Model Explain Ability.
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/

In Microsoft Azure OpenAI Service and the AI-900/AI-102 study materials, grounding data is the correct term used to describe the process of providing contextual or external information to improve the accuracy, relevance, and quality of responses generated by a generative AI model such as GPT-3.5 or GPT-4.
Grounding is a prompt engineering technique where the AI model is supplemented with relevant background data, such as company documents, knowledge bases, or user context, that helps the model generate factually correct and context-aware responses. Microsoft Learn defines grounding as a way to connect the model's general knowledge to specific, real-world information. For example, if you ask a GPT-3.5 model about your organization's HR policies, the base model will not know them unless that policy information is provided (grounded) in the prompt. By embedding this contextual data, the AI becomes "grounded" in the facts it needs to respond reliably.
This technique differs from other prompt engineering concepts:
* A. Providing examples (few-shot prompting) shows the model sample inputs and outputs to guide formatting or style, not factual context.
* B. Fine-tuning involves retraining the model with labeled data to permanently adjust its behavior - it's not a prompt-based technique.
* D. System messages define the model's role, tone, or style (for example, "You are a helpful assistant") but do not add factual context.
Therefore, when you provide contextual information (like product details, policy documents, or reference text) within a prompt to enhance the quality and factual reliability of the model's responses, you are applying the grounding data technique.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of computer vision workloads on Azure," the "Describe Image" feature within Azure's Computer Vision service automatically generates textual captions that describe the contents of an image.
This feature uses a deep learning model trained on millions of labeled images to identify objects, people, and scenes, then formulates a natural language sentence summarizing what it sees. For example, given a photograph showing "two people sitting at a table with a laptop," the service might generate the caption: "A man and a woman sitting at a desk using a laptop." Here's how the other options differ:
* A. Recognize text: Refers to Optical Character Recognition (OCR), which extracts written text from images, not generates descriptive captions.
* C. Identify the areas of interest: Refers to detecting regions of an image that stand out visually, such as hotspots or significant features, not descriptive captioning.
* D. Detect objects: Identifies and classifies objects in an image (e.g., cars, chairs, people) but doesn't produce a sentence or caption summarizing them.
Thus, only "Describe the images" generates automatic, human-readable captions that summarize photo content, a core computer vision workload taught in AI-900.

Explanation:
"features."
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe fundamental principles of machine learning on Azure," in a machine learning model, the data used as inputs are known as features, while the data that represents the output or target prediction is known as the label.
Features are measurable attributes or properties of the data used by a model to learn patterns and make predictions. They are also referred to as independent variables because they influence the result that the model tries to predict. For example, in a machine learning model that predicts house prices:
* Features might include square footage, location, and number of bedrooms, while
* The label would be the house price (the value being predicted).
In the context of Azure Machine Learning, during model training, features are passed into the algorithm as input variables (X-values), and the label is the corresponding output (Y-value). The model then learns the relationship between the features and the label.
Let's review the incorrect options:
* Functions: These are mathematical operations or relationships used inside algorithms, not the input data itself.
* Labels: These are the outputs or results that the model predicts, not the inputs.
* Instances: These refer to individual data records or rows in the dataset, not the input fields themselves.
Hence, in any supervised or unsupervised learning process, the input data (independent variables) are called features, and the model uses them to predict labels (dependent variables).

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," object detection is a computer vision technique used to locate and identify objects within an image. It not only determines what objects are present but also where they appear in the image by returning bounding box coordinates around each detected item.
In this scenario, the goal is to identify the location of widgets within digital photos. This requires both recognition (knowing that the object is a widget) and localization (determining its position). The Custom Vision service in Azure allows you to train a model specifically for your own images, making it ideal for recognizing company-specific products such as widgets. By selecting the Object Detection domain in Custom Vision, you can label regions of interest in your training images. The model then learns to detect and locate those objects in new photos.
Let's examine the other options:
* A. Computer Vision Spatial Analysis: Used for people tracking, movement detection, and occupancy analytics in video streams - not for locating products in still images.
* C. Custom Vision classification: This model categorizes an image as a whole (e.g., "contains a widget" or "does not contain a widget") but does not locate objects within the image.
* D. Computer Vision Image Analysis: Provides general image tagging, description, and OCR capabilities but does not pinpoint object locations.