AI-900, AI-100: Microsoft Azure AI

Kjøp nå

Pris: $19.99

AI-900: Grunnleggende om Microsoft Azure AI

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (Skript brukes i naturlige språkforståelsessystemer for å organisere en kunnskapsbase i forhold til situasjonen som systemet skal forstå) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

Denne eksamenen er beregnet på kandidater med både teknisk og ikke-teknisk bakgrunn. Erfaring med datavitenskap og programvareingeniør er ikke nødvendig; derimot, litt generell programmeringskunnskap eller erfaring vil være fordelaktig.

Azure AI Fundamentals kan brukes til å forberede seg på andre Azure-rollebaserte sertifiseringer som Azure Data Scientist Associate eller Azure AI Engineer Associate, DP-900.

AI-100: Designing and Implementing an Azure AI Solution

Candidates for this exam should have subject matter expertise using cognitive services, maskinlæring, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, tale, datamaskin syn, and conversational AI.

Responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions, recommending the appropriate tools and technologies, and designing and implementing AI solutions that meet scalability and performance requirements.

Azure AI Engineers translate the vision from solution architects and work with data scientists, dataingeniører, IoT-spesialister, and software developers to build complete end-to-end solutions.

A candidate for this exam should have knowledge and experience designing and implementing AI apps and agents that use Microsoft Azure Cognitive Services, Azure Bot-tjeneste, Azure kognitivt søk, og datalagring i Azure. I tillegg, a candidate should be able to recommend solutions that use open source technologies, understand the components that make up the Azure AI portfolio and the available data storage options, and understand when a custom API should be developed to meet specific requirements.