Microsoft Azure AI Fundamentals Practice Test (AI-900)

Predicting whether an email is spam or not spam based on its content - This is the correct answer. This is a classification task, where the goal is to predict discrete class labels (spam or not spam) based on input features (the content of the email).

Predicting the future price of a stock based on historical data - This is a regression task, as predicting stock prices involves predicting a continuous numerical value rather than discrete class labels.

Identifying customer clusters based on purchasing behavior - This is a clustering task, which is an unsupervised learning technique that groups customers based on similarities in their purchasing behavior, without predefined class labels.

Estimating the total sales revenue for the next quarter - This is another regression task, as it involves predicting a continuous value (sales revenue) based on input data.

Computer Vision - This is the correct answer. An AI system that processes images to recognize and categorize objects is an example of computer vision. This type of workload focuses on enabling machines to interpret and understand visual information, such as identifying objects within images.

Natural Language Processing (NLP) is focused on understanding and generating human language, such as text or speech. It is not used for processing images.

Knowledge Mining involves extracting insights and patterns from unstructured data, such as documents and media, but it is not specifically focused on recognizing or categorizing objects within images. It may include visual data analysis but is broader in scope.

The Azure AI Vision service provides extensive image analysis capabilities, including identifying objects within images and extracting text through optical character recognition (OCR).

Azure AI Face Detection service is specialized for detecting and analyzing human faces, but doesn't support general object identification or text extraction.

Azure Form Recognizer is designed to extract structured data from forms and documents but is less suitable for general image analysis tasks.

Azure Speech to Text service is used for converting spoken language into text and does not process images.

Facial detection solutions are used to locate human faces within digital images or videos, enabling systems to process and analyze facial data. While facial recognition involves identifying and verifying individual identities, facial detection simply identifies the presence and position of faces.

Options related to recognizing identities, translating text, or detecting general objects pertain to other computer vision tasks and not specifically to facial detection.

The Codex code-davinci-002 model in Azure OpenAI Service is specifically designed for code generation tasks. It can understand and generate programming code, providing developers with code completions, suggestions, and even translating natural language descriptions into code.

While GPT-3 models like text-davinci-003 and GPT-3.5 Turbo are powerful for natural language generation, they are not optimized for code generation.

DALL·E 2 is an image generation model and does not assist with code.

Fairness - This is the correct answer. To address the issue of the recruitment system consistently selecting candidates from a single demographic group, the development team should focus on fairness. Fairness ensures that the AI system treats all candidates equitably, regardless of their demographic group, and reduces bias in decision-making.

Inclusiveness is about ensuring that diverse perspectives are considered during the development process and that AI systems are accessible to all groups. While important, it is a broader concept and does not directly address the specific issue of bias in selection leading to a lack of diversity.

Transparency refers to making the AI model’s decision-making process understandable and visible to stakeholders. While transparency is important, fairness is the key principle for addressing the lack of diversity in candidate selection.

Accountability involves ensuring that there is oversight and responsibility for the outcomes of AI systems. While important, accountability alone does not directly address the bias in selection processes; fairness is the principle most relevant to correcting this issue.

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.

The Azure AI Face Detection service is designed specifically for detecting human faces and analyzing facial features like age, emotion and gender. It provides advanced facial recognition capabilities tailored for these tasks.

The Azure AI Vision service offers general image analysis, it does not specialize in detailed facial attribute analysis.

Azure AI Form Recognizer is used for extracting text from forms and documents.

Azure AI Speech service processes and recognizes spoken language, neither of which address facial detection or analysis.

Content moderation workloads in AI are designed to automatically detect and filter inappropriate or harmful content, such as offensive language, violent images, or spam. This helps maintain a safe and welcoming environment for users.

The ability to recognize and extract printed and handwritten text from images is a key feature of optical character recognition (OCR) solutions. OCR allows for the conversion of text within images into editable and searchable data formats.

The other options describe features of different computer vision solutions, classifying images into categories is related to image classification, detecting faces and analyzing features pertains to facial detection and analysis, and segmenting and identifying objects within images is a feature of object detection solutions.

Producing new data similar to the training data - This is the correct answer. Generative AI models are designed to generate new data that is similar to the training data, such as creating new images, text, or music. They learn patterns from the input data and use this knowledge to produce new, original content.

Classifying data into predefined categories - This is a characteristic of classification models, not generative AI. Classification models categorize data into specific labels or classes, but they do not generate new data.

Detecting anomalies in data patterns - Anomaly detection is typically done by models designed for identifying outliers or unusual patterns in data, not generative AI models.

**Compressing data into lower-dimensional representations **- This is the purpose of dimensionality reduction techniques (e.g., PCA), which aim to reduce the number of features in data while preserving important information. It is not related to generative AI.

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.

Computer Vision is the appropriate AI workload for analyzing visual data to identify and track objects or patterns within images or video. In this scenario, the team needs to process visual data to recognize different animal species, which is a typical application of Computer Vision.

Natural Language Processing (NLP) deals with understanding and generating human language, which is not relevant here.

Knowledge Mining involves extracting information from large datasets but doesn't specifically handle visual recognition tasks.

Document Intelligence focuses on extracting information from documents, which does not apply to analyzing images or videos.

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.

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.