-ai-ml-and-devops-working-better-together/

This brings us to the end of AI-100 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are looking for other Azure certification exams check out this page

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