"Experts since 2008"

Microsoft Azure AI Fundamentals

"Exam AI-900"

Microsoft Azure AI Fundamentals - AZ 900

Market Price: INR. 4200
Our Special price INR. 3100

The pathway to AI, Data Science, and Cloud Computing.The Microsoft Certified Fundamentals exams are the pathway to fully understanding what cloud computing is, what options are available from Microsoft, and which options make sense for businesses to invest in. This set of exams is the starting point for students who are looking to start a career in artificial intelligence, data science, and cloud computing. With the usage of big data and artificial intelligence on the rise, the demand for data scientists is not being met—and the gap is widening.

There are various appealing job opportunities you can unlock once you obtain the associate certification. But before going for this level of certification, you must equip yourself first with fundamental knowledge. This is only possible if you have completed the Azure fundamentals certification. Once you have cleared this certification, you will not find trouble learning more intense topics. Through the AI-900 exam, the fundamental level certification of Azure qualifies you for expert and advanced level certification.

Those IT experts who hold Azure AZ-900 certification can earn more than their counterparts who don’t hold it. If you pass the fundamental certification of Azure, you become competent enough to handle more responsibilities in your current job.

What will you learn?

Features of common AI workloads

Features of content moderation and personalization workloads
Computer vision workloads
Identify natural language processing workloads
Knowledge mining workloads
Document intelligence workloads
Features of generative AI workloads

Guiding principles for responsible AI

Considerations for fairness in an AI solution
Considerations for reliability and safety in an AI solution
Considerations for privacy and security in an AI solution
Considerations for inclusiveness in an AI solution
Considerations for transparency in an AI solution
Considerations for accountability in an AI solution

Common machine learning techniques

Regression machine learning scenarios
Classification machine learning scenarios
Clustering machine learning scenarios
Features of deep learning techniques

Core machine learning concepts

Azure regions, region pairs, and sovereign regions
Availability zones
Azure datacenters
Azure resources and resource groups
Subscriptions
Management groups
Hierarchy of resource groups, subscriptions, and management groups

Azure Machine Learning capabilities

Capabilities of Automated machine learning
Data and compute services for data science and machine learning
Model management and deployment capabilities in Azure Machine Learning

Common types of computer vision solution

Features of image classification solutions
Features of object detection solutions
Features of optical character recognition solutions
Features of facial detection and facial analysis solutions

Azure tools and services for computer vision tasks

Capabilities of the Azure AI Vision service
Capabilities of the Azure AI Face detection service

Features of common NLP Workload Scenarios

Features and uses for key phrase extraction
Features and uses for entity recognition
Features and uses for sentiment analysis
Features and uses for language modeling
Features and uses for speech recognition and synthesis
Features and uses for translation

Azure tools and services for NLP workloads

Describe capabilities of the Azure AI Language service
Describe capabilities of the Azure AI Speech service

Features of generative AI solutions

Features of generative AI models
Common scenarios for generative AI
Responsible AI considerations for generative AI

Capabilities of Azure OpenAI Service

Natural language generation capabilities of Azure OpenAI Service
Code generation capabilities of Azure OpenAI Service
Image generation capabilities of Azure OpenAI Service

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