Microsoft Azure AI Fundamentals Practice Test (AI-900)

Computer Vision focuses on processing and interpreting visual information from images or videos. It is the most suitable choice for analyzing video surveillance to detect suspicious activities.

Natural Language Processing deals with understanding and generating human language, which is not applicable to visual data.

Knowledge Mining involves extracting insights from large volumes of structured and unstructured data, typically text-based.

Generative AI is concerned with creating new content rather than analyzing existing footage.

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.

Translation is the Azure AI service feature that allows applications to convert text from one language to another, enabling international users to read content in their preferred language.

Language Detection identifies the language of a text but does not translate it. Sentiment Analysis determines the sentiment expressed in text, and Key Phrase Extraction identifies important phrases within text. These features do not provide translation capabilities.

Azure Translator is designed specifically for translating text between languages while maintaining the original context and meaning. It utilizes advanced neural machine translation to handle idiomatic expressions and nuances in language.

The Azure Text Analytics service's key phrase extraction identifies important terms in text but does not translate text.

The Azure Speech service for speech recognition transcribes spoken words into text but does not translate text.

Azure Text Analytics for language detection identifies the language of a given text but does not perform translation.

Clustering - This is the correct answer. Clustering is an unsupervised machine learning technique used to group data into segments based on similarities without requiring labeled data. It is ideal for grouping customers into segments based on their behavior.

Regression is used for predicting continuous numerical values based on input features, not for grouping data or segmenting customers.

Classification is a supervised learning technique where the goal is to categorize data into predefined classes. It requires labeled data, which is not available in this case.

Time Series Analysis is used to analyze data that is collected over time (e.g., stock prices, sales data) to identify trends or patterns. It is not focused on segmenting data into groups based on behavior.

Codex is the Azure OpenAI model specifically designed for code generation tasks, allowing developers to transform natural language prompts into code in various programming languages.

GPT-3's text-davinci-003 is powerful for natural language understanding and generation but is not optimized for code generation.

DALL·E is used for image generation.

Azure's Computer Vision API is intended for analyzing visual content, not generating code.

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.

Azure OpenAI Service provides natural language generation capabilities, enabling applications to produce human-like text based on prompts. This makes it suitable for creating AI chatbots that can respond to customer inquiries in a conversational manner.

Azure Cognitive Search focuses on indexing and searching data.

Azure Bot Service can host chatbots but doesn't provide the language generation itself.

Azure Cognitive Services' Speech Recognition deals with transcribing spoken words into text.

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.

Regression - This is the correct answer. Regression is the most suitable technique for estimating the future value of investments based on historical data, market trends, and economic indicators. Regression models are used to predict continuous numerical values, making them ideal for tasks like forecasting future investment values.

Classification is used for categorizing data into predefined labels or classes (e.g., spam vs. non-spam), not for predicting continuous values like the future value of investments.

Clustering is an unsupervised learning technique used to group similar data points, but it is not suited for predicting numerical outcomes such as investment values.

Association Rule Learning is used for discovering interesting relationships or patterns in data (e.g., in market basket analysis) but is not appropriate for predicting continuous variables like investment values.

Obtaining explicit consent from users and adhering to relevant data protection laws like GDPR is the best approach to ensure compliance when processing personal data. This involves informing users about how their data will be used and ensuring all data handling practices meet legal requirements. While encryption, data anonymization, and restricting data collection are important measures, they alone may not fulfill all legal obligations under privacy laws.

Text-to-Speech (TTS) conversion - This is the correct answer. Text-to-Speech (TTS) conversion is the capability that allows applications to generate audible speech from textual content. This is part of Azure AI Speech, which converts written text into natural-sounding spoken language.

Speech-to-Text (STT) conversion - Speech-to-Text (STT) conversion is the reverse process, where spoken language is converted into written text, not the generation of audible speech from text.

Entity Recognition - Entity recognition is part of Azure AI Language, which identifies specific entities like names, dates, or locations within text but does not generate audible speech.

Key Phrase Extraction - Key phrase extraction is a feature of Azure AI Language that identifies important phrases within a text document, but it is unrelated to generating speech from text.

Removing or masking personally identifiable information (PII) before feature engineering-through data anonymization or strong redaction-ensures that customer identities cannot be recovered, so the training data is no longer classified as personal data under GDPR. Encryption alone protects data at rest and in transit but exposes PII once the data are decrypted inside the training pipeline. Reversible hashing (pseudonymization) still permits re-identification if the lookup table is compromised. Aggregating the data into population-level statistics removes all personalization signals, making it unsuitable for a recommender system.

The ability to generate data by learning the data distributions is essential for synthesizing new content. Generative models possess this feature, allowing them to create new data instances similar to the training data.

In contrast, classifying data into predefined classes, predicting continuous values, and identifying clusters involve analyzing existing data without producing new content.

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.

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.

They should use Computer Vision. This workload enables machines to interpret and understand visual information from images or videos. In this scenario, the retail company aims to analyze images from cameras to identify and categorize products, which is a typical application of Computer Vision.

Natural Language Processing deals with understanding and processing human language, which is not applicable here.

Knowledge Mining involves extracting information from large volumes of text but doesn't directly address image analysis.

Generative AI focuses on creating new content, such as text or images, and is not suited for product identification tasks.

Leverage the service's ability to generate images from text descriptions - This is the correct answer. Azure OpenAI Service provides models like DALL·E, which can generate images based on descriptive text inputs.

Use code generation features to create image-rendering scripts - This feature is focused on generating code for specific tasks

Apply language translation capabilities to interpret user inputs - Language translation is used for converting text from one language to another

Implement image analysis to extract information from images - Image analysis focuses on understanding and processing images

Content Synthesis tasks - This is the correct answer. Content synthesis tasks involve generating new content that is similar to the examples the model has been trained on. This is typically seen in generative models, where the system can create new, original content such as text, images, or music based on the patterns it has learned.

Anomaly Detection focuses on identifying outliers or unusual patterns in data, not on generating new content. It is used for detecting abnormal behavior or data points.

Data Classification involves categorizing data into predefined classes or categories, such as labeling emails as spam or not. It does not involve generating new content.

Regression Analysis is used to predict numerical values based on historical data, such as forecasting sales or stock prices. It is not focused on generating new content.

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

Azure AI Document Intelligence 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.