According to the Microsoft Learn documentation and the Azure AI Fundamentals (AI-900) study guide, the GPT (Generative Pre-trained Transformer) family of models within Azure OpenAI Service is used for text- based natural language tasks, including summarization, content generation, and text completion.
When you need to summarize text from a document, GPT models (such as GPT-3.5 or GPT-4) can process large sections of text, extract the most relevant details, and generate concise summaries that retain the key meaning. The summarization task uses the model's natural language understanding capabilities to identify core concepts and generate human-like, coherent text.
Other options are incorrect:
* A. Whisper # Used for speech-to-text transcription, not text summarization.
* B. DALL-E # Generates images from text prompts, not text summaries.
* C. Codex # Specializes in code generation and completion, not document summarization.

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:

Box 1: 11

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer
/evaluate-model

Explanation:
Features
The correct completion of the sentence is:
"In a machine learning model, the data that is used as inputs are called features." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types," the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.
In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.
Microsoft Learn further defines these key concepts:
* Features: Input variables (independent variables) used by the model to learn patterns.
* Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).
* Instances: Individual rows or data records within the dataset (each instance has multiple features).
* Functions: Algorithms or mathematical operations used during training and prediction - not data inputs.
Therefore, among the provided options - features, functions, labels, instances - only features accurately describe the data elements used as inputs for training or inference in a machine learning model.
In summary, within the AI-900 learning context:
* Features = inputs to the model.
* Labels = outputs for supervised learning.
* Instances = examples (rows) of data.

Explanation:

These answers are derived from the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." The Azure Face service, part of Azure Cognitive Services, provides advanced facial recognition capabilities including detection, verification, identification, grouping, and similarity analysis.
Let's analyze each statement:
* "The Face service can be used to group all the employees who have similar facial characteristics." # YesThe Face service supports a grouping function that automatically organizes a collection of unknown faces into groups based on visual similarity. It doesn't require labeled data; instead, it identifies clusters of similar-looking faces. This is particularly useful when building or validating datasets of people.
* "The Face service will be more accurate if you provide more sample photos of each employee from different angles." # YesAccording to Microsoft documentation, model accuracy improves when you provide multiple high-quality images of each person under different conditions-such as varying lighting, poses, and angles. This diversity allows the service to better learn unique facial characteristics and improves recognition reliability, especially for identification and verification tasks.
* "If an employee is wearing sunglasses, the Face service will always fail to recognize the employee." # NoThis is incorrect. While occlusions (like sunglasses or hats) can reduce accuracy, the service may still recognize the person depending on how much of the face remains visible. Microsoft Learn explicitly notes that partial occlusion affects recognition confidence but does not guarantee failure.
In conclusion, the Face service can group similar faces (Yes), become more accurate with diverse samples (Yes), and still recognize partially covered faces though with lower confidence (No). These principles align directly with the Face API's core functions and AI-900 learning objectives regarding computer vision and responsible AI-based facial recognition.