Microsoft Azure AI Fundamentals Practice Test (AI-900)

Key Phrase Extraction is used to identify the most significant terms and phrases within text data, helping to highlight the main topics and themes discussed. This feature enables organizations to quickly gain insights into large volumes of text by focusing on the key concepts mentioned.

Sentiment Analysis determines the emotional tone (positive, negative, neutral) of the text but does not extract specific topics.

Entity Recognition identifies and categorizes specific entities such as names, places, and organizations but may not capture the overall topics or themes.

Language Modeling predicts the likelihood of a sequence of words and is used for tasks like text generation, not topic extraction.

Deep Learning techniques excel at processing large amounts of unstructured data like images and text because they can automatically learn hierarchical representations and features directly from the data without manual feature engineering. Other techniques like logistic regression, k-means clustering, and decision trees are generally more effective on structured data and may require significant feature engineering.

Azure AI Language service provides capabilities for natural language processing tasks such as sentiment analysis, key phrase extraction, and named entity recognition. This makes it the appropriate choice for analyzing text data to gain insights from customer reviews.

Azure AI Speech service is designed for speech recognition and synthesis, which are not applicable to analyzing written text.

Azure Cognitive Search is used for indexing and searching over structured and unstructured data but does not perform text analytics tasks like sentiment analysis.

Azure Bot Service helps create conversational experiences and does not offer text analysis features.

Language Modeling is the natural processing language (NLP) technique that enables prediction of the next word or sequence of words in a text, making it ideal for features like text prediction and auto-completion. It learns patterns from large amounts of text data to anticipate user input.

Sentiment analysis identifies the emotional tone of text, key phrase extraction finds important phrases, and entity recognition detects and classifies entities like names and places; none of these are used for predicting text input.

Object Detection is the appropriate technique because it not only identifies instances of objects within an image but also provides their locations, typically using bounding boxes. This makes it suitable for locating and counting products on shelves.

Image Classification assigns a label to an entire image but does not identify individual objects within the image or their positions.

Optical Character Recognition (OCR) is used to extract text from images, which is not relevant for detecting products.

Semantic Segmentation classifies each pixel in an image, assigning a class label to every pixel, which can provide more detailed localization but is more complex than necessary for simply locating and counting objects.

To prevent the model from using too much data at once - This is incorrect. Splitting datasets does not aim to limit data usage. Instead, it ensures proper assessment of model performance and generalization.

To evaluate the model’s ability to generalize to new data - This is the correct answer. The validation dataset is used to assess a model’s performance on unseen data, ensuring it generalizes well beyond the training data.

To increase accuracy by training on multiple subsets - This is incorrect. Splitting the data is not meant to increase accuracy but to validate the model’s performance on a separate dataset.

To simplify the model by reducing the number of features - This is incorrect. Dataset splitting does not reduce the number of features but partitions the data for training and evaluation purposes.

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.

The Azure AI Language service provides pre-built natural language processing (NLP) capabilities such as key phrase extraction, entity recognition, and sentiment analysis. It supports multiple languages and can be easily integrated into applications via REST APIs. Since it offers pre-trained models, there's no need to manage infrastructure or train models from scratch, meeting the company's requirements.

Azure Cognitive Search focuses on indexing and searching content, not providing comprehensive NLP features.

Azure Machine Learning requires building and training custom models, which the company wants to avoid.

Azure AI Speech service handles speech recognition and synthesis, not text-based NLP tasks.

Key Phrase Extraction is used to identify the main topics or significant concepts in text data, helping organizations understand commonly discussed themes. This is ideal for summarizing large volumes of feedback to pinpoint areas of interest or concern.

Sentiment Analysis determines the emotional tone behind the text but doesn't highlight specific topics.

Named Entity Recognition identifies and classifies specific entities like names of people, organizations, or locations, but may miss broader topics.

Language Detection determines the language of the text and doesn't assist in extracting thematic content.

Azure Personalizer is designed to provide personalized experiences by learning from user behavior and preferences, making it the best choice for suggesting content based on individual interactions.

Azure Content Moderator is used for detecting and filtering inappropriate content.

Azure Cognitive Search provides indexing and search capabilities.

Azure Machine Learning is a platform for building and deploying custom machine learning models but does not offer out-of-the-box personalization features.

Azure Automated Machine Learning is designed to automate the process of selecting the most appropriate algorithms and tuning hyperparameters for your dataset and problem type. It iteratively trains models with different algorithms and parameters to find the best performing model.

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

Azure Machine Learning Studio Notebooks offer a coding environment for custom model development, which may not save time for those with limited expertise.

Azure Cognitive Services provide pre-built AI services but are not used for custom model training.

Key Phrase Extraction is the appropriate Azure AI capability for this scenario. It analyzes text to identify the most relevant phrases that represent the main topics or concepts within a document. These key phrases can be used as metadata tags to improve searchability and organization.

Sentiment Analysis determines the emotional tone of the text, which is not useful for identifying key concepts.

Entity Recognition identifies and categorizes specific entities like names of people, organizations, or locations but may not capture all significant terms.

Speech Recognition converts spoken language into text and is not applicable to text documents.

Clustering is the most suitable technique because it involves grouping similar data points together based on features, without using predefined labels. This allows the bank to identify distinct customer segments.

Regression is used for predicting continuous numerical values, which doesn't fit the goal of segmenting customers into categories.

Classification predicts categorical labels based on training data with known categories, but in this case, the categories (customer segments) are not predefined.

Reinforcement Learning involves an agent interacting with an environment to maximize cumulative reward, which is not applicable here.

Facial Detection and Analysis allows an application to detect human faces in images and analyze certain facial attributes. The Azure AI Face service can optionally return predictions for attributes such as age, though access to this feature is limited under Responsible AI policies.

Object Detection identifies and provides bounding boxes for various objects within an image but does not provide specific facial attribute analysis like age estimation.

Optical Character Recognition (OCR) is used to extract printed and handwritten text from images.

Image Classification assigns one or more labels to an entire image, rather than analyzing specific elements within it.

Object Detection - This is the correct answer. Object detection is the computer vision solution that identifies and locates multiple instances of different types of objects within an image. It not only detects the presence of objects but also identifies their locations using bounding boxes.

Image Classification - Image classification assigns a single label to an entire image but does not detect or locate multiple objects within the image.

Optical Character Recognition (OCR) - OCR is used to extract text from images or documents, not for detecting and locating objects.

Facial Detection and Analysis - This solution focuses specifically on detecting and analyzing faces within images, not on detecting and locating multiple types of objects in general.

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.

Facial analysis features of the Azure AI Face service detect faces in an image and can predict certain attributes-such as an estimated age-for each detected face (this attribute is available only to approved customers with limited access). Object detection locates generic objects but does not return facial attributes. Text Analytics works with text data, and Speech Recognition works with audio; neither service can estimate age from images.

Image Classification - This is the correct answer. Image classification assigns labels to images based on the overall visual content, without pinpointing the location of specific objects. It categorizes the entire image into predefined classes for example "dog," "cat," "car" but it does not locate individual objects within the image.

Object Detection involves identifying and locating specific objects within an image, often with bounding boxes, in addition to classifying the objects. This is different from image classification, which does not detect object locations.

Optical Character Recognition (OCR) is used to extract and recognize text from images, but it is not focused on classifying images or identifying overall content.

Semantic Segmentation divides an image into regions that correspond to different object categories and labels every pixel in the image, which is more detailed than image classification. It assigns labels to every part of the image, rather than classifying the entire image at once.

Azure AI Vision offers optical character recognition (OCR) features that enable the extraction of text from images and documents, making it ideal for processing printed forms.

Azure AI Face specializes in facial detection and analysis, not text extraction.

Azure AI Text Analytics analyzes text for sentiment and key phrases but does not extract text from images.

Azure AI Language Understanding focuses on interpreting conversational language, not on OCR tasks.

Azure AI Document Intelligence is specifically designed to extract structured data from scanned documents like invoices and receipts. It uses machine learning models to identify and extract key-value pairs, text, and tables, transforming unstructured documents into structured data.

Azure AI Search is used for indexing and searching over large sets of data but is not the primary service for extracting structured data from document layouts.

Azure AI Language processes unstructured text to detect sentiment, key phrases, and entities but doesn't work directly with the layout and structure of scanned documents.

Azure Computer Vision OCR extracts text from images but doesn't inherently structure the data or extract key-value pairs as needed for invoices and receipts.