Microsoft AI-900 Microsoft Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 10 Q136-150
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Question 136
Which Azure service can detect and recognize handwritten text in images?
A) Azure Computer Vision OCR
B) Azure Form Recognizer
C) Azure Speech-to-Text
D) Azure Cognitive Search
Answer: A) Azure Computer Vision OCR
Explanation:
The first choice provides Optical Character Recognition (OCR) capabilities specifically designed to extract text from images, scanned documents, and handwritten content. It can identify text in printed and handwritten formats, recognize characters in multiple languages, and convert them into machine-readable text. This functionality is ideal for digitizing handwritten forms, historical documents, or notes. By recognizing handwriting accurately, the service enables automated processing, search, and analysis of textual content that previously required manual transcription. It is widely used in scenarios such as digitizing student exams, processing paper-based records, and enabling accessibility for scanned materials.
The second choice extracts structured data from forms, invoices, and receipts. Azure Form Recognizer can identify fields, tables, and key-value pairs but is optimized for structured documents rather than free-form handwritten text. It works best with printed forms and pre-defined layouts and is less effective for general handwritten content.
The third choice converts spoken audio into text. Azure Speech-to-Text processes live or recorded audio but does not extract text from images or detect handwriting. Its functionality is restricted to audio-to-text conversion rather than visual recognition.
The fourth choice indexes structured and unstructured content for search. Azure Cognitive Search allows querying and searching large volumes of data but does not provide image processing or handwriting recognition capabilities. Its primary focus is content search, not text extraction from images.
The correct selection is the service that can detect, recognize, and extract handwritten text from images. Azure Computer Vision OCR enables automation, digitization, and accessibility for visual content containing handwritten text. Other services focus on structured form processing, audio transcription, or content search, making them unsuitable for handwriting recognition. Therefore, Azure Computer Vision OCR is the correct choice.
Question 137
Which Azure AI service can identify language, key phrases, and sentiment in text?
A) Azure Cognitive Services Text Analytics
B) Azure Translator Text API
C) Azure Video Analyzer
D) Azure Blob Storage
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides natural language processing capabilities that can analyze unstructured text to extract insights. It can detect the language of input text, identify key phrases, recognize entities, and determine sentiment, categorizing text as positive, negative, or neutral. These features are useful for analyzing customer reviews, social media posts, or survey responses. Text Analytics enables organizations to automate the extraction of actionable insights, integrate results into business workflows, and enhance decision-making through AI-driven text understanding.
The second choice translates text between languages. Azure Translator Text API converts text from one language to another but does not provide sentiment analysis or key phrase extraction. Its primary function is translation rather than deep text analysis.
The third choice analyzes video content for activities, objects, and people. Azure Video Analyzer focuses on visual intelligence and does not process text or provide sentiment insights. Its purpose is video-based content analysis, not natural language processing.
The fourth choice is a cloud-based object storage service. Azure Blob Storage stores documents, files, and unstructured data but does not provide AI capabilities for analyzing text or extracting sentiment. It is limited to storage and management of content.
The correct selection is the service specifically built to analyze text and extract insights like sentiment, language, and key phrases. Azure Cognitive Services Text Analytics enables organizations to efficiently process unstructured text data and gain meaningful insights. The other services focus on translation, video analysis, or storage and cannot perform text analytics. Therefore, Text Analytics is the correct choice.
Question 138
Which Azure service can translate spoken audio from one language to another in real time?
A) Azure Speech Translation
B) Azure Speech-to-Text
C) Azure Text-to-Speech
D) Azure Form Recognizer
Answer: A) Azure Speech Translation
Explanation:
The first choice combines speech recognition, translation, and speech synthesis to provide real-time translation of spoken language. It can process live conversations, meetings, or recorded audio and translate them instantly into a target language. This service is used in multilingual communication, call centers, accessibility tools, and live event translation. It ensures that users can interact across languages without delays and supports natural conversational flow, enabling real-time understanding and response in different languages.
The second choice converts spoken audio into written text. Azure Speech-to-Text transcribes audio but does not perform translation. It captures what was said in the original language rather than converting it to another language.
The third choice converts written text into spoken audio. Azure Text-to-Speech generates natural-sounding speech from text input but does not translate spoken language. It is focused on speech synthesis rather than translation.
The fourth choice extracts structured data from forms and documents. Azure Form Recognizer analyzes fields, tables, and key-value pairs but does not process audio or provide language translation. Its functionality is entirely unrelated to voice translation.
The correct selection is the service designed for real-time translation of spoken audio. Azure Speech Translation enables live communication across languages by converting speech from one language into another instantly. Other services focus on transcription, speech synthesis, or document extraction and cannot provide real-time conversational translation. Therefore, Azure Speech Translation is the correct choice.
Question 139
Which workload type is used to assign items to categories based on historical labeled data?
A) Classification
B) Regression
C) Clustering
D) Reinforcement Learning
Answer: A) Classification
Explanation:
The first choice predicts categorical outcomes based on input features. Classification uses historical labeled data to train models that assign new inputs to specific categories. For example, it can determine whether an email is spam or not, or whether a transaction is fraudulent. This is a supervised learning approach where the model learns patterns in labeled examples to make predictions for unseen data. Classification is ideal for problems with discrete outputs rather than numeric predictions.
The second choice predicts continuous numeric values. Regression estimates quantities such as revenue, temperature, or demand based on historical data. It is used for forecasting and trend prediction rather than assigning categories, making it unsuitable for categorical labeling.
The third choice groups unlabeled data into clusters based on similarity. Clustering is unsupervised learning used for segmentation or pattern discovery. It does not assign predefined categories but identifies natural groupings within data.
The fourth choice is reward-based learning where an agent interacts with an environment to maximize rewards. Reinforcement learning is applied to sequential decision-making problems such as games, robotics, or navigation and is not used for categorizing labeled data.
The correct selection is the workload type that assigns items to predefined categories using historical labeled data. Classification allows accurate categorization, supports supervised learning tasks, and is widely used in applications like fraud detection, spam filtering, and medical diagnosis. Regression, clustering, and reinforcement learning are suited for numeric prediction, grouping, or sequential decision-making, making them inappropriate for categorical assignments. Therefore, classification is the correct choice.
Question 140
Which Azure service allows anomaly detection in numeric time-series data?
A) Azure Anomaly Detector
B) Azure Form Recognizer
C) Azure Computer Vision
D) Azure Cognitive Search
Answer: A) Azure Anomaly Detector
Explanation:
The first choice identifies unusual patterns or deviations in numeric datasets over time. Azure Anomaly Detector is ideal for IoT sensor monitoring, financial transaction tracking, or operational metrics. It uses advanced algorithms to detect outliers, deviations, or unexpected trends automatically, allowing organizations to take proactive measures. It supports real-time monitoring and alerts, enabling predictive maintenance, fraud detection, and operational efficiency improvements.
The second choice extracts structured data from forms and invoices. Azure Form Recognizer identifies fields, tables, and key-value pairs but does not analyze numeric time-series data or detect anomalies. Its focus is document processing rather than pattern detection.
The third choice analyzes images and videos to detect objects, people, and activities. Azure Computer Vision focuses on visual data interpretation and cannot detect anomalies in numeric datasets. Its functionality is unrelated to time-series analysis.
The fourth choice indexes and queries structured and unstructured content. Azure Cognitive Search provides AI-powered search capabilities but does not detect anomalies in numeric data. It is designed for content retrieval rather than anomaly detection.
The correct selection is the service specifically built for detecting unusual patterns in numeric time-series data. Azure Anomaly Detector enables real-time monitoring, alerts, and insights for deviations, supporting applications such as predictive maintenance, fraud detection, and operational monitoring. Other services focus on document extraction, visual analysis, or search and are unsuitable for anomaly detection. Therefore, Azure Anomaly Detector is the correct choice.
Question 141
Which Azure service allows you to build, train, and deploy custom machine learning models at scale?
A) Azure Machine Learning
B) Azure Cognitive Search
C) Azure Form Recognizer
D) Azure Blob Storage
Answer: A) Azure Machine Learning
Explanation:
The first choice provides a comprehensive platform for building, training, and deploying machine learning models. It supports a full machine learning lifecycle, including data preparation, model experimentation, hyperparameter tuning, deployment, and monitoring. Azure Machine Learning is suitable for both code-first and low-code/no-code users, offering features like automated machine learning (AutoML) to simplify model creation. The platform integrates with other Azure services and provides tools for scalability, enabling deployment of models as APIs, batch scoring pipelines, or real-time endpoints.
The second choice provides AI-powered search and indexing of structured and unstructured content. Azure Cognitive Search enhances search experiences by integrating AI enrichment, but it does not provide a platform for building, training, or deploying custom machine learning models. Its primary function is content indexing and retrieval.
The third choice extracts structured data from documents such as forms, invoices, and receipts. Azure Form Recognizer automates data extraction and reduces manual processing, but it does not allow users to train, experiment, or deploy custom predictive models.
The fourth choice is cloud-based object storage for unstructured data, including files, logs, and media. Azure Blob Storage provides secure, scalable storage but does not provide machine learning capabilities, model training, or deployment services.
The correct selection is the service that provides an end-to-end environment for machine learning. Azure Machine Learning enables organizations to develop sophisticated models, test them at scale, and deploy them for production use. Other services focus on search, document extraction, or storage, which do not provide the comprehensive tools needed for building and operationalizing custom machine learning models. Therefore, Azure Machine Learning is the correct choice.
Question 142
Which Azure AI service allows developers to create applications that detect objects in images?
A) Azure Computer Vision
B) Azure Speech-to-Text
C) Azure Text-to-Speech
D) Azure Anomaly Detector
Answer: A) Azure Computer Vision
Explanation:
The first choice provides computer vision capabilities that allow developers to detect, classify, and analyze objects in images. It can recognize people, animals, landmarks, text, and other visual features. Azure Computer Vision supports object detection in both images and videos, providing bounding boxes and confidence scores for detected objects. This service is widely used in e-commerce for product recognition, in retail for inventory monitoring, and in security for surveillance analytics. Its prebuilt models enable developers to integrate image analysis into applications without building models from scratch.
The second choice converts spoken audio into text. Azure Speech-to-Text transcribes audio but does not process images or detect objects. Its domain is audio-to-text transcription rather than visual recognition.
The third choice converts text into natural-sounding audio. Azure Text-to-Speech provides speech synthesis for accessibility, virtual assistants, or multimedia applications but does not perform image analysis or object detection.
The fourth choice detects anomalies in numeric time-series data. Azure Anomaly Detector is designed for monitoring patterns and deviations in numerical datasets, not visual content. It cannot identify objects in images or videos.
The correct selection is the service specifically designed to interpret visual content and detect objects. Azure Computer Vision enables developers to integrate AI-powered object detection into applications efficiently. The other services focus on audio transcription, speech synthesis, or anomaly detection, none of which address image object detection. Therefore, Azure Computer Vision is the correct choice.
Question 143
Which Azure service can perform sentiment analysis on customer reviews?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Video Analyzer
D) Azure Blob Storage
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides natural language processing (NLP) capabilities to analyze unstructured text. It can determine the sentiment of customer reviews, categorizing them as positive, negative, or neutral. In addition, it can extract key phrases, recognize named entities, and detect language. Organizations use it to monitor customer satisfaction, social media feedback, and product reviews, enabling them to identify trends, address issues, and improve services. Its prebuilt models allow easy integration into applications without the need to train custom models.
The second choice extracts structured data from forms, invoices, or receipts. Azure Form Recognizer identifies tables, fields, and key-value pairs but does not analyze text sentiment. Its purpose is document processing rather than NLP.
The third choice analyzes video content to detect objects, activities, and people. Azure Video Analyzer focuses on visual content and cannot determine sentiment in textual data. Its functionality is unrelated to analyzing customer reviews.
The fourth choice is cloud-based object storage. Azure Blob Storage stores unstructured data but does not provide AI services for text analytics or sentiment detection. It serves primarily as a storage solution.
The correct selection is the service designed to extract insights from unstructured text. Azure Cognitive Services Text Analytics allows businesses to perform sentiment analysis efficiently and gain actionable insights from customer feedback. Other services focus on document extraction, video analysis, or storage and cannot provide sentiment analysis. Therefore, Text Analytics is the correct choice.
Question 144
Which Azure service can automatically extract fields and values from invoices and receipts?
A) Azure Form Recognizer
B) Azure Computer Vision
C) Azure Text-to-Speech
D) Azure Cognitive Search
Answer: A) Azure Form Recognizer
Explanation:
The first choice is designed to automate document processing by extracting structured data from invoices, receipts, and forms. It identifies fields, tables, and key-value pairs, reducing manual data entry and improving accuracy. Form Recognizer can handle different document formats, layouts, and scanned images, making it suitable for financial, accounting, and business workflows. Extracted data can be exported to databases or integrated into automated workflows, enabling faster and more efficient document management.
The second choice analyzes images and videos to detect objects, people, or text. Azure Computer Vision can extract text but is not optimized for structured data extraction from invoices or tabular fields. Its focus is general visual recognition rather than precise document field extraction.
The third choice converts written text into audio. Azure Text-to-Speech generates speech but does not extract data from documents. Its functionality is unrelated to invoice or receipt processing.
The fourth choice indexes and searches structured or unstructured data. Azure Cognitive Search enhances search capabilities but does not provide automated document field extraction. It is focused on retrieval rather than processing.
The correct selection is the service specifically built for extracting structured information from documents. Azure Form Recognizer enables automation of invoice and receipt processing, reducing errors and saving time. Other services focus on image recognition, text-to-speech, or search and cannot perform structured document extraction. Therefore, Form Recognizer is the correct choice.
Question 145
Which Azure service provides prebuilt APIs for vision, language, speech, and decision-making tasks?
A) Azure Cognitive Services
B) Azure Machine Learning
C) Azure Data Factory
D) Azure Synapse Analytics
Answer: A) Azure Cognitive Services
Explanation:
The first choice offers a suite of prebuilt APIs that provide ready-to-use AI capabilities across multiple domains. Vision APIs can analyze images, detect objects, and recognize faces. Language APIs support text analytics, translation, and conversational AI. Speech APIs enable speech recognition, synthesis, and translation. Decision APIs include anomaly detection and recommendations. These services allow developers to integrate AI into applications quickly without building custom models, reducing development time and complexity while providing scalable, enterprise-ready solutions.
The second choice is a platform for building and deploying custom machine learning models. Azure Machine Learning provides tools for data preparation, model training, and experimentation but does not provide prebuilt APIs for immediate use in vision, language, or speech tasks.
The third choice orchestrates data movement and transformation. Azure Data Factory enables ETL workflows but does not provide AI services for analyzing images, speech, or text.
The fourth choice is an analytics platform for querying and analyzing large datasets. Azure Synapse Analytics supports big data processing and analytics but does not provide prebuilt AI APIs for vision, language, or speech.
The correct selection is the service that provides ready-to-use APIs across multiple AI domains. Azure Cognitive Services enables developers to implement AI solutions quickly without requiring deep expertise in machine learning. The other services focus on model building, data integration, or analytics and are not designed for immediate AI API usage. Therefore, Azure Cognitive Services is the correct choice.
Question 141
Which Azure service allows you to build, train, and deploy custom machine learning models at scale?
A) Azure Machine Learning
B) Azure Cognitive Search
C) Azure Form Recognizer
D) Azure Blob Storage
Answer: A) Azure Machine Learning
Explanation:
The first choice provides a comprehensive platform for building, training, and deploying machine learning models. It supports a full machine learning lifecycle, including data preparation, model experimentation, hyperparameter tuning, deployment, and monitoring. Azure Machine Learning is suitable for both code-first and low-code/no-code users, offering features like automated machine learning (AutoML) to simplify model creation. The platform integrates with other Azure services and provides tools for scalability, enabling deployment of models as APIs, batch scoring pipelines, or real-time endpoints.
The second choice provides AI-powered search and indexing of structured and unstructured content. Azure Cognitive Search enhances search experiences by integrating AI enrichment, but it does not provide a platform for building, training, or deploying custom machine learning models. Its primary function is content indexing and retrieval.
The third choice extracts structured data from documents such as forms, invoices, and receipts. Azure Form Recognizer automates data extraction and reduces manual processing, but it does not allow users to train, experiment, or deploy custom predictive models.
The fourth choice is cloud-based object storage for unstructured data, including files, logs, and media. Azure Blob Storage provides secure, scalable storage but does not provide machine learning capabilities, model training, or deployment services.
The correct selection is the service that provides an end-to-end environment for machine learning. Azure Machine Learning enables organizations to develop sophisticated models, test them at scale, and deploy them for production use. Other services focus on search, document extraction, or storage, which do not provide the comprehensive tools needed for building and operationalizing custom machine learning models. Therefore, Azure Machine Learning is the correct choice.
Question 142
Which Azure AI service allows developers to create applications that detect objects in images?
A) Azure Computer Vision
B) Azure Speech-to-Text
C) Azure Text-to-Speech
D) Azure Anomaly Detector
Answer: A) Azure Computer Vision
Explanation:
The first choice provides computer vision capabilities that allow developers to detect, classify, and analyze objects in images. It can recognize people, animals, landmarks, text, and other visual features. Azure Computer Vision supports object detection in both images and videos, providing bounding boxes and confidence scores for detected objects. This service is widely used in e-commerce for product recognition, in retail for inventory monitoring, and in security for surveillance analytics. Its prebuilt models enable developers to integrate image analysis into applications without building models from scratch.
The second choice converts spoken audio into text. Azure Speech-to-Text transcribes audio but does not process images or detect objects. Its domain is audio-to-text transcription rather than visual recognition.
The third choice converts text into natural-sounding audio. Azure Text-to-Speech provides speech synthesis for accessibility, virtual assistants, or multimedia applications but does not perform image analysis or object detection.
The fourth choice detects anomalies in numeric time-series data. Azure Anomaly Detector is designed for monitoring patterns and deviations in numerical datasets, not visual content. It cannot identify objects in images or videos.
The correct selection is the service specifically designed to interpret visual content and detect objects. Azure Computer Vision enables developers to integrate AI-powered object detection into applications efficiently. The other services focus on audio transcription, speech synthesis, or anomaly detection, none of which address image object detection. Therefore, Azure Computer Vision is the correct choice.
Question 143
Which Azure service can perform sentiment analysis on customer reviews?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Video Analyzer
D) Azure Blob Storage
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides natural language processing (NLP) capabilities to analyze unstructured text. It can determine the sentiment of customer reviews, categorizing them as positive, negative, or neutral. In addition, it can extract key phrases, recognize named entities, and detect language. Organizations use it to monitor customer satisfaction, social media feedback, and product reviews, enabling them to identify trends, address issues, and improve services. Its prebuilt models allow easy integration into applications without the need to train custom models.
The second choice extracts structured data from forms, invoices, or receipts. Azure Form Recognizer identifies tables, fields, and key-value pairs but does not analyze text sentiment. Its purpose is document processing rather than NLP.
The third choice analyzes video content to detect objects, activities, and people. Azure Video Analyzer focuses on visual content and cannot determine sentiment in textual data. Its functionality is unrelated to analyzing customer reviews.
The fourth choice is cloud-based object storage. Azure Blob Storage stores unstructured data but does not provide AI services for text analytics or sentiment detection. It serves primarily as a storage solution.
The correct selection is the service designed to extract insights from unstructured text. Azure Cognitive Services Text Analytics allows businesses to perform sentiment analysis efficiently and gain actionable insights from customer feedback. Other services focus on document extraction, video analysis, or storage and cannot provide sentiment analysis. Therefore, Text Analytics is the correct choice.
Question 144
Which Azure service can automatically extract fields and values from invoices and receipts?
A) Azure Form Recognizer
B) Azure Computer Vision
C) Azure Text-to-Speech
D) Azure Cognitive Search
Answer: A) Azure Form Recognizer
Explanation:
The first choice is designed to automate document processing by extracting structured data from invoices, receipts, and forms. It identifies fields, tables, and key-value pairs, reducing manual data entry and improving accuracy. Form Recognizer can handle different document formats, layouts, and scanned images, making it suitable for financial, accounting, and business workflows. Extracted data can be exported to databases or integrated into automated workflows, enabling faster and more efficient document management.
The second choice analyzes images and videos to detect objects, people, or text. Azure Computer Vision can extract text but is not optimized for structured data extraction from invoices or tabular fields. Its focus is general visual recognition rather than precise document field extraction.
The third choice converts written text into audio. Azure Text-to-Speech generates speech but does not extract data from documents. Its functionality is unrelated to invoice or receipt processing.
The fourth choice indexes and searches structured or unstructured data. Azure Cognitive Search enhances search capabilities but does not provide automated document field extraction. It is focused on retrieval rather than processing.
The correct selection is the service specifically built for extracting structured information from documents. Azure Form Recognizer enables automation of invoice and receipt processing, reducing errors and saving time. Other services focus on image recognition, text-to-speech, or search and cannot perform structured document extraction. Therefore, Form Recognizer is the correct choice.
Question 145
Which Azure service provides prebuilt APIs for vision, language, speech, and decision-making tasks?
A) Azure Cognitive Services
B) Azure Machine Learning
C) Azure Data Factory
D) Azure Synapse Analytics
Answer: A) Azure Cognitive Services
Explanation:
The first choice offers a suite of prebuilt APIs that provide ready-to-use AI capabilities across multiple domains. Vision APIs can analyze images, detect objects, and recognize faces. Language APIs support text analytics, translation, and conversational AI. Speech APIs enable speech recognition, synthesis, and translation. Decision APIs include anomaly detection and recommendations. These services allow developers to integrate AI into applications quickly without building custom models, reducing development time and complexity while providing scalable, enterprise-ready solutions.
The second choice is a platform for building and deploying custom machine learning models. Azure Machine Learning provides tools for data preparation, model training, and experimentation but does not provide prebuilt APIs for immediate use in vision, language, or speech tasks.
The third choice orchestrates data movement and transformation. Azure Data Factory enables ETL workflows but does not provide AI services for analyzing images, speech, or text.
The fourth choice is an analytics platform for querying and analyzing large datasets. Azure Synapse Analytics supports big data processing and analytics but does not provide prebuilt AI APIs for vision, language, or speech.
The correct selection is the service that provides ready-to-use APIs across multiple AI domains. Azure Cognitive Services enables developers to implement AI solutions quickly without requiring deep expertise in machine learning. The other services focus on model building, data integration, or analytics and are not designed for immediate AI API usage. Therefore, Azure Cognitive Services is the correct choice.
Question 146
Which Azure AI service can summarize large text documents automatically?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Video Analyzer
D) Azure Blob Storage
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides natural language processing capabilities for analyzing unstructured text. It can extract key phrases, detect language, recognize entities, determine sentiment, and summarize large documents automatically. Summarization condenses long content into concise, meaningful information, making it easier to understand without reading the full document. This is useful for business reports, customer feedback, research articles, and news content. By applying AI models, Text Analytics reduces manual reading effort, identifies critical insights, and enables integration into applications for further automation or reporting.
The second choice extracts structured information from forms, invoices, and receipts. Azure Form Recognizer identifies fields, tables, and key-value pairs but does not provide text summarization for large unstructured documents. Its focus is on document processing rather than content summarization.
The third choice analyzes video content to detect objects, people, and activities. Azure Video Analyzer is focused on visual and audio streams and does not provide summarization of textual content. Its functionality is unrelated to text analysis.
The fourth choice stores unstructured data such as documents, images, and media. Azure Blob Storage provides secure and scalable storage but does not perform AI analysis or text summarization. Its primary purpose is storage and retrieval, not content interpretation.
The correct selection is the service designed to extract insights and summarize large text documents automatically. Azure Cognitive Services Text Analytics allows organizations to process and understand vast amounts of textual data efficiently. Other services focus on structured data extraction, video analysis, or storage and cannot provide automated summarization. Therefore, Text Analytics is the correct choice.
Question 147
Which Azure AI service can detect language and translate text between multiple languages?
A) Azure Translator Text API
B) Azure Speech-to-Text
C) Azure Form Recognizer
D) Azure Video Analyzer
Answer: A) Azure Translator Text API
Explanation:
The first choice allows translation of written text from one language to another. It supports multiple languages, providing developers the ability to localize content for global audiences. The service also detects the source language automatically, enabling seamless translation without prior knowledge of the input language. Translator Text API is widely used in applications such as multilingual customer support, real-time communication, and content localization, ensuring accurate and context-aware translations.
The second choice converts spoken audio into text. Azure Speech-to-Text provides transcription but does not perform translation of text between languages. Its primary function is speech recognition rather than text translation.
The third choice extracts structured information from forms and documents. Azure Form Recognizer identifies fields, tables, and key-value pairs but does not translate text or detect language. It focuses on structured document processing, not natural language translation.
The fourth choice analyzes video content for objects, activities, and people. Azure Video Analyzer is designed for visual content analysis and does not perform language detection or translation. Its functionality is limited to video intelligence.
The correct selection is the service specifically built for language detection and text translation. Azure Translator Text API enables applications to interact across languages efficiently, supporting localization and multilingual communication. Other services focus on speech transcription, document processing, or video analysis and cannot translate text. Therefore, Azure Translator Text API is the correct choice.
Question 148
Which workload type predicts a continuous numeric value from historical data?
A) Regression
B) Classification
C) Clustering
D) Reinforcement Learning
Answer: A) Regression
Explanation:
The first choice predicts continuous numeric outcomes based on input features. Regression models analyze historical patterns to forecast future values, such as sales, revenue, temperature, or demand. They estimate relationships between dependent and independent variables, enabling predictions for unobserved data. Regression is widely used in finance, marketing, operations, and scientific research to make data-driven decisions and planning.
The second choice predicts discrete categories for labeled data. Classification assigns inputs to specific categories, such as spam detection or fraud detection. It is not suitable for predicting numeric outcomes because it outputs categorical labels rather than continuous values.
The third choice groups unlabeled data into clusters based on similarity. Clustering is unsupervised learning for segmentation or pattern discovery. It does not predict numeric values but identifies natural groupings in data.
The fourth choice is reward-based learning where an agent interacts with an environment to maximize rewards. Reinforcement learning focuses on sequential decision-making and is not used for numeric prediction tasks. It involves learning policies rather than forecasting numeric outcomes.
The correct selection is the workload type designed for predicting continuous numeric values from historical data. Regression allows organizations to forecast future trends accurately, supporting informed decision-making. Classification, clustering, and reinforcement learning are unsuitable for numeric prediction tasks. Therefore, regression is the correct choice.
Question 149
Which Azure service allows real-time translation of spoken language in meetings?
A) Azure Speech Translation
B) Azure Speech-to-Text
C) Azure Text-to-Speech
D) Azure Cognitive Search
Answer: A) Azure Speech Translation
Explanation:
The first option describes a service that enables real-time translation of spoken audio from one language to another, providing a seamless solution for multilingual communication. This service integrates three key capabilities: speech recognition, language translation, and speech synthesis. Speech recognition converts the spoken words into text in the source language, translation transforms the text into the target language, and speech synthesis optionally converts the translated text back into audio. By combining these functions, the service allows participants speaking different languages to communicate instantly, making it particularly useful in live settings such as meetings, webinars, virtual conferences, and customer support calls. The real-time nature of the service ensures that conversations remain natural and fluid, preserving the flow of discussion and enabling immediate interaction without delays. It also enhances accessibility, allowing users who speak different languages or have hearing difficulties to engage effectively in global conversations. This combination of features makes the service a critical tool for organizations aiming to support international collaboration and inclusivity in communications.
The second option focuses on converting spoken audio into written text. This is achieved through speech-to-text technology, which transcribes what is spoken in the original language into text format. While this capability is valuable for creating transcripts of meetings, interviews, or calls, it does not provide translation into another language. Its functionality is limited to accurately capturing spoken content, making it suitable for documentation, record keeping, or enabling search through audio content, but it does not facilitate multilingual communication. Users can read or store the transcribed text, but they must rely on additional tools for translating it into a different language, which adds complexity and reduces the immediacy of cross-language interaction.
The third option refers to converting written text into spoken audio, a process known as text-to-speech. This capability allows applications to generate natural-sounding speech from written content, making it useful for accessibility, voice assistants, and narration of documents or digital content. While text-to-speech can produce lifelike audio from text, it does not include language translation of live speech or enable real-time conversation between speakers of different languages. Its primary focus is on generating speech from text rather than interpreting or converting speech between languages, making it unsuitable for live multilingual scenarios.
The fourth option provides AI-powered search capabilities. This service enhances content indexing and retrieval, allowing users to efficiently search through large volumes of structured and unstructured data. It is highly effective for document search, knowledge management, and enterprise content discovery. However, it cannot process or translate live audio streams, and its functionality is limited to text-based information retrieval rather than live translation. While it supports search and insight extraction from text, it does not facilitate real-time cross-language communication.
Among these four options, the service specifically designed for real-time voice translation stands out as the correct choice. It combines speech recognition, translation, and synthesis in one integrated workflow, enabling participants to communicate instantly across language barriers. By supporting live translation during meetings, webinars, and calls, it improves accessibility, promotes global collaboration, and ensures that conversations remain natural and fluid. The other options—speech-to-text, text-to-speech, and AI-powered search—serve important functions in transcription, audio generation, or information retrieval, but none of them provide the integrated, real-time translation needed for multilingual communication. Therefore, the correct selection for enabling real-time voice translation is the service that directly facilitates live cross-language conversations, which is Azure Speech Translation.
Question 150
Which Azure service can identify key entities, sentiment, and summarize text automatically?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Video Analyzer
D) Azure Machine Learning
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first option describes a service that offers natural language processing features capable of extracting meaningful insights from large amounts of unstructured text. This service can identify named entities such as people, organizations, products, and geographic locations embedded within written content. It can also evaluate sentiment to determine whether the tone of the text is positive, negative, or neutral. In addition, it can detect the language of the input and generate concise summaries of longer passages. These capabilities make it particularly valuable for businesses and organizations that handle significant volumes of text-based information, such as customer reviews, surveys, social media commentary, support tickets, reports, and internal documents. By converting raw text into structured information, the service reduces the need for manual review and increases the speed and accuracy of decision-making. It enables automated workflows, supports customer service optimization, and enhances understanding of user opinions and trends, all without requiring extensive in-house expertise in natural language processing.
The second option highlights a service that specializes in extracting structured information from documents such as forms, invoices, receipts, and other templates. This service uses machine learning to recognize fields, tables, and key-value pairs within semi-structured documents. While extremely effective for automating document processing and reducing manual data entry, it does not perform deeper language analysis. It cannot assess sentiment, extract insights from narrative text, or provide summaries. Its focus is on improving efficiency in handling documents that follow predictable layouts rather than understanding the meaning or emotional tone behind text. This makes it suitable for finance departments, administrative workflows, and data capture tasks, but not for analyzing open-ended text sources.
The third option refers to a service designed to analyze and process video content. It can detect objects, identify people, recognize activities, and extract information from visual and audio streams. Its purpose is to help organizations understand what is happening in their videos, whether for security monitoring, media analysis, or content indexing. However, even though it interprets visual scenes and may extract metadata from video files, it does not analyze text input or perform natural language processing tasks. It cannot identify named entities within written content or evaluate sentiment in text. Its capabilities belong firmly in the domain of computer vision and audio analysis, making it unrelated to text analytics.
The fourth option describes a platform for developing, training, and deploying custom machine learning models. This service is aimed at data scientists and machine learning engineers who want to build solutions tailored to specific business needs. It provides tools for managing datasets, experimenting with algorithms, tracking training runs, and deploying trained models into production. While it can be used to create custom natural language processing models, it does not provide prebuilt capabilities for tasks such as entity recognition, sentiment analysis, or text summarization. Any text analytics functionality would need to be created manually, requiring additional time, skills, and resources. Therefore, this platform is powerful but not immediately applicable for organizations seeking ready-made text insights.
After comparing all four options, it becomes clear that only the first service directly addresses the need for automatic extraction of insights from unstructured text. It delivers named entity recognition, sentiment evaluation, language detection, and summarization out of the box, allowing organizations to implement text analytics quickly and effectively. The remaining options each serve different functions: document extraction, video analysis, and custom model development. None of them provide immediate natural language understanding capabilities. For this reason, the service dedicated to text analytics is the correct choice, as it offers the most relevant and efficient solution for analyzing and interpreting textual information.