In Azure Machine Learning designer, a low-code drag-and-drop interface, users can visually build machine learning workflows. According to the AI-900 study guide and Microsoft Learn module "Create and publish models with Azure Machine Learning designer", two key components that can be dragged onto the designer canvas are datasets and modules.
* Datasets (A): These are collections of data that serve as the input for training or evaluating models.
They can be registered in the workspace and then dragged onto the canvas for use in transformations or model training.
* Modules (D): These are prebuilt processing and modeling components that perform operations such as data cleaning, feature engineering, model training, and evaluation. Examples include "Split Data,"
"Train Model," and "Evaluate Model."
Compute (B) and Pipeline (C) are not drag-and-drop items within the designer. Compute targets are infrastructure resources used to run the pipeline, while a pipeline represents the overall workflow, not a component that can be added like a dataset or module.
Hence, the correct answers are A. Dataset and D. Module.
Reference:Microsoft Learn - Create a machine learning model with Azure Machine Learning designer

Explanation:

The correct completion of the sentence "_____ is an example of speech recognition." is A voice-activated security key system.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of common AI workloads", speech recognition refers to the ability of a system or application to convert spoken language into text or actionable commands. It allows computers to interpret and respond to human speech inputs, bridging human-computer interaction through natural language.
Microsoft Learn clearly explains that speech recognition is used in applications such as voice assistants, dictation software, and voice-activated security systems, where the spoken input from a user is captured, analyzed, and translated into commands or text. For example, when a user says "Unlock door" or "Open session," the speech recognition system interprets that sound input, converts it into text or a command, and then performs the appropriate action. This is a direct implementation of speech-to-text processing combined with command execution logic.
Let's analyze the other options:
* Creating an audio commentary for a video recording is related to speech synthesis (text-to-speech), not recognition.
* Creating captions for a video recording involves speech-to-text transcription, which is a subset of speech recognition, but the question emphasizes a system that responds to voice commands, making the first option more accurate.
* Identifying key phrases in a video transcript involves natural language processing (NLP) techniques rather than speech recognition.
Therefore, the voice-activated security key system best represents the use of speech recognition technology because it interprets spoken commands and takes a corresponding action based on recognized speech patterns.
This aligns directly with the AI-900 learning objectives where speech recognition is defined as a process that enables applications to interpret and respond to human voice input.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation for Azure AI Custom Vision, this service is a specialized part of the Azure AI Vision family that enables developers to train custom image classification and object detection models. It allows organizations to build tailored computer vision models that recognize images or specific objects relevant to their business needs.
* Detect objects in an image # YesThe Azure AI Custom Vision service supports both image classification (assigning an image to one or more categories) and object detection (identifying and locating objects within an image using bounding boxes). This means it can indeed detect and differentiate multiple objects in a single image, making this statement true.
* Requires your own data to train the model # YesThe Custom Vision service is designed to be customizable. Unlike prebuilt Azure AI Vision models that work out of the box, Custom Vision requires you to upload and label your own dataset for training. The model then learns from your examples to perform specialized image recognition tasks relevant to your domain. Thus, this statement is also true.
* Analyze video files # NoWhile Custom Vision can analyze images, it does not directly process or analyze video files. Video analysis is handled by a different service-Azure Video Indexer-which can extract insights such as spoken words, scenes, and faces from videos.
In summary:
# Yes - Detect objects in images
# Yes - Requires your own data
# No - Does not analyze video files.

The correct answer is B. Azure AI Document Intelligence (formerly Form Recognizer).
This Azure service uses AI and OCR technologies to analyze and extract structured data from documents such as invoices, receipts, and purchase orders. It identifies key fields like billing address, invoice number, total amount due, and line items. The service supports prebuilt models for common document types and custom models for specialized layouts.
Option review:
* A. Azure AI Search: Used for knowledge mining and semantic search, not document data extraction.
* B. Azure AI Document Intelligence - # Correct. Designed for form and invoice extraction.
* C. Azure AI Custom Vision: Used for image classification and object detection, not text extraction.
* D. Azure OpenAI: Generates or processes language but not structured document data.
Therefore, Azure AI Document Intelligence is the right service to extract data from scanned invoices.

In Azure Machine Learning designer, the Split Data module is specifically designed to divide a dataset into training and validation (or testing) subsets. The AI-900 study guide and the Microsoft Learn module "Split data for training and evaluation" explain that this module allows users to control how data is partitioned, ensuring that models are trained on one portion of the data and tested on unseen data to assess performance.
By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70-80% of the data is used for training and the remaining 20-30% for validation or testing. This ensures the model's generalizability and prevents overfitting.
The other options serve different purposes:
* A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.
* B. Add Rows: Combines multiple datasets vertically.
* D. Join Data: Combines datasets horizontally based on a common key.
Only Split Data performs the function of dividing data into training and validation subsets.
Reference:Microsoft Learn - Split data for training and evaluation in Azure Machine Learning designer