Before building or training any model in Azure Machine Learning Studio-including when using Automated ML (AutoML)-you must first create a Machine Learning workspace.
A workspace serves as the central environment for all machine learning assets such as datasets, compute targets, models, pipelines, and experiments. According to the AI-900 study guide and Microsoft Learn module
"Describe features and tools for machine learning in Azure," a workspace is the foundational setup required to organize and manage all ML-related resources.
The sequence typically follows these steps:
* Create a Machine Learning workspace.
* Configure compute resources (e.g., compute instance or cluster).
* Upload or register datasets.
* Use Automated ML or Designer to train models.
* Deploy and manage the trained models.
Option A (Jupyter notebook) is an optional tool for coding experiments.
Option C (Registered dataset) is created after the workspace exists.
Option D (Designer pipeline) is a visual tool used within the workspace.
Hence, B. a Machine Learning workspace is the correct answer because it is the first and mandatory step before using Automated ML or any training component in Azure Machine Learning Studio.

Explanation:

This question tests your understanding of machine learning workflow tasks as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Explore the machine learning process." The AI-900 curriculum divides the machine learning lifecycle into key phases: data preparation, feature engineering and selection, model training, model evaluation, and model deployment. Each phase has specific tasks designed to prepare, build, and assess predictive models before deployment.
* Examining the values of a confusion matrix # Model evaluationIn Azure Machine Learning, evaluating a model involves checking its performance using metrics such as accuracy, precision, recall, and F1- score. The confusion matrix is one of the most common tools for this purpose. According to Microsoft Learn, "model evaluation is the process of assessing a trained model's performance against test data to ensure reliability before deployment." Analyzing the confusion matrix helps determine whether predictions align with actual outcomes, making this task part of model evaluation.
* Splitting a date into month, day, and year fields # Feature engineeringFeature engineering refers to transforming raw data into features that better represent the underlying patterns to improve model performance. The study guide describes it as "the process of creating new input features from existing data." Splitting a date field into separate numeric fields (month, day, year) is a classic example of feature engineering because it enables the model to learn from temporal patterns that might otherwise remain hidden.
* Picking temperature and pressure to train a weather model # Feature selectionFeature selection involves identifying the most relevant variables that have predictive power for the model. As defined in Microsoft Learn, "feature selection is the process of choosing the most useful subset of input features for training." In this scenario, selecting temperature and pressure variables as inputs for a weather prediction model fits perfectly within the feature selection stage.
Therefore, the correct matches are:
# Examining confusion matrix # Model evaluation
# Splitting date field # Feature engineering
# Picking temperature & pressure # Feature selection

Explanation:

The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.

Explanation:

This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.

Explanation:
The correct mappings are based on the Microsoft Azure AI Fundamentals (AI-900) curriculum topic:
"Describe features of computer vision workloads." Microsoft divides computer vision tasks into key workload types - image classification, object detection, facial recognition, and optical character recognition (OCR) - each designed for specific visual analysis objectives.
* Identify celebrities in images # Facial recognitionFacial recognition goes beyond simple face detection; it can identify or verify specific individuals by comparing facial features with known profiles.
According to Microsoft Learn, the Face service in Azure Cognitive Services can detect, recognize, and identify people in photos or videos. Recognizing celebrities or known individuals is a prime example of facial recognition.
* Extract movie title names from movie poster images # Optical Character Recognition (OCR)OCR is used to detect and extract text content from images, such as printed or handwritten words. Azure's Computer Vision API uses OCR technology to read text in various languages from photos, scanned documents, or posters. Therefore, extracting movie titles or actor names from a poster image is a perfect use case for OCR.
* Locate vehicles in images # Object detectionObject detection identifies and locates specific objects within an image, returning bounding boxes that indicate their positions. In Azure, the Custom Vision service or Computer Vision object detection models are used to detect multiple objects like vehicles, pedestrians, or animals in a single image.
Summary:
* Facial recognition # Identifies specific people (celebrities)
* OCR # Extracts text (movie titles)
* Object detection # Finds and locates physical items (vehicles)
Thus, the verified and official answer is: