Microsoft Azure AI Fundamentals Practice Test (AI-900)

Automated Machine Learning is the correct answer. It allows users to automatically train and tune machine learning models by selecting the best algorithms and hyperparameters for a given dataset and problem type, reducing the need for manual intervention.

Azure Machine Learning Designer provides a drag-and-drop interface for building models but still requires users to manually select algorithms and tune parameters.

Custom model training with the Azure ML SDK involves writing code and manually handling the training process, which can be resource-intensive.

Using pre-trained models from Azure Cognitive Services offers models for specific tasks but doesn't allow customization for the company's sales forecasting data.

Designing user interfaces that accommodate various abilities is a core principle of inclusive design, ensuring that people with different needs, such as visual or motor impairments, can effectively use the kiosk. Personalization tailors the experience but does not inherently solve for fundamental accessibility. Using complex technical language or designing for a single demographic group are practices that directly contradict the goal of inclusiveness.

Computer Vision is the AI workload that enables machines to interpret and analyze visual data, such as radiology scans, to detect patterns and anomalies indicating diseases.

Natural Language Processing (NLP) deals with text and speech data.

Knowledge Mining involves extracting information from large datasets.

Predictive Analytics focuses on forecasting future outcomes based on data, but they do not directly process visual imagery like radiology scans.

The Azure Face service is designed for detailed facial analysis. Its Detect API can return bounding boxes, 27-point landmarks, head-pose roll/yaw/pitch values, and (in approved scenarios) an age attribute. The Azure Computer Vision Image Analysis API only returns a basic face rectangle without these detailed attributes. Azure Cognitive Search indexes data, and Azure Video Indexer extracts insights from video, making them unsuitable for fine-grained face analysis in still images.

The correct answer is DALL-E. DALL-E models are specifically designed to generate original images from textual prompts within the Azure OpenAI Service. GPT-4 is a large language model focused on understanding and generating text. Azure Computer Vision is an Azure AI service used for analyzing existing images, such as recognizing objects and extracting text, not for generating new ones. Codex is a model family that is optimized for generating code.

Classification - This is the correct answer. Fraud detection is a classification task because the model needs to classify each transaction as either fraudulent or legitimate, which involves assigning data points to predefined categories or labels.

Regression is used for predicting continuous numerical values for example sales forecasts or prices, not for classifying transactions into categories such as "fraudulent" or "legitimate."

Clustering is an unsupervised learning technique used to group data based on similarities, but it is not suitable for determining whether a transaction is fraudulent or legitimate, which requires labeled data and a classification approach.

Dimensionality Reduction is used to reduce the number of features in the data, typically for improving performance or visualization, but it is not a task in itself for determining fraud or legitimacy.

Classification is used when the goal is to assign data points to discrete categories based on input features. In this case, the hospital aims to categorize patients as having the disease or not, which is a typical classification problem.

Regression is meant for predicting continuous numerical values, such as prices or temperatures.

Clustering is where data points are grouped based on similarity without predefined labels, which doesn't fit the need to assign patients to known categories.

Reinforcement Learning involves learning optimal actions through trial and error, which is not applicable here.

The Azure AI Vision service provides a wide range of computer vision functionalities, including image analysis, object detection, facial detection and analysis, and optical character recognition (OCR). This makes it suitable for detecting customer demographics (using face analysis), recognizing products on shelves (object detection), and reading text from signs (OCR).

The Azure AI Face Detection service specializes in facial detection and recognition but does not handle object detection or OCR.

The Azure AI Anomaly Detector service is designed for identifying anomalies in time-series data, not for image or video analysis.

The Azure AI Language service focuses on natural language processing tasks and does not provide image analysis capabilities.

Natural Language Processing (NLP) workload - This is the correct answer. Analyzing customer reviews to determine overall satisfaction involves processing and understanding text data, which is exactly what Natural Language Processing (NLP) is designed for. NLP techniques such as sentiment analysis can be applied to assess customer sentiment from reviews and feedback.

Computer Vision workload - Computer vision is used for analyzing visual data, such as images or videos.

Content Moderation workload - Content moderation focuses on filtering inappropriate or harmful content.

Document Intelligence workload - Document intelligence focuses on understanding and extracting structured information from documents, often for tasks like form processing or invoice extraction.

The Azure AI Language service is designed for analyzing text using natural language processing techniques, including key phrase extraction, sentiment analysis, and entity recognition. It allows users to process large volumes of text data to gain insights into the content.

The Azure AI Speech service focuses on speech-to-text and text-to-speech capabilities and is not optimized for text analysis tasks.

Azure Cognitive Search is used for adding search functionalities to applications but doesn't provide advanced text analytics out of the box.

Azure Machine Learning service is a platform for building, training, and deploying machine learning models, which would require custom development effort to perform these specific text analysis tasks.

They create new data based on learned patterns from training data - This is the correct answer. AI models that generate new content, such as text, images, or music, are typically generative models. They learn patterns from training data and use this knowledge to produce original content that resembles the data they were trained on.

They classify input data into predefined categories - This describes classification models, which focus on categorizing data into specific labels or categories, rather than generating new content.

They predict numerical values from historical data - This is characteristic of predictive models, which are used for forecasting or regression tasks, rather than generating new content.

They retrieve exact copies of existing content - This describes retrieval-based models, which focus on finding and returning pre-existing content from a database or dataset, rather than generating new, original content.

Label - This is the correct answer. In supervised machine learning, the label is the target variable that the model aims to predict based on the input variables (features). For example, in a dataset predicting house prices, the house price would be the label.

Feature - A feature is an input variable (predictor) used by the model to make predictions. Features are the independent variables in the dataset that help determine the value of the label.

Parameter - A parameter refers to the internal variables of a model that are learned during training, such as the weights in a neural network. Parameters are not the target variable but the model's learned coefficients.

Hyperparameter - A hyperparameter is a setting that is manually chosen before training a model (such as the learning rate or the number of trees in a random forest) but is not part of the dataset itself. Hyperparameters control the learning process and model configuration.

Language Modeling - This is the correct answer. Language modeling is primarily used for tasks like predicting the next word or suggesting phrases as a user types. It helps the application understand the context and structure of language to provide relevant suggestions in real-time.

Key Phrase Extraction - Key phrase extraction is used to identify important terms or concepts within a text, but it is not typically used for real-time suggestions or completing sentences as a user types.

Sentiment Analysis - Sentiment analysis is used to determine the emotional tone of text such as positive, negative or neutral. But it is not relevant for generating word or phrase suggestions while typing.

Entity Recognition - Entity recognition identifies and extracts specific entities like names, places or dates from text. It is not focused on generating suggestions for completing sentences or phrases.

Speech Translation - This is the correct answer. Speech translation is the Azure AI Speech service feature that enables real-time translation of spoken content into different languages. This allows employees to understand spoken content in meetings held in various languages, providing real-time translation into their native language.

Speech Recognition - Speech recognition converts spoken language into text but does not translate it into another language. It would help with transcribing speech, but it wouldn't provide the real-time output in a different language.

Speech Synthesis - Speech synthesis, also known as Text-to-Speech (TTS), converts written text into audible speech, but it doesn't translate spoken content into another language.

Text-to-Speech (TTS) - Text-to-Speech (TTS) converts written text into spoken words but doesn't involve translating spoken language. It is not the correct feature for translating speech in real time during meetings.

Document intelligence workloads are designed to extract structured data from scanned forms and invoices, automating the data extraction process. This workload leverages AI to interpret and process documents, identifying key information within them.

Natural Language Processing (NLP) workloads focus on understanding and generating human language, which is more suited for text analysis tasks.

Computer Vision workloads deal with recognizing and analyzing visual content in images and videos but do not specifically address extracting structured data from documents.

Knowledge Mining workloads are used to extract insights from large volumes of unstructured data but are not specialized for processing forms and invoices.

The correct answer is Sentiment analysis. The Azure AI Language service provides sentiment analysis to evaluate the emotional tone of text. It classifies text at the document and sentence level as positive, negative, or neutral, which is ideal for analyzing customer feedback. Key phrase extraction identifies main points, not emotion. Entity recognition identifies specific people, places, and organizations. Language detection identifies the language of the text.

Advanced language models for text creation - This is the correct answer. Azure OpenAI Service offers advanced language models like GPT, which are ideal for generating personalized product descriptions based on minimal input, making it the best choice for this task.

Pre-trained image generation models - This capability is used to generate images from text descriptions, but it is not suitable for drafting personalized product descriptions.

Code completion features - This feature helps with generating code snippets and automating programming tasks, but it is not designed for creating product descriptions from text inputs.

Sentiment analysis tools - Sentiment analysis is used to assess the emotional tone of text, but it does not generate text or product descriptions.

Image classification involves assigning a label to an entire image based on its content. It analyzes the visual features of an image to determine which class it belongs to among a set of predefined categories.

The other options describe features of object detection (detecting and localizing multiple objects within an image), optical character recognition (OCR) and facial recognition (identifying individual faces within a group photo), which are different from image classification.

The Face detection service locates human faces in an image, returns a bounding-box rectangle for each face, and can optionally supply limited facial attributes such as head pose, blur, or mask information. It does not classify landmarks, perform OCR, or translate speech; those capabilities belong to other Azure Cognitive Services.

Use the service's facial attribute analysis capability (invoking the Detect API with the returnFaceAttributes parameter). In addition to the face rectangle, the call can return headPose, blur, mask, accessories, qualityForRecognition, occlusion and other attributes for every detected face, enabling per-user customization.

Face Detection without attributes supplies only bounding-box coordinates (and optionally a faceId).

Face Identification compares a detected face to a person group to find who it is.

Face Similarity Matching (Find Similar) returns faces that visually resemble a given face but does not expose individual attributes.