Microsoft Azure AI Fundamentals Practice Test (AI-900)

Use the service's facial attribute analysis capability (invoking the Detect API with the returnFaceAttributes parameter). In addition to the face rectangle, the call can return headPose, blur, mask, accessories, qualityForRecognition, occlusion and other attributes for every detected face, enabling per-user customization.

Face Detection without attributes supplies only bounding-box coordinates (and optionally a faceId).

Face Identification compares a detected face to a person group to find who it is.

Face Similarity Matching (Find Similar) returns faces that visually resemble a given face but does not expose individual attributes.

Entity recognition is the appropriate NLP technique for this task. It involves identifying and classifying key elements in text into predefined categories such as names of persons, organizations, locations, dates and monetary values. This allows the company to extract structured data from unstructured text.

Key Phrase Extraction identifies important terms and phrases but doesn't categorize them into specific entity types.

Sentiment Analysis determines the emotional tone behind a body of text.

Translation converts text from one language to another.

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

Sentiment analysis of customer reviews involves processing and understanding human language, which is a key aspect of natural language processing (NLP).

Recognizing objects in images is a computer vision task.

Predicting equipment failures using sensor data is related to predictive analytics.

Translating data into visual charts is data visualization.

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.

Azure Cognitive Services Translator provides a document translation feature that enables the translation of entire documents while preserving their original formatting and structure, including diagrams and other non-text elements. This is suitable for translating complex documents without losing their layout.

Azure Text Analytics is designed for text analysis tasks like sentiment analysis and key phrase extraction, not for translation.

Azure Form Recognizer extracts data from forms and documents but doesn't perform translation.

Azure Language Understanding (LUIS) helps in understanding user intents and utterances but does not offer translation services.

Object Detection is the appropriate capability because it can identify and locate multiple instances of objects within an image, providing both the class of each object and its position through bounding boxes. This enables the company to count and track products in images effectively.

Image Classification assigns a single label to an entire image without providing location data.

Optical Character Recognition (OCR) extracts text from images but doesn't detect objects.

Facial Recognition is specialized for detecting and identifying human faces, not general products.

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.

The Model registry in Azure Machine Learning provides a centralized repository to store and manage multiple versions of machine learning models. It allows teams to track model versions, annotate them with metadata, and retrieve specific versions for deployment, ensuring consistent and reproducible results.

Experiment tracking is used to record and analyze training runs but does not handle model versioning.

Data labeling service assists with annotating data for training models, not managing models themselves.

Pipeline orchestration automates machine learning workflows but does not offer model version management.

Document intelligence workloads are designed to extract structured data from scanned forms and invoices, automating the data extraction process. This workload leverages AI to interpret and process documents, identifying key information within them.

Natural Language Processing (NLP) workloads focus on understanding and generating human language, which is more suited for text analysis tasks.

Computer Vision workloads deal with recognizing and analyzing visual content in images and videos but do not specifically address extracting structured data from documents.

Knowledge Mining workloads are used to extract insights from large volumes of unstructured data but are not specialized for processing forms and invoices.

Image Classification is when a model assigns a single label to an entire image based on its overall content. It does not identify the presence or location of multiple objects within the image.

Object Detection on the other hand involves identifying and locating multiple objects within an image.

Optical Character Recognition (OCR) extracts text from images.

Semantic Segmentation assigns a class label to each pixel in the image, effectively delineating object boundaries.

Assigns one or more labels to an entire image based on its content - This is the correct answer. The primary function of an image classification solution is to assign one or more labels to an entire image based on its overall content. For example, it can classify an image as "cat," "dog," or "car" based on what is depicted in the image, without identifying specific object locations within the image.

Detects and localizes individual objects within an image - This describes object detection, which not only identifies objects but also locates them within the image using bounding boxes, whereas image classification only assigns labels to the whole image.

Extracts textual information from images - This describes Optical Character Recognition (OCR), which focuses on extracting and recognizing text from images, not classifying the entire image.

Analyzes facial features to identify individuals - This describes facial recognition, which focuses on identifying and analyzing faces within an image, not general image classification.

Speech Translation - This is the correct answer. Speech translation is the Azure AI Speech service feature that enables real-time translation of spoken content into different languages. This allows employees to understand spoken content in meetings held in various languages, providing real-time translation into their native language.

Speech Recognition - Speech recognition converts spoken language into text but does not translate it into another language. It would help with transcribing speech, but it wouldn't provide the real-time output in a different language.

Speech Synthesis - Speech synthesis, also known as Text-to-Speech (TTS), converts written text into audible speech, but it doesn't translate spoken content into another language.

Text-to-Speech (TTS) - Text-to-Speech (TTS) converts written text into spoken words but doesn't involve translating spoken language. It is not the correct feature for translating speech in real time during meetings.

Object Detection identifies multiple items within an image and provides their locations by returning bounding boxes around each detected item.

Image Classification assigns a single label to the entire image without providing location information.

Optical Character Recognition (OCR) extracts text from images.

Facial Recognition is used to identify individuals based on facial features.

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.

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.

Azure AI Vision provides pre-built features for image analysis, including object detection and generation of descriptive tags, without requiring custom model training. This makes it suitable for developers who want to quickly add image analysis capabilities to their applications.

Azure AI Face detection specializes in facial recognition and analysis, which is not needed in this scenario.

Azure AI Custom Vision allows developers to build and train custom image classification and object detection models, which is unnecessary since pre-trained models suffice.

Azure AI Form Recognizer focuses on extracting text and structure from documents, which is unrelated to analyzing images for objects and tags.

Automated Machine Learning simplifies the model development process by systematically testing multiple algorithms and hyperparameter settings to identify the best-performing model for a given dataset. It automates the time-consuming process of model selection and tuning, allowing users to focus on other tasks.

Azure Machine Learning Designer provides a visual interface to build machine learning pipelines but does not automate the selection and tuning of models.

Azure Notebooks is a cloud-based Jupyter Notebook service for writing and running code, without built-in capabilities for automated model selection.

Azure Machine Learning Interpretability offers tools to explain and interpret machine learning models but does not assist in finding the optimal model through systematic testing.

This scenario represents a Generative AI workload because the system is generating new original artwork that imitates the style of existing artists. Generative AI focuses on producing new data that shares characteristics with the training data.

Computer Vision workloads involve interpreting and analyzing visual information but not generating new images.

Knowledge Mining workloads are about extracting insights from existing data, not creating new content.

Content Moderation workloads deal with identifying and filtering inappropriate content. Therefore, the most suitable workload in this case is Generative AI.

Removing personally identifiable information (PII) from the data, also known as data anonymization, is the most effective way to protect customer privacy during model training.

While encrypting data and limiting access are important security measures, they do not prevent the AI model from potentially learning sensitive information. Using secure servers protects data from external threats but does not address privacy concerns related to data handling during training.