Microsoft AI-900 Microsoft Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 5 Q61-75

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Question 61

Which Azure service can extract key insights from unstructured text such as emails, reviews, or social media posts?

A) Text Analytics
B) Form Recognizer
C) Computer Vision
D) Translator Text API

Answer: A) Text Analytics

Explanation:

Text Analytics can process unstructured text to extract key insights, including sentiment, entities, key phrases, and language. Form Recognizer extracts structured data from forms and documents but does not provide insights from unstructured text. Computer Vision analyzes visual content like images and videos, which is unrelated to text processing. Translator Text API converts text between languages but does not analyze sentiment or extract entities. Text Analytics is widely used in customer feedback analysis, social media monitoring, and knowledge discovery. By analyzing unstructured data, organizations can understand customer opinions, identify trends, and automate content categorization. It supports large-scale processing of textual data, providing actionable insights without manual review. This capability allows businesses to respond proactively to customer needs, optimize marketing strategies, and monitor brand perception in real time.

Question 62

Which Azure AI service can automatically extract structured data from receipts and invoices?

A) Form Recognizer
B) Text Analytics
C) Computer Vision
D) Azure Bot Service

Answer: A) Form Recognizer

Explanation:

Form Recognizer extracts structured information from documents, including receipts, invoices, and forms, using machine learning models. Text Analytics analyzes unstructured text but does not extract structured form data. Computer Vision can detect text from images but is not optimized for tables, key-value pairs, or structured document layouts. Azure Bot Service focuses on conversational AI and cannot extract structured data from forms. Form Recognizer can identify fields, tables, and key-value pairs, automating workflows and reducing manual data entry errors. It is widely applied in finance, healthcare, and administrative workflows. By leveraging Form Recognizer, organizations can efficiently digitize documents, accelerate processing, and integrate extracted data into downstream applications or databases.

Question 63

Which AI workload focuses on identifying patterns in data without predefined labels?

A) Unsupervised learning
B) Supervised learning
C) Regression
D) Reinforcement learning

Answer: A) Unsupervised learning

Explanation:

Unsupervised learning identifies patterns, clusters, or relationships in data without using predefined labels or outcomes. Supervised learning requires labeled data to train models for prediction. Regression predicts continuous numeric values, not patterns in unlabeled data. Reinforcement learning involves learning optimal actions through rewards and penalties, rather than discovering inherent patterns. Unsupervised learning is commonly used for customer segmentation, anomaly detection, and market analysis. Azure Machine Learning provides algorithms like K-Means and hierarchical clustering for analyzing unlabeled datasets. By using unsupervised learning, organizations can uncover hidden trends, detect anomalies, and make informed decisions without needing pre-labeled datasets. It is ideal for exploratory analysis and discovering structures in large volumes of data.

Question 64

Which Azure service enables chatbots to understand natural language input and identify user intent?

A) Language Understanding (LUIS)
B) Text Analytics
C) Computer Vision
D) Form Recognizer

Answer: A) Language Understanding (LUIS)

Explanation:

LUIS interprets user input in natural language, identifying intents and extracting entities to enable intelligent conversational interactions. Text Analytics analyzes sentiment, key phrases, or language but does not provide intent recognition for conversation. Computer Vision analyzes images and videos and cannot process textual intent. Form Recognizer extracts structured data from documents but does not interpret conversations. LUIS is often integrated with Azure Bot Service to create context-aware bots capable of understanding queries, guiding users, and performing tasks automatically. By using LUIS, developers can build sophisticated conversational AI that responds accurately, improving user experience, reducing operational overhead, and enabling scalable customer support solutions.

Question 65

Which AI workload is best suited for detecting fraudulent transactions in financial data?

A) Predictive analytics using machine learning
B) Computer vision
C) Natural language processing
D) Knowledge mining

Answer: A) Predictive analytics using machine learning

Explanation:

Predictive analytics leverages historical data to forecast outcomes and detect anomalies, making it ideal for identifying fraudulent transactions. Computer vision analyzes images or video, which is not applicable for transaction data. Natural language processing analyzes text and language, not numeric financial patterns. Knowledge mining extracts insights from structured or unstructured data but does not inherently predict anomalies. Machine learning models, such as decision trees, logistic regression, or neural networks, can learn from historical fraudulent patterns to detect suspicious transactions in real time. Azure Machine Learning supports building and deploying predictive models that monitor financial data streams. By using predictive analytics, organizations can reduce losses, detect fraud efficiently, and respond proactively to suspicious activity, improving security and trust in financial systems.

Question 66

Which Azure service can translate text between multiple languages?

A) Translator Text API
B) Text Analytics
C) Form Recognizer
D) Computer Vision

Answer: A) Translator Text API

Explanation:

Translator Text API enables text translation between multiple languages, allowing applications to support global users. Text Analytics analyzes sentiment, key phrases, or entities but does not perform translation. Form Recognizer extracts structured data from forms but cannot translate content. Computer Vision analyzes visual data and does not process text for translation. Translator Text API uses neural machine translation models to deliver context-aware, high-quality translations. It supports batch processing, real-time translation, and integration into web or mobile applications. By leveraging Translator Text API, organizations can break language barriers, provide multilingual support, and deliver localized experiences efficiently across diverse user bases.

Question 67

Which AI service can identify objects and text within images?

A) Computer Vision
B) Text Analytics
C) Form Recognizer
D) Azure Bot Service

Answer: A) Computer Vision

Explanation:

Computer Vision is designed to detect objects, read printed or handwritten text (OCR), and analyze scenes in images or videos. Text Analytics analyzes unstructured text but cannot process images. Form Recognizer extracts structured data from forms and documents but is not intended for general image analysis. Azure Bot Service builds conversational AI agents but cannot identify objects or text within images. Computer Vision APIs allow developers to add image understanding capabilities to applications for scenarios like content moderation, accessibility, inventory management, and visual search. By leveraging pre-trained models, developers can extract meaningful insights from visual content without building complex models from scratch.

Question 68

Which machine learning task predicts a continuous numeric value based on historical data?

A) Regression
B) Classification
C) Clustering
D) Reinforcement learning

Answer: A) Regression

Explanation:

Regression is a foundational technique in machine learning that focuses on predicting continuous numeric outcomes based on input data. Unlike classification, which predicts discrete categories or labels, regression estimates numerical values such as sales figures, product prices, demand forecasts, or energy consumption levels. By modeling the relationship between independent variables and a target variable, regression allows organizations to make data-driven predictions about future outcomes. This predictive capability is essential across many industries, enabling companies to plan effectively, optimize resources, and respond proactively to changing conditions.

Regression is distinct from other machine learning approaches, each of which serves a specific purpose. Classification is used to assign data points to predefined categories, such as labeling emails as spam or non-spam, predicting whether a customer will churn, or determining loan approval status. Clustering, an unsupervised learning technique, groups similar data points together without requiring labels, making it useful for customer segmentation, anomaly detection, and market analysis. Reinforcement learning focuses on training models to optimize actions within an environment by using rewards and penalties, rather than predicting numeric outcomes. While each of these methods is powerful in its own context, regression uniquely addresses the need to forecast continuous numerical values.

Regression has wide-ranging applications in real-world business and operational scenarios. In sales and marketing, regression models can forecast future revenue based on historical sales data, seasonal trends, promotional campaigns, and external economic indicators. In finance, regression is used to predict asset prices, investment returns, and risk factors, enabling informed decision-making and strategic planning. In supply chain management, regression can forecast product demand, helping organizations optimize inventory, reduce waste, and improve logistics. Energy providers rely on regression models to estimate consumption patterns, allowing for efficient production planning and grid management. By analyzing historical trends and identifying underlying patterns, regression provides actionable insights that help organizations anticipate future developments and make proactive decisions.

Azure Machine Learning offers a comprehensive platform for building, training, and deploying regression models. The platform supports a wide range of regression algorithms, including linear regression, decision trees, random forests, and neural networks. Linear regression provides a simple, interpretable approach for modeling relationships between variables, making it ideal for straightforward predictive tasks. Decision trees and random forests handle more complex, nonlinear relationships and are robust against noisy or high-dimensional data. Neural networks offer advanced capabilities for modeling intricate patterns in large datasets, capturing subtle relationships that simpler models may miss. Azure Machine Learning also provides tools for data preprocessing, feature selection, model evaluation, and deployment, allowing organizations to implement scalable and reliable regression solutions.

By leveraging regression models in Azure Machine Learning, organizations can transform historical data into predictive insights. These models enable accurate forecasting, better resource allocation, and data-driven strategic planning. For example, retailers can use regression to plan inventory levels for upcoming seasons, financial analysts can anticipate market trends, and energy companies can optimize supply to meet projected demand. Continuous monitoring and retraining of regression models further enhance predictive accuracy as new data becomes available.

regression is a critical machine learning technique for predicting continuous numeric outcomes. Unlike classification, clustering, or reinforcement learning, regression focuses on forecasting numerical values based on historical data. By utilizing regression algorithms such as linear regression, decision trees, and neural networks in Azure Machine Learning, organizations can gain predictive insights, anticipate future trends, optimize operations, and make informed, data-driven decisions across industries.

Question 69

Which AI service allows applications to summarize long documents automatically?

A) Text Analytics
B) Form Recognizer
C) Computer Vision
D) Translator Text API

Answer: A) Text Analytics

Explanation:

Text Analytics is a robust artificial intelligence service that enables organizations to process, analyze, and extract valuable insights from large volumes of textual data. One of its most powerful features is text summarization, which allows long documents, reports, or extensive textual content to be condensed into shorter, more manageable summaries while retaining the essential meaning and context. By identifying key points, important phrases, and core concepts, text summarization helps users quickly understand the main ideas of a document, reducing the time and effort required to read and interpret complex content. This capability is particularly valuable in business, research, and operational contexts where large amounts of textual information are generated daily.

Unlike Text Analytics, other Azure services focus on different types of data processing and do not provide text summarization capabilities. Form Recognizer, for instance, specializes in extracting structured information from forms, invoices, and tables but does not analyze or condense unstructured text for summaries. Computer Vision processes visual content such as images and videos, performing tasks like object detection or OCR, but it cannot summarize textual content. Similarly, the Translator Text API converts text between languages, enabling multilingual communication, but it does not condense or summarize content. These differences highlight Text Analytics’ unique role in generating concise, meaningful insights from unstructured text.

Summarization in Text Analytics relies on natural language processing (NLP) techniques to identify the most relevant sentences, phrases, and concepts in a document. The service evaluates the context and importance of each piece of information, allowing it to create summaries that maintain coherence and preserve the overall meaning of the text. This approach enables organizations to transform dense, unstructured content into actionable insights, making it easier for teams to extract critical information and make informed decisions. The automated nature of summarization also minimizes human effort, reduces errors, and accelerates workflows, particularly when dealing with high volumes of text.

The applications of text summarization are broad and impactful across industries. In customer feedback analysis, businesses can quickly identify trends, concerns, or areas of satisfaction from reviews, surveys, or support tickets. Summarization allows decision-makers to focus on the most critical insights without manually reading each submission. In research and academic settings, summarization enables scholars to condense long research papers, technical reports, or literature reviews into concise summaries, facilitating quicker comprehension and easier comparison of findings. In corporate environments, summarization helps employees process lengthy reports, meeting notes, or internal documentation efficiently, ensuring that key information is easily accessible. Knowledge management systems also benefit from summarization by providing quick overviews of organizational documents, enabling faster retrieval and better understanding of information.

By leveraging Text Analytics for summarization, organizations save significant time, reduce cognitive load, and improve overall productivity. Automated summaries enable faster decision-making, allow teams to prioritize critical information, and support data-driven strategies. Summarization also enhances comprehension by highlighting the most relevant content, ensuring that insights are not lost in lengthy documents.

Text Analytics provides a powerful solution for automatically summarizing textual content, extracting key points, and condensing information for easier understanding. Unlike Form Recognizer, Computer Vision, or Translator Text API, which focus on forms, images, or translation, Text Analytics is specifically designed to process and summarize text. By using NLP-based summarization, organizations can efficiently manage large volumes of text, improve comprehension, save time, and make faster, data-driven decisions across a variety of applications, including customer feedback analysis, reports, research papers, and knowledge management.

Question 70

Which Azure AI service can detect sentiment, key phrases, and entities in text?

A) Text Analytics
B) Form Recognizer
C) Computer Vision
D) Translator Text API

Answer: A) Text Analytics

Explanation:

Text Analytics can detect sentiment, extract key phrases, and identify entities from unstructured text, providing actionable insights. Form Recognizer extracts structured data from documents but does not analyze textual sentiment or entities. Computer Vision analyzes images and videos rather than text. Translator Text API translates text between languages but does not extract insights. By using Text Analytics, organizations can process customer feedback, social media posts, and surveys to understand opinions, trends, and key information. This enables data-driven decisions, improved customer experience, and scalable insights across large datasets.

Question 71

Which Azure service can automatically detect anomalies in time-series data from sensors or metrics?

A) Anomaly Detector
B) Text Analytics
C) Form Recognizer
D) Translator Text API

Answer: A) Anomaly Detector

Explanation:

Anomaly Detector identifies unusual patterns or outliers in numeric or time-series data. Text Analytics analyzes textual data but cannot detect anomalies in metrics. Form Recognizer extracts structured information from forms and documents but does not process numeric time-series data. Translator Text API translates text but does not provide anomaly detection. Anomaly Detector leverages machine learning to learn normal patterns from historical data and flag deviations in real time. It is used in scenarios such as IoT device monitoring, financial fraud detection, and operational performance tracking. By detecting anomalies early, organizations can respond proactively to potential issues, optimize maintenance schedules, and reduce operational risk. The service is designed for scalability and ease of integration, allowing developers to monitor large datasets without requiring deep AI expertise.

Question 72

Which AI workload is suitable for predicting whether a customer will churn?

A) Supervised learning
B) Unsupervised learning
C) Clustering
D) Knowledge mining

Answer: A) Supervised learning

Explanation:

Supervised learning uses labeled datasets where outcomes (such as churn or retention) are known. Unsupervised learning analyzes data without labels, useful for discovering patterns but not directly predicting specific outcomes. Clustering groups similar data points without labels and is unsupervised. Knowledge mining extracts insights from structured or unstructured data but does not inherently predict outcomes. In supervised learning for churn prediction, historical customer data is used to train models to recognize patterns associated with leaving or staying. Algorithms like logistic regression, decision trees, or neural networks can then predict the probability of churn for new customers. Azure Machine Learning supports building and deploying these models, helping organizations reduce attrition, retain valuable customers, and make proactive business decisions.

Question 73

Which Azure AI service can identify handwritten text in documents?

A) Form Recognizer
B) Text Analytics
C) Computer Vision
D) Translator Text API

Answer: A) Form Recognizer

Explanation:

Form Recognizer is a specialized AI service within the Azure ecosystem designed to extract structured information from a wide variety of documents, including forms, invoices, receipts, and other types of business paperwork. One of its distinguishing features is its ability to handle handwritten content, making it an essential tool for organizations that process both printed and handwritten documents. By leveraging advanced machine learning models, Form Recognizer identifies and extracts key data elements such as fields, tables, and key-value pairs, transforming unstructured or semi-structured documents into structured digital data that can be easily analyzed, stored, or integrated into other business systems.

Unlike Form Recognizer, other Azure services are optimized for different tasks and are not suitable for extracting handwritten or form-specific information. Text Analytics, for example, excels at processing unstructured text, performing tasks such as sentiment analysis, key phrase extraction, entity recognition, and language detection. However, it cannot interpret handwriting or extract structured data from images or forms. Computer Vision provides optical character recognition (OCR) capabilities that allow printed text in images to be read and converted into digital text, but it is not optimized to understand the layout of forms, tables, or handwritten entries. Similarly, Translator Text API is designed to translate written text between multiple languages, enabling cross-lingual communication, but it does not process documents to extract structured or handwritten content. These differences highlight the unique role of Form Recognizer in digitizing and automating document processing.

Form Recognizer employs machine learning models that have been trained to recognize document structures and identify the relevant information automatically. It can detect key-value pairs, which are commonly found in forms such as invoices or application forms, as well as tables that contain multiple rows and columns of data. Additionally, it is capable of reading handwritten entries with a high degree of accuracy, which is particularly valuable in industries where handwritten documentation is still common. This capability reduces the need for manual data entry, minimizes errors, and accelerates workflows, allowing organizations to process large volumes of documents quickly and efficiently.

The practical applications of Form Recognizer span multiple industries. In finance, it can be used to process invoices, receipts, and loan applications, automating data extraction for accounting, auditing, and compliance purposes. In healthcare, Form Recognizer can digitize patient intake forms, medical records, and insurance documents, ensuring accurate and timely data capture while reducing administrative overhead. Government agencies can also benefit by automating the processing of applications, permits, and tax forms, improving efficiency, reducing delays, and minimizing human error. Across all of these use cases, Form Recognizer helps organizations transition from paper-based or semi-digital workflows to fully automated, scalable solutions.

By automating the extraction of structured data from printed and handwritten documents, Form Recognizer allows organizations to save time, increase accuracy, and streamline operations. It transforms manual, error-prone processes into efficient, data-driven workflows, enabling faster decision-making and more effective resource management.

Form Recognizer is a robust tool for extracting structured information from forms, invoices, and other documents, including handwritten content. Unlike Text Analytics, Computer Vision, or Translator Text API, it is specifically designed to handle both printed and handwritten forms, detect tables, key-value pairs, and other structured elements, and automate document processing. This capability drives efficiency and accuracy in industries such as finance, healthcare, and government, helping organizations digitize paper-based workflows, reduce errors, and accelerate operational processes.

Question 74

Which AI workload is appropriate for grouping customers with similar buying habits?

A) Clustering
B) Regression
C) Classification
D) Reinforcement learning

Answer: A) Clustering

Explanation:

Clustering is an unsupervised learning technique that groups data points with similar characteristics without predefined labels. Regression predicts numeric values, not groups. Classification predicts predefined categories based on labeled data. Reinforcement learning optimizes actions through trial and error and does not group data. Clustering is widely used for market segmentation, customer profiling, and personalization. Azure Machine Learning provides clustering algorithms such as K-Means and hierarchical clustering, allowing organizations to identify natural groupings in their datasets. By using clustering, businesses can target specific customer segments, improve marketing strategies, and offer personalized recommendations based on behavior patterns.

Question 75

Which Azure service can summarize long text documents automatically?

A) Text Analytics
B) Form Recognizer
C) Computer Vision
D) Translator Text API

Answer: A) Text Analytics

Explanation:

Text Analytics is an advanced AI service that enables organizations to extract meaningful insights from large volumes of text. One of its key capabilities is summarization, which allows long documents or extensive textual content to be condensed into shorter, more manageable versions without losing the essential meaning or context. By automatically identifying the most important points and highlighting critical information, summarization helps users quickly understand the main ideas of a document, reducing the time and effort required to process complex information. This capability is particularly valuable in scenarios where large amounts of text are generated, such as customer feedback, reports, research papers, or internal knowledge repositories.

Summarization in Text Analytics leverages natural language processing (NLP) techniques to analyze text at a semantic level. The service examines sentences, paragraphs, and sections to determine the relative importance of different ideas and concepts. It can capture the main points, preserve the context, and generate concise summaries that retain the meaning of the original content. This approach enables organizations to transform dense, unstructured text into actionable insights, making it easier for decision-makers to understand critical information and respond effectively.

Other Azure services operate in related domains but do not provide text summarization capabilities. Form Recognizer, for instance, is designed to extract structured data from documents and forms, including tables, fields, and key-value pairs, but it does not analyze the content to generate summaries. Computer Vision focuses on interpreting visual content such as images and videos and cannot process textual information for summarization purposes. Similarly, the Translator Text API specializes in converting text between languages, enabling multilingual communication, but it does not condense or summarize content. These services complement Text Analytics in broader AI workflows but are not substitutes for its summarization capabilities.

The practical applications of text summarization are extensive and span multiple industries. In customer feedback analysis, businesses can quickly identify common themes, complaints, or praise from large sets of reviews or survey responses, allowing them to take immediate action to improve products or services. In corporate environments, summarization can help employees navigate lengthy reports, meeting notes, or research documents, ensuring they grasp key points without reading the entire content. Academic and research institutions can use summarization to extract the essential findings from long papers or technical reports, making it easier for scholars and students to access critical information. Knowledge management systems also benefit by creating condensed summaries of internal documents, enabling faster retrieval and understanding of organizational knowledge.

By automatically summarizing large volumes of text, organizations save significant time, reduce cognitive load, and improve comprehension. This allows teams to focus on decision-making and strategy rather than manually reading and interpreting extensive documents. Summarization also enhances productivity by enabling faster responses, better prioritization, and more informed, data-driven decisions.

Text Analytics provides robust text summarization capabilities that condense lengthy content while preserving meaning and context. Unlike Form Recognizer, Computer Vision, or Translator Text API, which focus on forms, images, or translation, Text Analytics is specifically designed to process and summarize text. By leveraging NLP-based summarization, organizations can quickly extract insights, improve understanding, streamline workflows, and make faster, more informed decisions across a variety of applications, from customer feedback analysis to research and knowledge management.

Microsoft AI-900 Microsoft Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 5 Q61-75

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