Free Microsoft Azure AI Fundamentals AI-900 Practice Test

Generative models are designed to create new content, such as text or images, based on patterns learned from existing data. In this scenario, a generative model can generate unique ad copy that aligns with client needs by learning from a corpus of advertising texts.

**Classification models **categorize data into predefined classes and are not used for content creation.

Predictive analytics models forecast future data based on historical trends but do not generate new content.

Reinforcement learning models focus on decision-making processes to achieve specific goals and are not primarily used for generating creative content.

Azure OpenAI Service's natural language generation capability allows users to generate human-like text content such as articles, summaries, and conversational responses based on input prompts.

Other options like data visualization, speech-to-text transcription, and code compilation do not facilitate the creation of such text-based content.

Azure Vision service provides image analysis features that can generate descriptive captions and extract tags from images. This capability enables the company to automatically process their product images and improve the searchability and organization of their online catalog.

Azure Face service focuses on detecting and analyzing human faces, which is not applicable for general product image analysis.

Azure Cognitive Search is a search solution but does not offer image analysis to generate captions and tags.

Azure Form Recognizer is used to extract text and data from forms and documents, not to analyze and caption images.

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.

This statement is True.

Azure OpenAI Service leverages advanced language models, such as GPT-3, to produce human-like text responses when given user input. These natural language generation capabilities enable tasks like drafting emails, writing code comments, creating conversational agents, and more.

Document Intelligence - This is the correct answer. Document Intelligence refers to AI workloads that extract data from unstructured or scanned documents and transform it into structured data. It involves techniques like optical character recognition (OCR) and data extraction to process documents, forms, and invoices into usable structured formats.

Knowledge Mining focuses on extracting insights from a variety of unstructured data sources, including documents, but it is broader and may involve additional capabilities beyond just extracting data from documents.

Natural Language Processing (NLP) is used for understanding and processing human language, primarily text, but it is not specifically focused on extracting structured data from scanned or unstructured documents.

Computer Vision deals with analyzing visual data, such as images and video, it is not primarily focused on extracting structured data from scanned documents, which is the focus of document intelligence.

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.

This statement is False.

The described technique is entity recognition, not key phrase extraction. Key phrase extraction involves extracting significant words and phrases that represent the main topics in a text, helping to summarize its content. Entity recognition, on the other hand, focuses on detecting and categorizing specific entities mentioned in the text.

The 'SellingPrice' is the label because it is the output variable that the model aims to predict. In supervised learning, the label is the target variable, and the features are input variables that help predict the label.

'SizeInSquareFeet', 'NumberOfBedrooms', and 'LocationRating' are features used by the model to make predictions.

Regression techniques are appropriate when the target variable is a continuous numerical value, like estimating the length of hospital stay. Regression models help predict quantitative outcomes based on input features.

Classification techniques are for categorical target variables.

Clustering is for grouping data without predefined labels.

Association Rule Mining is for discovering relationships between variables in large datasets.

Including fail-safe mechanisms in the system design helps prevent the application from causing harm if it encounters unexpected situations. Minimizing transparency, reducing data diversity, and ignoring edge cases can compromise safety and lead to undesirable outcomes.

Implementing content filtering is essential to monitor and remove harmful outputs that the model might generate. Content filters help ensure that any inappropriate, biased, or offensive content is identified and prevented from reaching end-users, supporting responsible AI deployment.

While enhancing response diversity and increasing dataset size can improve model performance, they do not directly prevent harmful content. Optimizing the model for higher throughput focuses on performance rather than responsible content generation.

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.

Implementing robust encryption techniques for data at rest and in transit - This is the correct answer. To address concerns about privacy and security, the company should implement strong encryption techniques to protect user data both when it is stored at rest and when it is transmitted in transit. This ensures that sensitive data is secure and reduces the risk of unauthorized access.

Collecting as many data points as possible to improve recommendations - While collecting data may help improve recommendations, it raises privacy concerns if personal data is not properly protected or managed. This approach does not directly address the need for secure handling of user data.

Giving developers access to user data for debugging purposes - Giving developers access to user data can create significant privacy and security risks. It is important to ensure that user data is only accessible to those who have a legitimate need and that proper safeguards are in place.

Storing user data on shared servers to reduce costs - Storing user data on shared servers without adequate security measures can expose the data to higher risks of breaches and unauthorized access. It is essential to store user data securely, even if it means higher costs for private or dedicated infrastructure.

Designing user interfaces that accommodate various abilities ensures inclusiveness by allowing people with different needs, including those with disabilities, to use the solution effectively.

Focusing solely on personalization tailors the experience but may not address broader accessibility issues. Incorporating complex technical language can alienate users who are not technically proficient. Optimizing for a specific group limits the solution's accessibility to others.