Microsoft AI-900 Microsoft Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 13 Q181-195
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Question 181
Which Azure AI service can detect key phrases, entities, and sentiment in unstructured text?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Computer Vision
D) Azure Anomaly Detector
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice is designed to extract insights from unstructured text. Azure Cognitive Services Text Analytics can identify key phrases, detect named entities such as people, organizations, and locations, and determine sentiment as positive, negative, or neutral. It is widely used in analyzing customer feedback, social media posts, surveys, and business documents to gain actionable insights. Automation of these tasks reduces manual effort and accelerates decision-making. It supports multiple languages and can scale to analyze large datasets efficiently, making it ideal for enterprise applications.
The second choice extracts structured data from documents like forms, invoices, and receipts. Azure Form Recognizer identifies key-value pairs, tables, and fields, but it does not analyze textual sentiment or extract key phrases. Its focus is structured document processing rather than natural language understanding.
The third choice analyzes visual content in images and videos. Azure Computer Vision detects objects, faces, and text in images but cannot analyze unstructured text for sentiment, key phrases, or entities. Its domain is visual intelligence rather than text analytics.
The fourth choice identifies anomalies in numeric datasets. Azure Anomaly Detector monitors patterns and deviations in numeric data but cannot extract key phrases, detect entities, or analyze sentiment in text. Its functionality is unrelated to natural language processing.
The correct selection is the service designed for text analysis. Azure Cognitive Services Text Analytics allows organizations to extract entities, key phrases, and sentiment from unstructured documents efficiently. Other services focus on document extraction, visual analysis, or numeric anomaly detection and cannot provide natural language insights. Therefore, Azure Cognitive Services Text Analytics is the correct choice.
Question 182
Which Azure AI service can analyze images to detect objects, text, and people?
A) Azure Computer Vision
B) Azure Speech-to-Text
C) Azure Form Recognizer
D) Azure Cognitive Services Text Analytics
Answer: A) Azure Computer Vision
Explanation:
The first choice provides prebuilt APIs for visual intelligence. Azure Computer Vision can detect objects, identify faces, extract printed or handwritten text, and recognize people or activities in images and videos. It is commonly used in security, retail, healthcare imaging, and accessibility solutions. The service returns structured outputs including bounding boxes, confidence scores, and extracted text, which can be integrated into workflows and applications. Computer Vision can process both single images and video streams, making it versatile for real-time analysis and automated visual intelligence tasks.
The second choice converts spoken audio into written text. Azure Speech-to-Text is for audio transcription and cannot detect objects, text, or people in visual content. Its functionality is limited to speech processing.
The third choice extracts structured data from documents. Azure Form Recognizer identifies tables, fields, and key-value pairs but does not analyze visual content for objects or people. Its focus is document processing.
The fourth choice analyzes unstructured text for sentiment, key phrases, and entities. Azure Cognitive Services Text Analytics cannot process images or detect objects and people. Its domain is text analysis, not visual analysis.
The correct selection is the service built for image and video analysis. Azure Computer Vision enables object detection, text extraction, and face recognition. Other services focus on audio transcription, document extraction, or text analysis and cannot provide computer vision capabilities. Therefore, Azure Computer Vision is the correct choice.
Question 183
Which Azure AI service automatically identifies unusual patterns or trends in numeric datasets?
A) Azure Anomaly Detector
B) Azure Computer Vision
C) Azure Form Recognizer
D) Azure Cognitive Services Text Analytics
Answer: A) Azure Anomaly Detector
Explanation:
The first choice detects deviations and unusual patterns in numeric time-series data. Azure Anomaly Detector applies machine learning algorithms to monitor IoT sensor data, financial transactions, operational metrics, and predictive maintenance. It supports real-time or batch processing and provides alerts and actionable insights when anomalies are detected. The service handles trends, seasonal patterns, and complex datasets, allowing organizations to proactively respond to potential issues, prevent operational disruptions, or detect fraudulent activity.
The second choice analyzes visual content in images and videos. Azure Computer Vision cannot detect anomalies in numeric datasets. Its functionality focuses on object detection, text extraction, and visual intelligence.
The third choice extracts structured data from documents. Azure Form Recognizer identifies tables, fields, and key-value pairs but cannot monitor numeric patterns or detect anomalies. Its functionality is specific to document processing.
The fourth choice analyzes text for key phrases, entities, and sentiment. Azure Cognitive Services Text Analytics cannot monitor numeric datasets for unusual patterns. Its domain is natural language processing, not numeric anomaly detection.
The correct selection is the service specifically designed for numeric anomaly detection. Azure Anomaly Detector enables real-time monitoring, alerts, and actionable insights for IoT, finance, and operational data. Other services focus on visual analysis, document extraction, or text analytics and cannot provide numeric anomaly detection. Therefore, Azure Anomaly Detector is the correct choice.
Question 184
Which Azure AI service enables developers to build chatbots that understand user intents and extract entities?
A) Azure AI Language (LUIS)
B) Azure Computer Vision
C) Azure Form Recognizer
D) Azure Anomaly Detector
Answer: A) Azure AI Language (LUIS)
Explanation:
The first choice provides natural language understanding for conversational AI. Azure AI Language (LUIS) allows developers to define intents, entities, and utterances so chatbots can understand and respond to user input accurately. It can extract actionable information and power virtual assistants, customer support bots, and voice-driven applications. LUIS supports multiple languages and continuous learning, improving accuracy over time. Integration with other Azure services enables developers to create end-to-end intelligent applications.
The second choice analyzes visual content. Azure Computer Vision detects objects, faces, and text but cannot understand natural language or extract intents and entities. It is not designed for chatbot development.
The third choice extracts structured data from documents. Azure Form Recognizer identifies key-value pairs and tables but cannot interpret natural language or understand user input. Its focus is document processing.
The fourth choice detects anomalies in numeric datasets. Azure Anomaly Detector monitors patterns in numeric data but does not provide conversational AI functionality. Its domain is numeric monitoring rather than natural language understanding.
The correct selection is the service designed for conversational AI. Azure AI Language (LUIS) enables chatbots to understand user intents and extract relevant entities for intelligent responses. Other services focus on visual recognition, document extraction, or numeric anomaly detection and cannot handle conversational AI. Therefore, LUIS is the correct choice.
Question 185
Which Azure AI service provides prebuilt APIs for vision, speech, language, 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 prebuilt AI APIs for multiple domains. Vision APIs detect objects, recognize faces, classify images, and extract text. Language APIs provide sentiment analysis, key phrase extraction, translation, and conversational AI. Speech APIs allow speech recognition, text-to-speech, and real-time translation. Decision APIs include anomaly detection, recommendations, and predictive insights. These prebuilt APIs allow developers to quickly integrate AI into applications without training custom models, enabling rapid deployment and enterprise-ready solutions.
The second choice is a platform for building, training, and deploying machine learning models. Azure Machine Learning focuses on model development, experimentation, and deployment but does not offer prebuilt APIs for immediate use in vision, language, speech, or decision-making.
The third choice orchestrates ETL workflows. Azure Data Factory handles data movement and transformation but does not provide AI APIs for vision, language, speech, or decision-making.
The fourth choice is a data analytics platform for large-scale querying and reporting. Azure Synapse Analytics supports big data analytics and data warehousing but does not offer prebuilt AI services.
The correct selection is the service designed to provide ready-to-use AI capabilities across multiple domains. Azure Cognitive Services allows developers to implement vision, speech, language, and decision-making AI quickly. Other services focus on model training, data orchestration, or analytics and cannot provide prebuilt AI APIs. Therefore, Azure Cognitive Services is the correct choice.
Question 186
Which Azure AI service can detect objects, people, and text in images or videos?
A) Azure Computer Vision
B) Azure Speech-to-Text
C) Azure Form Recognizer
D) Azure Cognitive Services Text Analytics
Answer: A) Azure Computer Vision
Explanation:
The first option provides a comprehensive set of APIs designed to analyze visual content in both images and videos. Azure Computer Vision is capable of performing a wide range of visual intelligence tasks, including detecting objects, recognizing faces, identifying printed or handwritten text, and detecting people within visual media. This service is widely used across industries to enable automated understanding of visual data, supporting applications in security, retail, healthcare, accessibility, and more. By processing images and video frames, it delivers structured outputs such as bounding boxes, confidence scores, and extracted text, which can then be integrated into larger workflows or AI-driven applications. The ability to analyze both single images and continuous video streams makes Azure Computer Vision versatile and applicable to a variety of scenarios, from monitoring live security footage to analyzing retail shelf inventory or extracting information from visual content in healthcare imaging.
One of the key advantages of Azure Computer Vision is its ability to provide detailed, actionable insights from visual content. For instance, in retail environments, it can automatically detect and identify products on shelves, track stock levels, and support inventory management systems. In security applications, the service can recognize and track individuals in video streams, detect suspicious behavior, and trigger alerts. Healthcare providers can use it to analyze medical images, highlight areas of interest, or extract important textual information from scanned documents. Beyond these use cases, the service also supports accessibility solutions, such as generating image descriptions for visually impaired users, translating visual content into machine-readable text, and providing detailed analyses that can enhance human-computer interaction.
The second option, Azure Speech-to-Text, is focused entirely on converting spoken audio into written text. While this service is extremely useful for transcribing meetings, interviews, and voice commands, it does not have the capability to detect objects, people, or text in visual media. Its functionality is limited to audio processing, which makes it highly specialized for speech transcription and voice-driven applications, but unsuitable for tasks that require the analysis of images or video content.
The third option, Azure Form Recognizer, is designed to extract structured data from documents, forms, and receipts. It identifies tables, key-value pairs, and other important fields, enabling automated document processing and workflow integration. While this service excels at converting document content into machine-readable formats, it does not perform object detection, facial recognition, or text identification within images or video streams. Its capabilities are focused on document analysis rather than general-purpose computer vision tasks.
The fourth option, Azure Cognitive Services Text Analytics, provides tools to analyze unstructured text by detecting key phrases, sentiment, entities, and language. This service is specifically built for text processing and natural language understanding. While it is highly valuable for text-based insights and AI applications that require language comprehension, it cannot analyze visual content such as images or videos. As a result, it is not suitable for applications that require object detection, face recognition, or text extraction from visual media.
Considering these options, the correct choice is the service that is explicitly designed for analyzing visual content. Azure Computer Vision provides a robust and versatile solution for object detection, facial recognition, and text extraction from both images and video. It delivers structured outputs that can be integrated into applications, automated workflows, and AI pipelines, making it ideal for a wide range of industries and use cases. In contrast, the other options—Azure Speech-to-Text, Azure Form Recognizer, and Azure Cognitive Services Text Analytics—are specialized for audio transcription, document processing, and text analysis, respectively, and do not provide the capabilities needed for visual intelligence tasks. Therefore, for organizations and developers seeking to analyze visual content efficiently and accurately, Azure Computer Vision is the appropriate choice.
Question 187
Which Azure AI service identifies anomalies or unusual patterns in numeric datasets?
A) Azure Anomaly Detector
B) Azure Computer Vision
C) Azure Form Recognizer
D) Azure Cognitive Services Text Analytics
Answer: A) Azure Anomaly Detector
Explanation:
The first choice monitors numeric datasets to detect unusual patterns or deviations. Azure Anomaly Detector uses machine learning algorithms to detect anomalies in time-series data such as IoT sensor readings, operational metrics, or financial transactions. It supports real-time and batch processing, providing alerts and actionable insights when anomalies are detected. The service can handle seasonal trends, periodic patterns, and complex variations, allowing organizations to proactively respond to potential issues, prevent operational disruptions, or detect fraud.
The second choice analyzes images and video content. Azure Computer Vision cannot detect anomalies in numeric data. Its functionality focuses on object detection, facial recognition, and text extraction in visual media.
The third choice extracts structured data from forms, invoices, and receipts. Azure Form Recognizer identifies tables, fields, and key-value pairs but cannot analyze numeric patterns for anomalies. Its purpose is document processing, not numeric monitoring.
The fourth choice analyzes unstructured text. Azure Cognitive Services Text Analytics extracts key phrases, entities, and sentiment but does not detect numeric anomalies. Its domain is text analysis, not numeric pattern monitoring.
The correct selection is the service specifically designed for numeric anomaly detection. Azure Anomaly Detector provides real-time monitoring, alerts, and actionable insights for operational, IoT, or financial datasets. Other services focus on visual analysis, document extraction, or text analytics and cannot detect numeric anomalies. Therefore, Azure Anomaly Detector is the correct choice.
Question 188
Which Azure AI service enables developers to create conversational AI that understands intents and entities?
A) Azure AI Language (LUIS)
B) Azure Computer Vision
C) Azure Form Recognizer
D) Azure Anomaly Detector
Answer: A) Azure AI Language (LUIS)
Explanation:
The first option provides natural language understanding capabilities that are specifically designed to enable the development of conversational AI applications. Azure AI Language, formerly known as LUIS, is a service that allows developers to create chatbots, virtual assistants, and other voice-driven applications that can understand and respond intelligently to user input. The service works by enabling developers to define user intents, which represent the actions a user wants to perform, as well as entities, which are the pieces of information that provide context or detail to the intent. By providing example utterances that represent how users might phrase their requests, LUIS can learn to accurately interpret varied forms of input and extract the relevant information needed to take appropriate action.
LUIS is capable of processing both text and spoken input, making it highly versatile for different types of conversational applications. For example, in a customer support chatbot, LUIS can understand a request such as “I want to change my delivery address” by identifying the intent as a request to update shipping information and extracting entities such as the new address. This enables the chatbot to respond correctly or initiate the required backend process. Similarly, virtual assistants can use LUIS to comprehend commands, answer questions, and guide users through workflows by analyzing natural language input. The service is designed for continuous learning, which means it improves over time as it encounters more user interactions. By analyzing patterns in user input, refining intents and entities, and incorporating corrections, LUIS can enhance its accuracy and responsiveness, providing a more seamless conversational experience. It also supports multiple languages, making it suitable for global applications.
Another significant advantage of Azure AI Language is its ability to integrate with other Azure services, creating a comprehensive AI-driven ecosystem. For example, chatbots built with LUIS can be integrated with Azure Bot Service for deployment across web, mobile, and voice channels. Combined with Azure Cognitive Services, such as Speech-to-Text and Text-to-Speech, applications can process spoken input, convert it to text for understanding, and respond with natural-sounding speech. This integration allows developers to build end-to-end conversational AI solutions that are sophisticated, scalable, and capable of handling a wide range of user interactions.
The second option, Azure Computer Vision, focuses on analyzing images and video rather than understanding language. While it provides powerful capabilities such as object detection, facial recognition, and text extraction from visual content, it does not process natural language or extract intents and entities from user input. As such, it cannot power chatbots or virtual assistants and is not suitable for conversational AI development.
The third option, Azure Form Recognizer, is designed to extract structured data from forms, invoices, and receipts. It can identify tables, key-value pairs, and other fields from scanned or digital documents. While extremely useful for automating document processing, Form Recognizer does not interpret natural language or enable conversational interactions, limiting its application to document-focused tasks rather than AI-driven communication.
The fourth option, Azure Anomaly Detector, is focused on numeric datasets. It monitors trends in time-series data and detects anomalies, providing alerts and actionable insights for operational metrics, IoT data, or financial transactions. While valuable for monitoring and detecting unexpected patterns in numeric data, it has no capabilities related to conversational AI or natural language understanding.
Considering all options, the correct selection is the service designed specifically for natural language understanding and conversational AI. Azure AI Language (LUIS) enables developers to create applications that understand user intents, extract relevant entities, and respond intelligently to both text and spoken input. It supports continuous learning, multiple languages, and integration with other Azure services to provide complete, AI-driven conversational solutions. Other services, including Azure Computer Vision, Azure Form Recognizer, and Azure Anomaly Detector, focus on visual recognition, document extraction, and numeric anomaly detection, respectively, and do not offer capabilities for building chatbots or virtual assistants. Therefore, Azure AI Language (LUIS) is the appropriate and most effective choice for creating applications that require conversational intelligence and natural language understanding.
Question 189
Which Azure AI service can summarize text, detect sentiment, and extract entities from unstructured documents?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Computer Vision
D) Azure Anomaly Detector
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides a comprehensive solution for analyzing unstructured text through natural language processing capabilities. Azure Cognitive Services Text Analytics is specifically designed to extract meaningful insights from textual data, enabling organizations to process large volumes of information efficiently and intelligently. This service can perform a variety of critical functions on text, including summarization of lengthy documents, extraction of key phrases, identification of named entities such as people, organizations, locations, dates, and more, and determination of sentiment, classifying text as positive, negative, or neutral. These capabilities make it a powerful tool for understanding the content and intent of textual data across a wide range of use cases.
Text Analytics is widely employed in applications such as customer feedback analysis, surveys, social media monitoring, and document summarization. For instance, businesses can automatically analyze customer reviews or survey responses to identify common complaints, praise, or emerging trends. By extracting key phrases and entities, organizations can pinpoint recurring topics and understand which aspects of their products or services resonate most with customers. Sentiment analysis adds another layer of intelligence, allowing companies to gauge the emotional tone of customer feedback. This automation not only saves significant time compared to manual review but also ensures that insights are consistent, unbiased, and actionable, supporting data-driven decision-making at scale.
In addition to customer experience applications, Text Analytics is valuable for internal document processing. Organizations often deal with vast volumes of unstructured data such as reports, emails, meeting notes, or technical documentation. Azure Cognitive Services Text Analytics can process these documents to generate concise summaries, helping employees quickly grasp the essential points without needing to read entire reports. Named entity recognition further allows companies to identify critical information, such as project names, deadlines, or personnel involved, enhancing operational efficiency and improving knowledge management. Its ability to handle multiple languages and large-scale datasets makes it suitable for enterprise environments with global operations, ensuring consistent performance and insight generation regardless of the size or complexity of the dataset.
The second choice, Azure Form Recognizer, focuses on extracting structured data from documents such as forms, receipts, or invoices. Form Recognizer identifies tables, fields, and key-value pairs and is extremely useful for automating document workflows where specific data points must be extracted and processed. While it excels in structured document extraction, it does not provide natural language processing capabilities such as summarization, sentiment analysis, or entity extraction from unstructured text. Its core functionality is limited to recognizing and organizing information that is already clearly defined within a structured format. Therefore, while Form Recognizer is indispensable for document automation workflows, it does not address the broader needs of understanding and analyzing natural language content.
The third choice, Azure Computer Vision, focuses on visual intelligence and analyzes images and video content. This service can detect objects, recognize faces, identify text within images, and even provide image tagging or categorization. Although highly sophisticated in the visual domain, Computer Vision does not have the capability to process natural language or extract textual insights. It cannot summarize text, identify sentiment, or extract named entities from documents or conversational text. Its domain is strictly visual analysis, making it ideal for applications such as security monitoring, quality inspection in manufacturing, and accessibility tools, but it is not suitable for understanding unstructured textual data.
The fourth choice, Azure Anomaly Detector, specializes in numeric data analysis. It monitors time-series datasets to identify unusual patterns, deviations, or trends that may indicate problems, such as operational anomalies, financial fraud, or sensor malfunctions. While this service is highly effective for detecting patterns in numeric or structured data, it cannot process unstructured text, perform sentiment analysis, or extract key phrases or entities from textual content. Its primary function is numeric anomaly detection rather than text analysis, meaning it is not suitable for tasks that require natural language understanding or insight generation from written documents.
Considering all four options, the correct selection is the service designed specifically for natural language processing and text analytics. Azure Cognitive Services Text Analytics provides organizations with the ability to summarize long documents, extract key phrases, recognize entities, and analyze sentiment efficiently. It automates tasks that would otherwise require extensive manual effort, improves accuracy and consistency in insight generation, and scales to handle enterprise-level datasets across multiple languages. Other services, including Form Recognizer, Computer Vision, and Anomaly Detector, focus on structured document extraction, visual analysis, and numeric anomaly detection, respectively. While these services are highly specialized and valuable in their own domains, they cannot provide actionable insights from unstructured text.
Therefore, for any application requiring analysis of natural language, extraction of key information, sentiment evaluation, or document summarization, Azure Cognitive Services Text Analytics is the most appropriate and effective choice. Its broad capabilities, scalability, and integration with other Azure services make it an essential tool for businesses and organizations that need to leverage the power of AI to understand and act upon unstructured textual data efficiently. By using Text Analytics, organizations can unlock the value hidden in their text data, streamline operations, and support informed decision-making across various business functions.
Question 190
Which Azure AI 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 provides a comprehensive suite of prebuilt artificial intelligence APIs designed to support a wide range of applications across multiple domains. Azure Cognitive Services is the platform that brings together these APIs, offering developers ready-to-use AI capabilities that cover vision, language, speech, and decision-making. These services are designed to simplify the integration of AI into applications, enabling rapid deployment without the need to build, train, or fine-tune models from scratch. By leveraging prebuilt APIs, organizations can implement sophisticated AI solutions efficiently and reliably, which accelerates innovation and reduces the barriers to entry for AI adoption.
Within the vision domain, Azure Cognitive Services provides APIs that enable applications to see and interpret visual content. The Vision APIs include capabilities for object detection, image classification, facial recognition, and text extraction from images and scanned documents. Developers can use these APIs to analyze images and videos, detect the presence of people or objects, recognize facial features, and read printed or handwritten text. This functionality is essential for a wide variety of real-world applications, such as security monitoring, inventory management, accessibility solutions, retail analytics, and healthcare imaging. The structured outputs from these APIs, including bounding boxes, confidence scores, and extracted text, can be easily integrated into automated workflows, dashboards, and enterprise applications, providing actionable insights from visual content quickly and accurately.
In the language domain, Azure Cognitive Services offers APIs for natural language processing that allow applications to understand and process textual content. These Language APIs provide functionality such as sentiment analysis, key phrase extraction, entity recognition, translation between multiple languages, and conversational AI through services like LUIS. These capabilities enable developers to create applications that can understand user intent, summarize documents, analyze social media content, or translate text across languages. Businesses can leverage these tools to improve customer engagement, automate support through chatbots or virtual assistants, and gain deeper insights from unstructured textual data. The APIs are designed to work with large datasets and multiple languages, making them suitable for enterprise-level applications with global reach.
Speech capabilities are also an integral part of Azure Cognitive Services. The Speech APIs provide speech recognition, text-to-speech conversion, and real-time translation, allowing applications to interact with users through voice. These services enable the creation of voice-enabled interfaces, automated transcription services, accessibility solutions for users with disabilities, and multilingual communication tools. By integrating speech capabilities, developers can enhance user experiences and create more natural, intuitive interactions between humans and machines. The combination of speech recognition and text-to-speech synthesis also allows for seamless implementation of conversational AI applications that rely on voice as an input and output modality.
In addition to vision, language, and speech, Azure Cognitive Services also provides decision-making capabilities. These Decision APIs include tools for anomaly detection, personalized recommendations, and predictive insights. For example, anomaly detection can be used to identify unusual patterns in time-series data such as IoT sensor readings, operational metrics, or financial transactions, alerting organizations to potential issues before they escalate. Recommendation engines enable personalized user experiences by suggesting relevant products, content, or services, while predictive analytics tools help businesses forecast trends, optimize processes, and make data-driven decisions. These decision-focused APIs allow organizations to incorporate intelligent decision-making directly into their applications without requiring in-depth AI expertise.
The second choice, Azure Machine Learning, is designed for building, training, and deploying custom machine learning models. While it provides a powerful environment for experimentation, model development, and deployment, it does not offer the same prebuilt AI APIs that Azure Cognitive Services does. Developers using Azure Machine Learning must create models from scratch, which requires a deeper understanding of machine learning techniques, data preprocessing, model selection, and evaluation. This approach is ideal for custom, highly specialized AI solutions but is not optimized for rapid implementation of ready-made AI functionality across multiple domains.
The third choice, Azure Data Factory, is a platform focused on orchestrating data workflows. It handles the extraction, transformation, and loading (ETL) of data from diverse sources, enabling organizations to integrate and prepare datasets for analytics or machine learning. While essential for managing and moving data efficiently, Azure Data Factory does not provide prebuilt AI APIs or the ability to directly implement vision, language, speech, or decision-making functionality within applications.
The fourth choice, Azure Synapse Analytics, is designed for data analytics and warehousing. It provides large-scale querying, reporting, and business intelligence capabilities, allowing organizations to analyze structured and semi-structured datasets efficiently. Although Synapse Analytics is valuable for data-driven decision-making, it does not provide AI services for vision, language, speech, or predictive modeling, making it unsuitable for rapid deployment of AI functionalities.
Considering all four options, the correct selection is the service specifically designed to provide prebuilt AI capabilities across multiple domains. Azure Cognitive Services allows developers to quickly integrate intelligent features into applications without building or training models from scratch. It provides enterprise-ready APIs for vision, language, speech, and decision-making, enabling organizations to implement AI functionality efficiently and at scale. Other services, including Azure Machine Learning, Azure Data Factory, and Azure Synapse Analytics, focus on custom model training, data orchestration, and analytics, respectively, and cannot provide the same ready-to-use AI APIs.
Therefore, for organizations seeking to implement artificial intelligence quickly, reliably, and across multiple domains, Azure Cognitive Services is the ideal choice. By leveraging prebuilt APIs, businesses can enhance applications with intelligent capabilities, gain actionable insights, and deliver advanced functionality to users without the complexities of developing models from the ground up. The versatility, scalability, and integration options offered by Azure Cognitive Services make it a critical tool for accelerating AI adoption and driving innovation in a wide range of industries.
Question 191
Which Azure AI service can automatically extract key-value pairs and tables from forms and receipts?
A) Azure Form Recognizer
B) Azure Cognitive Services Text Analytics
C) Azure Computer Vision
D) Azure Anomaly Detector
Answer: A) Azure Form Recognizer
Explanation:
The first choice automates the extraction of structured data from documents like forms, receipts, and invoices. Azure Form Recognizer identifies tables, key-value pairs, and fields with high accuracy, reducing manual data entry and saving significant time. It supports both printed and handwritten documents, allowing organizations to digitize documents efficiently. Prebuilt models handle common document types, while custom models allow training on specialized layouts, enabling processing of unique forms. Organizations often use it in finance, accounting, and business workflows to integrate extracted data into automated systems, improving operational efficiency.
The second choice focuses on analyzing unstructured text. Azure Cognitive Services Text Analytics extracts key phrases, identifies entities, and determines sentiment but is not designed to process forms or structured tables. Its strength lies in natural language understanding rather than document layout extraction.
The third choice provides visual intelligence capabilities. Azure Computer Vision can detect objects, text, and faces in images but is not optimized for extracting structured data from tables or key-value pairs. Post-processing is needed to convert raw text into structured formats.
The fourth choice monitors numeric time-series data for anomalies. Azure Anomaly Detector identifies unusual trends and patterns in numeric datasets but cannot process document layouts or extract structured form data. Its functionality is unrelated to document processing.
The correct selection is the service built specifically for automated document data extraction. Azure Form Recognizer enables organizations to digitize documents, reduce manual labor, and integrate structured data into workflows seamlessly. Other services specialize in text analytics, visual recognition, or numeric anomaly detection and do not provide structured data extraction from forms. Therefore, Azure Form Recognizer is the correct choice.
Question 192
Which Azure AI service converts spoken audio into written text?
A) Azure Speech-to-Text
B) Azure Text-to-Speech
C) Azure Translator Text API
D) Azure Form Recognizer
Answer: A) Azure Speech-to-Text
Explanation:
The first choice converts speech into written text. Azure Speech-to-Text supports real-time transcription and batch processing for recorded audio, making it suitable for meetings, call centers, dictation, and voice-driven applications. It handles multiple languages, accents, and noisy environments while maintaining high accuracy. Features like speaker identification, timestamps, and punctuation formatting allow structured and readable transcripts. Organizations use it to automate documentation, create searchable records, and integrate audio data into analytics workflows.
The second choice converts text into spoken audio. Azure Text-to-Speech synthesizes natural-sounding voice but does not transcribe audio. Its focus is on speech output rather than transcription.
The third choice translates written text between languages. Azure Translator Text API performs text translation but does not convert spoken audio into written text. Its functionality is limited to text-based translation.
The fourth choice extracts structured data from forms and receipts. Azure Form Recognizer identifies tables and key-value pairs but does not process audio or transcribe speech. Its domain is document processing.
The correct selection is the service explicitly designed to convert audio to text. Azure Speech-to-Text provides accurate transcription, multi-language support, and integration with workflows. Other services focus on text-to-speech, translation, or document extraction and cannot perform audio transcription. Therefore, Azure Speech-to-Text is the correct choice.
Question 193
Which Azure AI service enables real-time translation of spoken language?
A) Azure Speech Translation
B) Azure Speech-to-Text
C) Azure Text-to-Speech
D) Azure Cognitive Services Text Analytics
Answer: A) Azure Speech Translation
Explanation:
The first choice provides real-time spoken language translation. Azure Speech Translation combines speech recognition, translation, and speech synthesis to allow live multilingual communication. It is ideal for meetings, webinars, conferences, and customer support where participants speak different languages. The service preserves conversational flow, supports multiple accents, and provides translated audio or text instantly. Organizations use it to facilitate global collaboration, accessibility, and seamless communication.
The second choice converts spoken audio into written text. Azure Speech-to-Text transcribes speech but does not translate it. Its function is limited to speech recognition rather than translation.
The third choice converts text into spoken audio. Azure Text-to-Speech generates audio from text but does not process live audio or provide translation. Its domain is speech synthesis.
The fourth choice analyzes unstructured text. Azure Cognitive Services Text Analytics extracts key phrases, entities, and sentiment but cannot translate spoken language. Its functionality is focused on text analysis rather than real-time audio translation.
The correct selection is the service designed specifically for live spoken language translation. Azure Speech Translation enables real-time multilingual communication. Other services focus on transcription, text-to-speech, or text analysis and cannot perform live translation. Therefore, Azure Speech Translation is the correct choice.
Question 194
Which Azure AI service provides prebuilt APIs for vision, language, speech, and decision-making?
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 comprehensive set of prebuilt AI APIs across multiple domains. Vision APIs detect objects, recognize faces, classify images, and extract text. Language APIs provide sentiment analysis, entity recognition, translation, and conversational AI capabilities. Speech APIs include speech recognition, text-to-speech, and real-time translation. Decision APIs handle anomaly detection, recommendations, and predictive analytics. These prebuilt APIs allow developers to integrate AI into applications quickly without building models from scratch, enabling rapid deployment, scalability, and enterprise-ready solutions.
The second choice is a platform for building, training, and deploying custom machine learning models. Azure Machine Learning focuses on experimentation and model deployment but does not provide prebuilt APIs for immediate use in vision, language, speech, or decision-making.
The third choice orchestrates ETL workflows and data movement. Azure Data Factory handles data integration and transformation but does not provide AI APIs for vision, language, speech, or decision-making.
The fourth choice is a data analytics platform for querying and reporting large datasets. Azure Synapse Analytics enables data warehousing and analytics but does not offer prebuilt AI services.
The correct selection is the service specifically built to provide ready-to-use AI capabilities across multiple domains. Azure Cognitive Services allows developers to implement AI functionality in vision, language, speech, and decision-making quickly. Other services focus on model training, data integration, or analytics and cannot provide prebuilt AI APIs. Therefore, Azure Cognitive Services is the correct choice.
Question 195
Which Azure AI service can summarize text, extract key phrases, and detect sentiment in unstructured documents?
A) Azure Cognitive Services Text Analytics
B) Azure Form Recognizer
C) Azure Computer Vision
D) Azure Anomaly Detector
Answer: A) Azure Cognitive Services Text Analytics
Explanation:
The first choice provides natural language processing for unstructured text. Azure Cognitive Services Text Analytics can summarize long documents, extract key phrases, identify entities such as people, organizations, and locations, and determine sentiment as positive, negative, or neutral. It is widely used for customer feedback analysis, surveys, social media monitoring, and research document summarization. Automation reduces manual work, accelerates insights, and provides actionable information. Multi-language support and scalability allow it to handle enterprise-level datasets efficiently.
The second choice extracts structured data from forms and receipts. Azure Form Recognizer identifies tables, key-value pairs, and fields but does not provide text summarization, sentiment detection, or key phrase extraction.
The third choice analyzes visual content. Azure Computer Vision detects objects, faces, and text in images but cannot summarize documents or analyze sentiment. Its domain is visual intelligence rather than natural language processing.
The fourth choice detects anomalies in numeric datasets. Azure Anomaly Detector monitors unusual numeric patterns but cannot process unstructured text for summarization or sentiment. Its functionality is unrelated to text analytics.
The correct selection is the service specifically designed for text analysis. Azure Cognitive Services Text Analytics provides summarization, key phrase extraction, and sentiment detection efficiently. Other services focus on document extraction, visual analysis, or numeric anomaly detection and cannot perform text analytics. Therefore, Text Analytics is the correct choice.