Microsoft Azure AI Fundamentals Practice Test (AI-900)

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.

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.

Object Detection - This is the correct answer. Object detection is a computer vision technique used to identify and locate objects within an image or video. In this case, object detection would be ideal for tracking and counting individuals in the surveillance video, as it can identify and track people as they move through the store.

Image Classification - Image classification assigns a label to an image but does not provide location information about specific objects. It would not be suitable for tracking and counting individuals in real-time surveillance footage.

Optical Character Recognition (OCR) - OCR is used to extract and recognize text from images or videos. It is not applicable for tracking or counting individuals in surveillance video.

Facial Detection and Analysis - Facial detection focuses on identifying and analyzing human faces, but it would not be the best solution for tracking and counting all individuals in a store, as it is specific to detecting faces rather than general object tracking.

This statement is correct. To ensure ongoing reliability and safety, an AI solution requires regular updates and maintenance. This is because the data and environment in which the model operates can change over time, a phenomenon known as 'data drift' or 'model drift'. These changes can degrade the model's performance and accuracy. Continuous monitoring and updating help address these shifts, fix any emerging issues, and maintain the system's effectiveness and safety. Reducing development costs or changing the fundamental architecture are not the primary goals of post-deployment maintenance.

Generative models can produce new content such as marketing slogans by learning from existing data. They can generate creative and brand-aligned slogans that resonate with the target audience, making this approach appropriate.

The design decisions focus on Inclusiveness. Microsoft's Inclusiveness principle states that AI systems should "empower everyone and engage all people, regardless of their backgrounds" and be inclusive "for people of all abilities." Adding screen-reader friendly labels, keyboard navigation, and descriptive alt-text removes barriers for users with disabilities, directly addressing this principle. The other options target different Responsible AI areas: Fairness concerns bias and equal treatment, Transparency is about making the system understandable, and Accountability relates to human oversight.

This statement is False.

Azure OpenAI Service's image generation capabilities, such as those offered by the DALL-E model, generate images based on textual descriptions provided by the user, not audio prompts. Users input text prompts that describe the desired image, and the service generates images accordingly. Audio input is not a supported method for image generation in Azure OpenAI Service.

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.

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.

In machine learning, the label is the variable that represents the output or the value the model is trying to predict. In this scenario, the model aims to predict the 'Final Selling Price' of products, making it the label.

The other variables such as 'Production Cost', 'Marketing Budget' and 'Competitor Prices' are features that the model uses to make the prediction.

Clustering is the most appropriate technique for discovering inherent groupings or patterns in data without predefined labels. In this scenario, the retailer aims to segment customers based on similarities to tailor marketing strategies for each group.

Classification involves assigning data points to predefined categories using labeled data, which is not suitable here since the categories are not known beforehand.

Regression is used for predicting continuous numerical values, not for identifying patterns or groupings.

Time Series Analysis focuses on data points collected over time, which does not align with the goal of understanding customer behavior patterns in this context.

The Azure AI Translator service specializes in converting text from one language to another, making it ideal for automatically translating product descriptions into multiple languages.

The Azure AI Speech service focuses on speech recognition and synthesis, handling audio inputs and outputs.

The Azure AI Language service deals with understanding and processing natural language but does not provide direct translation capabilities.

The Sentiment Analysis feature of the Azure AI Language service is designed to evaluate text and determine the emotional tone, categorizing it as positive, negative or neutral. This allows applications to understand the overall sentiment conveyed in customer comments.

Key Phrase Extraction identifies important terms or topics within the text but doesn't assess emotional tone.

Entity Recognition identifies and classifies named entities like people, organizations and locations within the text.

Translation converts text from one language to another and does not analyze sentiment. Therefore, sentiment analysis is the appropriate feature for determining the attitude expressed in customer comments.

Deep Learning - This is the correct answer. Deep learning is the most appropriate technique for analyzing unstructured data like images and speech, as it can automatically learn hierarchical representations from raw data. Deep learning models, especially neural networks, excel at processing and extracting meaningful features from complex data without requiring extensive manual feature engineering.

Clustering Algorithms are used for grouping data into clusters based on similarity, but they are not designed to automatically learn hierarchical representations from raw unstructured data like images and speech.

Regression Algorithms are used for predicting continuous numerical outcomes based on input features, but they are not suited for learning hierarchical representations from unstructured data.

Decision Trees are used for classification or regression tasks by splitting data into decision rules based on features. They require predefined features and are less effective than deep learning for automatically learning hierarchical representations from raw, unstructured data like images and speech.

Entity Recognition - This is the correct answer. Entity Recognition (also known as Named Entity Recognition or NER) is a natural language processing technique used to extract structured information such as names of people, places, organizations, dates and other specific entities from unstructured text documents.

Key Phrase Extraction - Key phrase extraction identifies important phrases in text but does not specifically target named entities like people, places, or dates.

Sentiment Analysis - Sentiment analysis is used to evaluate the emotional tone or sentiment (positive, negative, neutral) in text, not to extract specific entities.

Language Translation - Language translation is used to convert text from one language to another, but it does not focus on extracting structured information like names or dates.

Computer Vision's text reading capability - This is the correct answer. Computer Vision's text reading capability (including OCR features) in Azure AI services is the most suitable solution for extracting both printed and handwritten text from scanned documents and images. It can process unstructured data by detecting and reading text in various image formats, making it ideal for the organization's needs.

Form Recognizer to analyze and extract structured data - Form Recognizer is useful for extracting structured data (e.g., from forms or invoices), but it is not primarily designed to handle the extraction of general unstructured text from images, especially when dealing with a mix of printed and handwritten text.

Text Analytics to analyze and interpret text sentiment - Text Analytics is primarily used for analyzing and interpreting the sentiment, entities, or key phrases in text data, but it does not extract text from images or scanned documents.

Face API to detect and analyze faces in images - The Face API is focused on detecting and analyzing human faces in images, not for extracting text from documents or images. This is unrelated to the task of text extraction.

They can generate new data instances similar to the training data - This is the correct answer. Generative AI models are designed to create new data instances that resemble the training data. This makes them ideal for generating synthetic data to augment testing datasets, ensuring variety and realism in the data used for testing.

They can classify data into specific categories with high precision - This feature is characteristic of classification models, which categorize data into predefined labels but do not generate new data instances.

They can reduce data dimensionality while retaining key features - This describes dimensionality reduction techniques (such as PCA), which focus on simplifying the data without losing important features. It is not related to generating new data instances.

They can identify anomalies by learning normal data patterns - This is a feature of anomaly detection models, which are used to identify outliers or unusual patterns in data, not for generating synthetic data.

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.

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

The validation dataset is used to adjust model hyperparameters and prevent overfitting by evaluating the model's performance during training. It helps fine-tune the model before final testing. The training dataset is used for teaching the model, while the test dataset assesses the final performance on unseen data. Collecting new data is not related to the validation dataset's purpose.