The correct answers are A. Use a graphical user interface (GUI) to run automated machine learning experiments and C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
According to the Microsoft Azure AI Fundamentals (AI-900) official documentation and Microsoft Learn module "Create and manage Azure Machine Learning workspaces", there are two workspace tiers: Basic and Enterprise. The Enterprise workspace provides advanced capabilities for automation, visualization, and collaboration that are not available in the Basic tier.
Specifically:
* Automated machine learning (AutoML) using a GUI is only available in the Enterprise tier. AutoML automatically selects algorithms and tunes hyperparameters through the Azure Machine Learning studio interface.
* Azure Machine Learning designer, which allows users to visually drag and drop datasets and modules to create machine learning pipelines, also requires the Enterprise workspace.
In contrast:
* B. Create a compute instance and D. Create a dataset from a CSV file are fundamental actions supported in both Basic and Enterprise workspaces. These do not require the advanced licensing features of the Enterprise edition.
Therefore, tasks involving the graphical, no-code tools-Automated ML (AutoML) and the Designer- require the Enterprise workspace, aligning with AI-900's learning objectives.
Reference:Microsoft Learn - Create and manage Azure Machine Learning workspaces and Automate model training with Azure Machine Learning

Explanation:
Statements
Yes
No
A webchat bot can interact with users visiting a website.
Yes
Automatically generating captions for pre-recorded videos is an example of natural language processing.
No
A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of natural language processing.
Yes
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn modules on AI workloads, each of these statements maps to a distinct area of artificial intelligence - namely Conversational AI, Speech AI, and Natural Language Processing (NLP).
* "A webchat bot can interact with users visiting a website." - YesThis is true. A webchat bot represents an example of Conversational AI. It leverages natural language understanding (NLU) to interpret user input and generate appropriate responses. These bots can be created using Azure services such as Azure AI Bot Service and Language Understanding (LUIS). They enable automated interactions with users through text-based communication on websites, applications, or messaging platforms.
* "Automatically generating captions for pre-recorded videos is an example of natural language processing." - NoThis is false. Generating captions from audio involves speech recognition, not NLP.
Specifically, it uses speech-to-text technology to transcribe spoken words into written text. This function is typically performed by Azure's Speech service, which is part of the Speech AI workload, not the language-processing workload.
* "A smart device in the home that responds to questions such as 'What will the weather be like today?' is an example of natural language processing." - YesThis is true. Smart assistants like Alexa or Cortana use NLP to interpret spoken queries, extract meaning, and generate appropriate responses. NLP allows these devices to understand human language, retrieve relevant information, and respond conversationally.