Microsoft Azure AI Fundamentals Practice Test (AI-900)

Azure Personalizer is designed to provide personalized experiences by learning from user behavior and preferences, making it the best choice for suggesting content based on individual interactions.

Azure Content Moderator is used for detecting and filtering inappropriate content.

Azure Cognitive Search provides indexing and search capabilities.

Azure Machine Learning is a platform for building and deploying custom machine learning models but does not offer out-of-the-box personalization features.

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.

Azure AI Document Intelligence is specifically designed to extract structured data from scanned documents like invoices and receipts. It uses machine learning models to identify and extract key-value pairs, text, and tables, transforming unstructured documents into structured data.

Azure AI Search is used for indexing and searching over large sets of data but is not the primary service for extracting structured data from document layouts.

Azure AI Language processes unstructured text to detect sentiment, key phrases, and entities but doesn't work directly with the layout and structure of scanned documents.

Azure Computer Vision OCR extracts text from images but doesn't inherently structure the data or extract key-value pairs as needed for invoices and receipts.

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.

Evaluating model performance across demographic groups and adjusting it to reduce observed disparities shows that the team is actively measuring and mitigating bias. Fairness requires equitable outcomes, not merely equal treatment. Ignoring demographics, deleting sensitive attributes, or focusing only on overall accuracy can leave hidden biases unaddressed.

Natural Language Processing (NLP) is the AI workload that enables computers to understand, interpret, and generate human language. It is used to analyze textual data like customer reviews to determine sentiments and extract key topics.

Computer Vision is used for image and video analysis.

Knowledge Mining involves extracting structured information from large datasets but doesn't specialize in sentiment analysis of text.

Content Moderation focuses on detecting and filtering inappropriate content, not on sentiment analysis or topic extraction in text.

Accountability - This is the correct answer. Accountability ensures that AI systems are developed with mechanisms for oversight, and that organizations take responsibility for the outcomes produced by these systems. It involves ensuring that AI systems are used ethically and that their creators or operators are held responsible for their impact.

Transparency focuses on making AI systems understandable and providing visibility into how they work, but it doesn't directly address the mechanisms for oversight and responsibility for outcomes.

Inclusiveness is about ensuring that AI systems are designed to be fair and accessible, considering the diverse needs of users, but it is not specifically about oversight or accountability for outcomes.

Reliability and Safety focus on ensuring that AI systems perform as expected and do not cause harm, but accountability is the key consideration for ensuring oversight and responsibility for the system's outcomes.

Include a diverse set of data points representing different demographic groups - This is the correct answer. To promote fairness in an AI solution for loan approvals, it is crucial to include a diverse set of data points that represent various demographic groups. This helps the model learn from a wide range of experiences and ensures that the system does not disproportionately favor or disadvantage any particular group.

Exclude sensitive attributes like race and gender from the training data - While excluding sensitive attributes like race and gender can prevent direct bias, it may not be enough to ensure fairness.

Use historical data without modification to reflect real-world trends - Using historical data without modification might perpetuate existing biases in the data.

Prioritize algorithm efficiency over data diversity - While efficiency is important, prioritizing it over data diversity can lead to biased or incomplete models.

Object Detection is the appropriate AI workload for detecting and classifying multiple objects within an image. It not only identifies the presence of objects but also provides their locations by drawing bounding boxes around them.

Image Classification assigns a single label to an entire image, which is insufficient when multiple objects need to be identified and classified individually.

Semantic Segmentation labels every pixel in an image, which is more detailed than necessary for this task and is computationally more intensive.

Optical Character Recognition (OCR) is used to extract text from images, which is not applicable in detecting and classifying objects in images.

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.

Knowledge mining is the appropriate AI workload because it orchestrates services such as OCR, natural language processing, and search indexing to ingest, enrich, and make large, heterogeneous document collections easily searchable.

Natural language processing focuses on understanding and generating language from already-available text but does not, by itself, provide the pipeline to ingest multiple file formats or build a search index.

Computer Vision can extract text from images and scanned pages through OCR, but on its own it lacks the enrichment and indexing steps required to turn a mixed-format content repository into a searchable knowledge base.

Content personalization tailors experiences to individual users' preferences and does not address the need to search across a document repository.

Using training data that represents a wide range of user groups and experiences helps ensure that the AI model performs fairly across different populations. This approach reduces bias and improves the equity of the AI solution, which are essential aspects of inclusiveness. While implementing advanced algorithms might enhance accuracy, it doesn't address potential biases in the data. Focusing solely on performance optimization may overlook the needs of diverse users. Designing the user interface with modern aesthetics enhances visual appeal but does not necessarily make the application more inclusive unless it also considers accessibility features.

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.

Computer Vision is the AI workload that enables machines to interpret and analyze visual data, such as radiology scans, to detect patterns and anomalies indicating diseases.

Natural Language Processing (NLP) deals with text and speech data.

Knowledge Mining involves extracting information from large datasets.

Predictive Analytics focuses on forecasting future outcomes based on data, but they do not directly process visual imagery like radiology scans.

Knowledge Mining involves using AI to extract information from large volumes of unstructured data, such as documents, and making it accessible through search and analysis. It is the appropriate workload for extracting insights from unstructured documents and making them searchable.

Natural Language Processing (NLP) focuses on understanding and generating human language but does not specifically focus on making documents searchable.

Computer Vision deals with extracting information from images and videos.

Speech Recognition converts spoken language into text.

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.

Content Moderation workloads are specifically designed to detect and filter inappropriate or offensive content in text, images, and videos. They leverage AI models trained to recognize patterns associated with hate speech, profanity, nudity, and other forms of undesirable content. Therefore, content moderation is the best choice for automatically detecting and filtering inappropriate user-generated content.

Personalization workloads focus on tailoring content or recommendations to individual users based on their preferences and behavior, which does not address the need to filter inappropriate content.

Knowledge Mining involves extracting insights from large volumes of data, which is not directly related to content filtering.

Document Intelligence focuses on processing and analyzing documents to extract structured information, which is also not relevant to detecting inappropriate content in user submissions.

Document Intelligence - This is the correct answer. Document Intelligence refers to AI workloads that extract data from unstructured or scanned documents and transform it into structured data. It involves techniques like optical character recognition (OCR) and data extraction to process documents, forms, and invoices into usable structured formats.

Knowledge Mining focuses on extracting insights from a variety of unstructured data sources, including documents, but it is broader and may involve additional capabilities beyond just extracting data from documents.

Natural Language Processing (NLP) is used for understanding and processing human language, primarily text, but it is not specifically focused on extracting structured data from scanned or unstructured documents.

Computer Vision deals with analyzing visual data, such as images and video, it is not primarily focused on extracting structured data from scanned documents, which is the focus of document intelligence.

Transparency involves making AI systems understandable and explainable to users and stakeholders. By enhancing transparency, the company allows users to comprehend how data is processed and how decisions are made, building trust in the system.

Privacy focuses on protecting personal and sensitive data, which is important but does not specifically address understanding AI decision-making processes.

Fairness aims to prevent biases and ensure equitable outcomes.

Inclusiveness ensures AI systems are accessible and beneficial to diverse users.

Natural Language Processing (NLP) is used to analyze and understand human language in text or speech form. Since the company wants to analyze textual customer reviews to determine sentiment, NLP techniques are appropriate for this task. Computer Vision focuses on visual data like images and videos, which doesn't apply to text reviews. Predictive Maintenance and Time Series Forecasting involve predicting equipment failures and future values based on time-series data, respectively, neither of which relate to analyzing text reviews for sentiment.