Microsoft Certified Azure AI Engineer

Am 07. Juli 2020 hatte ich die Microsoft Prüfung AI-100 abgelegt und darf mich für 2 Jahre Microsoft Certified Azure AI Engineer Associate nennen.

Zur Vorbereitung wurde folgender Inhalt verwendet:

Analyze solution requirements (25-30%)

Recommend 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 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 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 Bot Framework

• 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 for 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 solution components

• Implement data logging processes

• Define and construct interfaces for custom AI services

• Create solution endpoints

• 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

• Configure prerequisite components to allow connectivity to the 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