Candidates for the Azure AI Engineer Associate certification build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services.

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 certification 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.

Skills measured

• Plan and manage an Azure Cognitive Services solution

• Implement Computer Vision solutions

• Implement natural language processing solutions

• Implement knowledge mining solutions

• Implement conversational AI solutions

The Exam consists of questions covering the following modules/topics:

- Plan and Manage an Azure Cognitive Services Solution (15-20%)

Select the appropriate Cognitive Services resource

Plan and configure security for a Cognitive Services solution

Create a Cognitive Services resource

Plan and implement Cognitive Services containers

- Implement Computer Vision Solutions (20-25%)

Analyze images by using the Computer Vision API

Extract text from images

Extract facial information from images

Implement image classification by using the Custom Vision service

Portal

Implement an object detection solution by using the Custom Vision service

Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer)

- Implement Natural Language Processing Solutions (20-25%)

Analyze text by using the Text Analytics service

Manage speech by using the Speech service

Translate language

Build an initial language model by using Language Understanding Service (LUIS)

Iterate on and optimize a language model by using LUIS

Manage a LUIS model

- Implement Knowledge Mining Solutions (15-20%)

Implement a Cognitive Search solution

Implement an enrichment pipeline

Implement a knowledge store

Manage a Cognitive Search solution

Manage indexing

- Implement Conversational AI Solutions (15-20%)

Create a knowledge base by using QnA Maker

Design and implement conversation flow

Create a bot by using the Bot Framework SDK

Create a bot by using the Bot Framework Composer

Integrate Cognitive Services into a bot