Microsoft Certified: Azure AI Engineer Associate Study Guide (AI-100/AI-102)

4 min readFeb 6, 2021

Note: In case you are planning to study to this exam and certification, please keep in mind that this exam will be replaced by the AI-102 exam coming on February 23, 2021 — (AI-102)

The Microsoft Certified: Azure AI Engineer Associate certification enables you to get the knowledge to be a subject matter expert using Cognitive Services, Machine Learning (ML), and knowledge mining to architect and implement AI solutions on Azure involving natural language processing, speech, computer vision, conversational AI and more.

In this study guide, I will share with you some of the useful resources you can use to guide you during your learning path to get this certification.

Certification Path

Exam AI-100: Designing and Implementing an Azure AI Solution

The AI-100 has a length of three hours. There are 30+ questions and you need a minimum of 700 of 1000 points to pass the exam.

The first place to go is the Microsoft Learn platform where a dedicated learning path is available, for free. Also, you should have a look to the Resources section in this study guide where you have useful resources to help you consolidate the knowledge that will help you get the exam and certification. If you prefer to watch videos, instead of read, explaining these core concepts and showing how to get started using Power Platform, then I invite you to have a look at the Microsoft Azure AI Engineer (AI-100) path, available on Pluralsight.

Skills measured

Analyze solution requirements (25–30%)

Recommend Azure Cognitive Services APIs to meet business requirements

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

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 appropriate services and tools for a solution
identify integration points with other Microsoft services
identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design AI solutions (40–45%)

Design solutions that include one or more pipelines

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

Design solutions that uses Cognitive Services

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

Design solutions that implement the Microsoft Bot Framework

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

Design the compute infrastructure to support a solution

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

Design for data governance, compliance, integrity, and security

define how users and applications will authenticate to AI services
design a content moderation strategy for data usage within an AI solution
ensure that data adheres to compliance requirements defined by your organization
ensure appropriate governance of data
design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions (25–30%)

Implement an AI workflow

develop AI pipelines
manage the flow of data through the solution components
implement data logging processes
define and construct interfaces for custom AI services
create solution endpoints
develop streaming solutions

Integrate AI services and solution components

configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs
configure integration with Azure Cognitive Services
configure prerequisite components to allow connectivity to the Microsoft Bot Framework
implement Azure Cognitive Search in a solution

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

Key Notes

Get familiar with options you have to create simple AI solutions with no or low customization
Get familiar with options you have to create custom AI solutions
Understand the different options you have available to create your ML solutions and when and how you should use each of them
Explore the different Cognitive Services available and understand in which scenarios you should use each of the services
Get familiar with options to use in scenarios of building AI capabilities to your solutions running in the Edge

Additional Notes

You can access my personal notes, while studying to the exam by accessing my original blog post — https://www.hugobarona.com/ai-100-study-guidedesigning-and-implementing-an-azure-ai-solution/

Originally published at https://www.hugobarona.com on February 6, 2021.