Microsoft Azure AI Fundamentals Practice Test (AI-900)

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.

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.

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.

Generating personalized product descriptions uses a text-generation model that creates new, tailored content for each visitor based on patterns learned from their browsing data. This is a clear example of generative AI, which focuses on producing novel outputs rather than selecting from fixed templates.

The other options are not generative:

• A scripted chatbot simply retrieves predefined replies, so no new content is generated.
• Analyzing purchasing patterns to recommend items is predictive analytics, concerned with forecasting rather than content creation.
• Image recognition that categorizes uploads is a classification task in computer vision, not content generation.

Entity Recognition is the Azure AI feature that identifies and classifies named entities in text, such as names of people, organizations, locations, and dates. This allows unstructured text data to be converted into structured information that can be analyzed and used for insights.

Sentiment Analysis determines the emotional tone (positive, negative, neutral) expressed in text but does not extract specific entities.

Key Phrase Extraction identifies the main points or key phrases in text but does not categorize them into entity types.

Language Modeling involves understanding and generating human language but is not used for extracting and classifying entities from text.

The DALL·E model within Azure OpenAI Service is specifically designed for generating images from textual descriptions. It enables users to create a variety of visual content based on prompts.

GPT-3 is optimized for natural language processing and text generation.

Codex is tailored for code generation tasks.

Text Analytics is used for language understanding but does not generate images.

Using a training dataset that covers all relevant demographic groups exposes the model to varied examples and reduces the chance that it learns patterns that favor or exclude particular populations.

Reducing the sampling temperature only makes outputs more deterministic; it does not address underlying bias in the learned parameters.

Removing outliers that correspond to minority groups shrinks representation and tends to worsen bias.

Simply increasing the depth or parameter count of the network can even amplify existing bias if the data remain unbalanced.

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.

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.

Azure OpenAI Service offers advanced language models that can generate human-like text based on input prompts, making it suitable for content creation and code suggestions. The other services listed do not provide text generation capabilities.

Azure Translator Service is designed for translating text between languages.

Azure Cognitive Search enables indexing and querying of content.

Azure Text Analytics provides features like sentiment analysis and key phrase extraction but does not generate new text.

This statement is False.

Filtering out offensive or explicit material from user-generated content is the function of content moderation systems, not content recommendation systems. Content recommendation systems are used to personalize user experiences by suggesting content based on user preferences and behavior.

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.

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.

Image Classification solution - This is the correct answer. Image classification is the most appropriate solution for determining the overall theme or subject of an image and assigning it to a predefined group. The model will classify the image as belonging to one of the predefined categories based on the visual content of the image for example, "nature," "architecture," "people".

Object Detection solution - Object detection not only classifies objects but also locates them within the image using bounding boxes. This solution is more suited for identifying and locating multiple objects within an image rather than assigning the image to a single predefined group.

Optical Character Recognition (OCR) solution - OCR is used to extract and recognize text from images or scanned documents, not to classify images into thematic groups based on visual content.

Facial Detection solution - Facial detection is specialized in detecting faces within an image, which is not suited for classifying images into broader thematic groups.

To promote fairness, the team should assess the model's performance across different demographic groups to identify and mitigate potential biases. Simply increasing the dataset size may not address underlying biases present in the data. Excluding sensitive attributes without considering their indirect influence can still lead to biased outcomes. Focusing solely on accuracy may neglect fairness considerations, potentially disadvantaging certain groups.

Utilize the text generation capabilities of Azure OpenAI Service - This is the correct answer. The text generation capabilities of Azure OpenAI Service, such as models like GPT, are ideal for building a chatbot that provides human-like answers to customer queries. These models can generate coherent, contextually relevant responses based on the input provided.

Employ the code generation features of Azure OpenAI Service - This feature is focused on generating code from natural language prompts.

Leverage the image creation functions of Azure OpenAI Service - Image creation functions like DALL·E are used for generating images from text descriptions

Use Azure's QnA Maker to fetch answers from a knowledge base - While QnA Maker is useful for creating a FAQ-style system based on a knowledge base, it does not offer the same level of dynamic, human-like conversation generation as the text generation models in Azure OpenAI Service.

Evaluate and adjust the training data to remove discriminatory patterns - This is the correct answer. Ensuring that the training data is free from biases and discriminatory patterns is essential for responsible AI. This helps ensure that the job descriptions generated by the model do not inadvertently favor one group over another, promoting fairness and equity.

Increase the model's vocabulary to include industry-specific terms - While increasing vocabulary can improve the model's relevance to specific industries, it does not directly address equity concerns in the generated content.

Optimize the model's performance to generate descriptions faster - Performance optimization for speed may improve efficiency but does not directly relate to ensuring equitable treatment of candidates in the generated job descriptions.

Reduce the computational resources required for deployment - Reducing computational resources can be important for cost or environmental reasons but does not directly address fairness or equity in the AI-generated job descriptions.

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.

Image Classification - This is the correct answer. Image classification is the most appropriate solution for assigning a single label to each image based on the main object or feature present in the image. It categorizes the entire image into predefined classes for example "dog," "car," "cat" without identifying specific objects' locations within the image.

Object Detection - Object detection is used for identifying and locating multiple objects within an image, often with bounding boxes. While it can classify objects, its primary focus is on locating objects, making it more complex than image classification for this task.

Optical Character Recognition (OCR) - OCR is specifically used for extracting text from images or documents, not for classifying images based on their content.

Facial Detection - Facial detection focuses on detecting human faces within images. It is not used for general image classification based on the main object in the image.

The Azure AI Language service is designed for analyzing text using natural language processing techniques, including key phrase extraction, sentiment analysis, and entity recognition. It allows users to process large volumes of text data to gain insights into the content.

The Azure AI Speech service focuses on speech-to-text and text-to-speech capabilities and is not optimized for text analysis tasks.

Azure Cognitive Search is used for adding search functionalities to applications but doesn't provide advanced text analytics out of the box.

Azure Machine Learning service is a platform for building, training, and deploying machine learning models, which would require custom development effort to perform these specific text analysis tasks.