Microsoft AI-900 Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 1 Q1-20

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Question 1:

You are building a retail analytics system that uses Azure services to classify product images and identify whether the items follow store planogram rules. Which Azure service should you use when you need a fully customizable image classification model that can be trained using your own dataset?

Answer:

A) Custom Vision
B) Computer Vision
C) Azure Bot Service
D) Translator Service

Explanation:

Custom Vision is the correct choice because it allows you to create and train image classification or object detection models using your own labeled images, making it suitable for scenarios where off-the-shelf models do not meet retail-specific requirements. Computer Vision is a general-purpose service that can extract tags, detect objects, and identify content in images, but it does not allow you to fully train a model using your own specialized dataset, which retail planogram analysis demands. Azure Bot Service is irrelevant because it is used to build conversational agents rather than image models. Translator Service performs text translation and does not provide any capabilities related to visual content or classification. Custom Vision offers features such as quick training iterations, performance metrics, exportable models, and domain-specific training environments, making it ideal for retail, industrial, or organizational settings where a unique dataset defines the classification criteria. The ability to continuously add images and retrain over time improves accuracy as new product variations or packaging changes occur. Image classification tasks in planogram compliance require granular understanding of product shape, branding, size, and positioning. A custom-trained model is essential because generic image recognition models often fail to distinguish between very similar products, especially when packaging variations exist. The system must detect not only the product category but the specific SKU, which is only feasible with a custom dataset. Custom Vision also supports sufficient labeling tools and iterative improvements, enabling teams to refine accuracy gradually. Therefore, Custom Vision is the only option that provides the flexibility, training control, and dataset-specific optimization required for advanced retail image classification.

Question 2:

A company wants to analyze thousands of customer reviews to determine common opinions and emotional tone. Which Azure AI service should you use to detect sentiments as positive, neutral, or negative?

Answer:

A) Text Analytics
B) Form Recognizer
C) Language Understanding (LUIS)
D) Speaker Recognition

Explanation:

Text Analytics is the correct choice because sentiment analysis is one of its core capabilities, and it efficiently extracts emotional tone from large volumes of text such as reviews, comments, or social media posts. It also supports key phrase extraction, language detection, and entity recognition, which makes it useful for deeper understanding of customer feedback. Form Recognizer is designed to extract structured data from forms, invoices, and receipts, making it unsuitable for free-form sentiment evaluation. LUIS focuses on intent detection for conversational interfaces but does not perform classical sentiment scoring on large bodies of text. Speaker Recognition identifies who is speaking in an audio sample rather than analyzing emotional tone, so it is irrelevant for text-based analysis. Text Analytics applies transformer-based models that are trained across large multilingual corpora, enabling high-quality sentiment classification even when reviews contain informal or mixed language. Sentiment analysis is essential for organizations tracking customer satisfaction, identifying emerging issues, or analyzing brand perception. By summarizing emotional tone at scale, Text Analytics supports targeted improvements in products, services, and communication strategies. Additionally, sentiment scoring can be integrated with dashboards or data pipelines to support ongoing monitoring. The service also includes confidence scoring that helps evaluate the reliability of predictions, which is useful when the reviews contain sarcasm, ambiguous phrasing, or nonstandard grammar. These capabilities make Text Analytics the best choice.

Question 3:

You need to convert audio from a live customer support call into text in real time. Which Azure service provides this capability?

Answer

A) Speech to Text
B) Text Analytics
C) QnA Maker
D) Custom Vision

Explanation:

Speech to Text is the correct answer because it converts spoken audio into text using advanced acoustic and language models. It supports real-time streaming, batch transcription, and custom speech adaptation, making it ideal for call centers, voice assistants, and transcription workflows. Text Analytics works only with written text and cannot process audio directly. QnA Maker creates knowledge bases for question–answer systems but does not transcribe speech. Custom Vision handles image classification and has no functionality related to audio or speech. Real-time speech transcription is essential for customer support environments because it enables automated note generation, compliance monitoring, analytics, and integration with downstream natural language processing. The Speech service includes features such as noise reduction, punctuation prediction, and diarization, making it effective even in noisy call settings. Its custom speech feature allows adaptation to industry-specific terminology, ensuring more accurate recognition. These capabilities make Speech to Text the appropriate choice for converting audio into text streams during live interactions.

Speech to Text is the correct option because it is specifically designed to transform spoken language into written text during live interactions. When dealing with customer support calls, agents often need accurate, real-time transcription for documentation, analytics, and automation. The Speech to Text capability within Azure’s Speech service enables this by using sophisticated acoustic and language models that continuously evolve through machine learning. These models allow the system to handle different accents, speaking speeds, and call environments with impressive accuracy. Since customer conversations can be fast-paced and filled with domain-specific terminology, the service also supports custom speech adaptation so organizations can train the system to understand their specialized vocabulary, product names, or technical jargon.

Real-time streaming is one of the standout advantages of Speech to Text. As the call is happening, the tool can output text instantly, allowing teams to monitor conversations for compliance or sentiment. This helps quality assurance teams identify issues early, guide agents with suggested responses, or automate follow-up tasks without waiting for post-call processing. Features like automatic punctuation, noise reduction, and speaker diarization make the transcriptions easier to read and analyze because the system can distinguish between speakers and minimize background sounds, which are common in busy call environments.

In contrast, Text Analytics is focused on extracting insights from text that already exists. It cannot listen to audio or convert speech into written form, so it would only become useful after transcription has been completed. QnA Maker provides a structured question–answer experience by building knowledge bases, but it does not handle live audio streams or transcription tasks. Custom Vision is fully unrelated to audio; it works with images to classify or detect visual objects.

Considering all of this, Speech to Text is clearly the appropriate choice for converting live customer support audio into readable text, especially when accuracy, speed, and adaptability matter.

Question 4:

You want to extract structured data such as invoice numbers, totals, and vendor names from a variety of document formats. Which Azure service is designed for this task?

Answer:

A) Form Recognizer
B) Translator
C) Anomaly Detector
D) Custom Vision

Explanation:

Form Recognizer is the correct choice because it extracts key-value pairs, tables, and text from both structured and semi-structured documents such as invoices, receipts, forms, and financial statements. It uses layout understanding combined with deep learning models to interpret document structures, making it highly suited for business automation. Translator is a language translation service and cannot extract structured data from documents. Anomaly Detector identifies unusual patterns in metric streams but does not interpret documents. Custom Vision targets image classification and object detection rather than form extraction. Form Recognizer supports prebuilt models, trained models, and custom models that allow organizations to adapt extraction to their own document designs. This is especially important when dealing with diverse vendor invoice templates that may vary in layout. The service can return confidence scores for extracted fields, supports JSON output for integration into workflows, and reduces the manual effort associated with data entry. Additionally, it can process handwritten fields and low-quality scans, making it highly robust for real business scenarios. These capabilities make Form Recognizer the best option for structured document extraction.

Question 5:

You want to build a chatbot that can answer questions from a knowledge base extracted from frequently asked questions. Which Azure service is ideal for creating this type of conversational agent?

Answer:

A) Azure Bot Service
B) Computer Vision
C) Custom Vision
D) Anomaly Detector

Explanation:

Azure Bot Service is the correct answer because it provides the framework for designing, deploying, and managing conversational bots that interact through text or voice. It integrates easily with knowledge bases and language services, enabling rich conversational experiences. Computer Vision is focused on image analysis and cannot support conversational workflows. Custom Vision trains image models but cannot respond conversationally. Anomaly Detector identifies anomalies in numeric datasets and has no role in building chatbots. Azure Bot Service also integrates with Azure AI Language services, enabling intelligent responses, context tracking, dialogue flows, and multi-channel deployment across platforms like Teams, web chat, and mobile apps. It includes SDKs and development tools for customizing bot behavior and supports integration with APIs, authentication, and event triggers. The ability to connect bots with natural language understanding makes the service particularly powerful. Azure Bot Service remains the most appropriate option for FAQ automation and conversational solutions.

Question 6:

A company wants to automatically detect unusual spikes in website traffic to identify possible security threats. Which Azure service is designed to determine when data points significantly deviate from expected patterns?

Answer:

A) Anomaly Detector
B) Custom Vision
C) Azure Bot Service
D) Text Analytics

Explanation:

Anomaly Detector is the correct service because it is specifically built to analyze time-series data and identify when values fall outside normal ranges, making it useful for detecting sudden traffic increases, usage dips, or irregular operational behavior. Custom Vision deals with image classification and cannot process numeric data patterns. Azure Bot Service helps create conversational bots, which have nothing to do with detecting irregularities in metric patterns. Text Analytics examines written text, so it also cannot interpret numerical traffic data. Anomaly Detector applies advanced statistical and machine-learning models to continually learn normal behaviors over time. When website traffic spikes, the system evaluates the deviation using AI-based forecasting, which is essential for identifying potential cyberattacks, DDOS attempts, or system malfunctions. Its ability to handle data with seasonal trends, noise, and irregular intervals makes it especially valuable in operational monitoring. It also assigns anomaly scores, helping organizations act on issues with greater confidence and reducing false positives. These capabilities make it ideal for monitoring websites, IoT sensor feeds, manufacturing output, or financial transactions. By integrating with dashboards or alerting systems, it becomes a critical part of a company’s risk-mitigation strategy. For these reasons, Anomaly Detector is the most appropriate option for this scenario.

Anomaly Detector is the correct answer because it is tailored to discover irregular patterns in time-series data, making it highly effective for monitoring website traffic and identifying unusual spikes that could signal security concerns. Websites naturally experience fluctuations in traffic, but distinguishing legitimate growth from suspicious activity requires a system that can adapt to changing patterns over time. Azure’s Anomaly Detector uses advanced statistical techniques along with machine learning models to learn the normal rhythm of data. Once it understands the typical behavior, it can flag values that stray too far from what is expected. This ability is crucial when dealing with sudden surges that may indicate malicious attempts such as distributed denial-of-service attacks or other cyber intrusions.

A major advantage of Anomaly Detector is that it continues to refine its understanding of the data as more information arrives. This dynamic learning helps it adjust to natural seasonal trends, traffic cycles, marketing events, or other predictable shifts without incorrectly labeling them as problems. It can also interpret noisy data and handle irregular intervals, which is valuable when the data collection process is not perfectly consistent. When the service identifies a deviation, it generates an anomaly score that helps teams gauge the severity and urgency of the issue. This scoring mechanism supports quick decision-making and reduces wasted effort on false alarms.

Comparatively, Custom Vision is unrelated to this scenario because it focuses on training image classification models and does not analyze numerical metrics. Azure Bot Service is intended for building conversational agents, so it plays no role in detecting metrics that drift away from normal behavior. Text Analytics processes written content to extract insights, so it cannot interpret time-series values or detect traffic spikes.

By integrating Anomaly Detector with monitoring dashboards, notification systems, or automated workflows, organizations can strengthen their defenses and react faster to incidents. It becomes a vital tool not only for website security but also for monitoring IoT device data, financial activities, or operational performance across various industries. For these reasons, Anomaly Detector stands out as the most suitable choice for detecting unusual spikes in website traffic.

Question 7:

A healthcare company wants to analyze handwritten doctor notes and convert them into searchable digital text. Which Azure service can handle both printed and handwritten text extraction?

Answer:

A) Computer Vision OCR
B) Custom Vision
C) Azure Bot Service
D) Language Understanding

Explanation:

Computer Vision OCR is the correct answer because it extracts printed and handwritten text from images and scanned documents, transforming them into machine-readable and searchable text. Custom Vision cannot extract text because its purpose is classifying or detecting objects in images. Azure Bot Service builds chatbots but does not read handwriting. Language Understanding focuses on intent recognition in text rather than extracting the text itself. The OCR capability of Computer Vision is essential for medical workflows, especially where doctor notes may be written rapidly, in varying handwriting styles, or on scanned sheets with imperfect quality. The OCR engine processes character shapes, spacing, and noise to convert handwriting into digital strings. This enables healthcare systems to index records, search patient information quickly, and reduce manual transcription errors. OCR can also support workflows involving insurance forms, prescriptions, and clinical notes. By integrating the extracted text into downstream analytics or patient-record systems, healthcare teams gain efficiency and improve patient safety. This capability makes Computer Vision OCR the correct and practical service for handwritten medical document processing.

Question 8:

A global e-commerce brand needs to offer instant language translation for product descriptions across multiple languages. Which Azure service is built specifically for high-quality automated translation?

Answer:

A) Translator Service
B) Custom Vision
C) Anomaly Detector
D) Form Recognizer

Explanation:

Translator Service is the correct answer because it provides neural machine translation for dozens of languages, enabling businesses to offer multilingual product descriptions, chat experiences, and international support. Custom Vision deals with image classification, not language translation. Anomaly Detector focuses on identifying abnormal numeric patterns. Form Recognizer extracts structured data but does not translate content. Translator uses deep neural network models designed to preserve context, grammar, and subtle meanings across languages. This ensures product descriptions maintain accuracy and clarity even when translated into linguistically distant languages. The service also supports custom translation models, allowing companies to adapt translations for brand-specific terminology or industry jargon. For e-commerce brands, preserving product meaning is crucial because mistranslations can lead to customer confusion or compliance issues. The Translator API can integrate seamlessly into content management systems, enabling automated translation workflows that scale globally. Its ability to perform translation at high speed and with consistent quality across multiple languages makes it ideal for large enterprises. These capabilities make Translator Service the best choice for multilingual product content automation.

Question 9:

A software company needs to determine the intent behind user statements in a chatbot conversation, such as identifying whether users want to reset a password or check account status. Which Azure service is designed to classify user intent?

Answer:

A) Language Understanding
B) Computer Vision
C) Form Recognizer
D) Anomaly Detector

Explanation:

Language Understanding is the correct option because it identifies intents and extracts entities from natural language input, which is essential for building intelligent chatbots. Computer Vision processes images and cannot analyze conversational intent. Form Recognizer deals with document extraction and has no role in classifying intents. Anomaly Detector does not interpret language at all. Language Understanding enables chatbots to recognize user goals by interpreting phrases that may be phrased inconsistently or contain informal language. It also extracts entities such as dates, amounts, or product names that help the bot take the right action. This service allows developers to train models on domain-specific examples, improving accuracy in real operational environments. The ability to handle synonyms, incomplete sentences, and varied user phrasing enables richer and more intuitive conversational experiences. It also integrates seamlessly with Azure Bot Service, making it easier to build bots that respond intelligently rather than relying on keyword matching. For these reasons, Language Understanding is the best solution for intent-based chatbot tasks.

Question 10:

A media company needs to search within video content for spoken keywords, faces, and topics appearing throughout recordings. Which Azure service provides integrated video indexing capabilities?

Answer:

A) Video Indexer
B) Custom Vision
C) Text Analytics
D) QnA Maker

Explanation:

Video Indexer is the correct answer because it processes video content and extracts insights such as spoken words, topics, faces, emotions, and scene segmentation. Custom Vision is designed for static image classification, not full video analysis. Text Analytics can analyze text but cannot process video streams. QnA Maker builds question-answer knowledge bases and does not analyze multimedia content. Video Indexer is particularly useful for media companies because it can handle long videos, identify speaker segments, tag content with topics, and extract transcripts. It can detect brands, identify celebrities, and generate searchable metadata that makes content more discoverable. Video content is difficult to analyze manually, but Video Indexer automates the process using AI models for speech recognition, facial recognition, translation, and topic inference. This enables content creators to categorize, edit, and repurpose footage more efficiently. Integrations with content management tools allow companies to streamline their production workflows. Because of these comprehensive video-analysis capabilities, Video Indexer is the service best suited for this scenario.

A media company that wants to search within video content for spoken keywords, faces, and topics appearing throughout recordings should use Video Indexer. Video Indexer is an Azure service designed specifically to extract rich insights from video and audio files, making it the ideal tool for media organizations, broadcasters, and content creators. It can process video content to identify spoken words through automatic speech recognition, extract topics from conversations, detect faces and emotions, and segment scenes, providing a comprehensive overview of the content without requiring manual review. Custom Vision, by contrast, is intended for image classification and object detection in static images, and it cannot analyze dynamic video streams or audio. Text Analytics focuses on written text, extracting entities, sentiment, and key phrases, but it does not have the capability to process video or audio files. QnA Maker is used for building knowledge bases that respond to user queries and does not provide any functionality for analyzing multimedia content.

Video Indexer provides several advantages for media companies. It can generate searchable transcripts of video and audio content, detect multiple speakers, and tag videos with relevant topics and keywords, which greatly enhances content discoverability. The service can also recognize celebrities, detect brands, and identify emotions expressed in the video, enabling more detailed analysis of audience engagement and content performance. Video Indexer supports multiple languages and offers translation services, making it suitable for global content distribution. By automatically creating metadata from videos, companies can streamline workflows such as editing, categorization, and archiving, reducing the time and effort required for manual review. Its integration with Azure’s cloud infrastructure allows organizations to scale processing for large volumes of content while maintaining high accuracy.

Overall, Video Indexer offers an end-to-end solution for analyzing and extracting insights from video content, making it the most suitable service for companies that need to search for spoken keywords, faces, topics, and other important elements in recordings. It combines speech recognition, facial detection, topic inference, and emotion analysis in a single service, enabling efficient content management and decision-making based on rich media insights.

Question 11:

A financial company wants to detect potential fraud by monitoring transaction patterns over time. They need a service that automatically identifies sudden deviations or unexpected financial behavior. Which Azure AI service is best for this requirement?

Answer:

A) Anomaly Detector
B) Text Analytics
C) Language Understanding
D) Custom Vision

Explanation:

Anomaly Detector is the correct choice because it is designed to analyze numerical time-series data and find patterns that differ from normal expected values. Financial transactions typically follow predictable spending behavior, and deviations such as sudden spending spikes, unusual withdrawals, or inconsistent transaction intervals can be automatically detected using machine-learning anomaly detection models. Text Analytics cannot process numerical transaction streams because it is limited to processing written text. Language Understanding is used to identify intent in text interactions and does not work with numeric patterns or financial time-series datasets. Custom Vision focuses on image-based classification and cannot be used to analyze financial data. Anomaly Detector applies dynamic learning, meaning the model continues to update its understanding of what constitutes normal behavior, which is especially important for financial environments where user activity evolves over time. It handles seasonality, noise, and irregular intervals, making it ideal for scenarios such as hourly transaction spikes or unusual day-to-day patterns. Fraud detection requires precise anomaly scoring, confidence thresholds, and compatibility with dashboards and alerting pipelines, all of which the Anomaly Detector service supports. For financial fraud detection, the ability to detect anomalies in real time helps organizations react quickly and prevent damage. This makes Anomaly Detector the most appropriate service for analyzing deviant transaction behavior.

Question 12:

A manufacturing plant wants to use AI to detect missing screws, misaligned parts, or damaged components on a production line in real time. Which Azure AI service should they use to train their own object detection model?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Azure Bot Service
D) Translator Service

Explanation:

Custom Vision is the correct answer because it supports custom object detection models that can identify multiple items within an image and locate them with bounding boxes. Manufacturing quality control almost always requires a custom dataset because each product has unique structural requirements. Computer Vision OCR focuses only on text extraction and cannot detect objects or defects. Azure Bot Service enables conversational experiences rather than visual detection. Translator Service converts text between languages and does not analyze images. Custom Vision’s object detection capabilities enable detection of small components like screws, rivets, or connectors, which are essential in assembly-line QA workflows. It allows training with labeled images, making it adaptable to unique manufacturing environments. The service supports multiple iterations, giving teams flexibility to continuously improve accuracy. Manufacturing environments often include variable lighting, fast-moving equipment, and different camera angles, all of which can be accommodated by increasing dataset size and diversity. Integrating Custom Vision with edge devices allows real-time defect detection directly on production machinery. This reduces waste, improves safety, and streamlines quality assurance. Therefore, Custom Vision is the correct and most capable service for this scenario.

A manufacturing plant looking to detect missing screws, misaligned parts, or damaged components on a production line in real time should use Custom Vision. Custom Vision is an Azure AI service specifically designed for building and training custom object detection models that can identify multiple items within an image and accurately locate them using bounding boxes. In manufacturing, quality control often requires detecting specific defects or anomalies that are unique to a particular product or assembly line. Prebuilt models usually cannot handle these unique requirements, making a customizable solution like Custom Vision essential. Computer Vision OCR is designed solely for extracting text from images and does not provide the ability to detect physical objects or defects. Azure Bot Service focuses on conversational AI, creating chatbots and virtual assistants, which has no relevance to visual inspection or real-time object detection. Translator Service is only capable of converting text from one language to another and does not analyze images or identify objects.

Custom Vision allows manufacturing teams to train models using labeled images of their specific products, making the system adaptable to unique components such as screws, rivets, connectors, or other small parts that need to be monitored for defects. The platform supports iterative model training, allowing teams to continuously refine accuracy as new variations or anomalies appear on the production line. Real-world manufacturing environments can present challenges such as varying lighting conditions, fast-moving machinery, and multiple camera angles. Custom Vision addresses these issues by enabling the inclusion of diverse datasets, which improves model robustness. Additionally, the service can be integrated with edge devices, allowing AI models to operate directly on production machinery, facilitating real-time detection without latency.

The implementation of Custom Vision in manufacturing significantly reduces waste by catching defective or missing components before products move further along the assembly line. It improves worker safety by automating inspections in hazardous or fast-paced environments and enhances overall production efficiency by ensuring that only high-quality products continue through the process. By combining AI-powered detection with real-time monitoring, manufacturers gain actionable insights and can prevent costly errors. The flexibility, adaptability, and real-time capabilities of Custom Vision make it the most appropriate choice for industrial quality assurance, enabling precise and efficient detection of defects, misalignments, or missing components throughout production workflows.

Question 13:

A global company wants to identify languages automatically from user-generated comments posted online. They do not need translation, just identification. Which Azure service should be used?

Answer:

A) Text Analytics
B) Form Recognizer
C) Custom Vision
D) Video Indexer

Explanation:

Text Analytics is the correct choice because it includes language detection as part of its core capabilities. It can identify the dominant language in a given piece of text, regardless of length or structure. Form Recognizer focuses on structured data extraction from documents and cannot determine language intent from raw text. Custom Vision is intended for image classification and cannot analyze text. Video Indexer focuses on extracting insights from videos such as spoken keywords and faces but is not used for pure language detection. Many organizations need language detection for routing content to appropriate translation pipelines, categorizing multilingual user feedback, or filtering content for regional compliance. The Text Analytics service uses machine-learning models that examine vocabulary, grammar, and statistical patterns to determine the language with high accuracy. It works well even when text contains slang, informal spelling, or mixed languages. The service also provides confidence scores so organizations can flag uncertain results. These capabilities make Text Analytics the most suitable choice for automated language identification.

Question 14:

A logistics company wants to build a virtual assistant that can understand questions about shipment status, delivery estimates, or package tracking. Which Azure service is responsible for intent recognition and routing user queries correctly?

Answer:

A) Language Understanding
B) Translator Service
C) Form Recognizer
D) Speech Recognition

Explanation:

Language Understanding is the correct option because it enables AI models to interpret user intent behind natural language input. For logistics applications, users may ask questions in many different ways, and the service identifies whether they want shipment tracking, delivery updates, or problem reporting. Translator Service only translates text and does not classify intent. Form Recognizer extracts structured data from documents and has no conversational role. Speech Recognition transcribes spoken text but does not interpret the user’s goal. The key to a successful virtual assistant is its ability to extract meaning, categorize requests, and direct them to the appropriate workflow. Language Understanding allows developers to define intents like track shipment, check delivery time, or change delivery address and train the model with example utterances. It also extracts entities such as tracking numbers, dates, or product names, which enables automated responses. When integrated with a chatbot, the system can deliver real-time logistical insights. These capabilities make Language Understanding the correct service for intelligent intent recognition in logistics scenarios.

Question 15:

A research team wants to automatically summarize long technical documents into concise paragraphs while preserving key points. Which Azure AI service includes text summarization capabilities?

Answer:

A) Azure AI Language
B) Computer Vision
C) Custom Vision
D) Anomaly Detector

Explanation:

Azure AI Language is the correct answer because it includes a text summarization feature that condenses long articles, manuals, or reports into shorter and more digestible summaries. Computer Vision focuses on image-based analysis, not long text summarization. Custom Vision deals with images as well and cannot summarize text. Anomaly Detector analyzes numerical trends and detects deviations, so it has no language summarization capabilities. Azure AI Language uses transformer-based models that analyze the context, key ideas, and relationships across paragraphs, producing extractive or abstractive summaries depending on the use case. Researchers benefit from automated summarization because it reduces the time required to review technical papers, data sheets, compliance documents, or regulatory content. The service ensures summaries remain coherent and preserve the original meaning. It can handle domain-specific terminology, making it suitable for scientific and engineering applications. The ability to scale summarization across large document sets improves productivity and supports faster decision-making. Therefore, Azure AI Language is the correct service for summarizing long technical documents.

A research team that needs to automatically summarize long technical documents into concise, meaningful paragraphs should use Azure AI Language. Azure AI Language is an Azure service specifically designed for advanced natural language processing tasks, including text summarization, entity recognition, sentiment analysis, translation, and question answering. Among its capabilities, text summarization allows it to condense lengthy articles, technical manuals, research papers, or regulatory documents into shorter, digestible summaries while retaining the key points and overall context. Computer Vision, in contrast, focuses entirely on image analysis and cannot process text in a way that produces coherent summaries. Custom Vision is similar in that it is geared toward object detection and image classification, and therefore has no functionality related to analyzing textual content. Anomaly Detector is specialized in examining numerical data trends and detecting deviations from expected patterns, so it is unsuitable for summarization or processing of written content.

Azure AI Language leverages transformer-based models capable of understanding the context, relationships, and main ideas across multiple paragraphs. This enables the generation of both extractive summaries, which pull the most important sentences directly from the original text, and abstractive summaries, which rephrase and condense information while maintaining the original meaning. For researchers and technical teams, this capability is particularly valuable because it saves substantial time that would otherwise be spent manually reviewing lengthy documents. The service is capable of handling domain-specific terminology, making it suitable for scientific papers, engineering reports, medical studies, or compliance documentation. By automating the summarization process, teams can process large volumes of text more efficiently, prioritize essential information, and make informed decisions faster.

Moreover, Azure AI Language can be integrated into workflows to support collaborative research, knowledge management, or data analysis projects. It can process documents at scale, ensuring that even extensive technical libraries or regulatory filings are summarized consistently and accurately. By preserving coherence, structure, and context, the service enhances productivity and ensures critical information is not lost during summarization. Because of these capabilities, Azure AI Language is the most appropriate choice for research teams seeking to condense complex technical documents into concise, high-quality summaries, enabling more efficient review and knowledge extraction across large datasets.

Question 16:

A company wants to create an application that can convert written text into natural-sounding spoken audio for accessibility purposes. Which Azure service is required?

Answer:

A) Text to Speech
B) Computer Vision
C) Form Recognizer
D) QnA Maker

Explanation:

Text to Speech is the correct answer because it converts written text into realistic-sounding voice output. It supports various voices, languages, and speaking styles, allowing organizations to build accessible applications for visually impaired users, automated announcements, or reading tools. Computer Vision analyzes images and cannot produce audio. Form Recognizer extracts data from documents but does not generate speech. QnA Maker is used to build question–answer knowledge bases. Text to Speech leverages neural voice models that mimic natural rhythm, intonation, and pacing to enhance user experience. Developers can adjust speech speed, pitch, and pronunciation, making it suitable for interactive systems or assistive technology. This flexibility ensures consistent audio output across devices and platforms. For accessibility solutions, voice clarity, expression, and language support are essential, all of which Text to Speech provides. Thus, it is the correct service for converting written content into spoken audio.

Question 17:

A university needs a way to automatically categorize thousands of incoming support emails into groups such as admissions, financial aid, housing, or technical support. Which service can classify text into predefined categories?

Answer:

A) Azure AI Language Classification
B) Custom Vision
C) Anomaly Detector
D) Video Indexer

Explanation:

Azure AI Language Classification is the correct answer because it allows developers to train models that categorize text into predefined labels, making it perfect for sorting large volumes of emails. Custom Vision handles image-based tasks and cannot classify text. Anomaly Detector works with numeric data trends rather than text classification. Video Indexer is meant for analyzing multimedia content, not written messages. Text classification models learn from example emails, subject lines, and message content, allowing the system to categorize new messages accurately. This helps the university automate routing, reduce manual sorting, and speed up response times. The service recognizes vocabulary patterns, intent indicators, and context clues within the emails. It is adaptable to new categories and scalable across large student populations. For these reasons, Azure AI Language Classification is the best choice for automated email categorization.

Question 18:

A transportation company wants to use AI to detect faces of authorized drivers when they enter company vehicles. Which Azure service provides prebuilt facial detection and recognition features?

Answer:

A) Face Service
B) Text Analytics
C) Translator
D) Speech to Text

Explanation:

Face Service is the correct answer because it provides facial detection, recognition, verification, face attributes, and face grouping features. Text Analytics works only with written text and cannot detect faces. Translator converts text across languages but has no visual-processing functions. Speech to Text handles audio and cannot identify faces. Face Service analyzes facial geometry, detects facial landmarks, identifies whether a face belongs to a known person, and can compare faces to stored profiles. Transportation companies can use this service to enhance security by ensuring only authorized drivers operate vehicles. It supports integration with access control systems and real-time monitoring dashboards. By identifying individuals accurately, organizations reduce risk and increase accountability. Thus, Face Service is the correct choice for face-based authorization.

A transportation company that wants to use AI to detect faces of authorized drivers when they enter company vehicles should use Face Service. Face Service is an Azure AI service specifically designed to provide prebuilt facial detection and recognition capabilities. It offers features such as face detection, facial landmark identification, face verification, recognition of known individuals, and face grouping. This service analyzes facial geometry to determine the position and relationship of facial features, which allows it to identify whether a detected face belongs to a registered person in the system. Text Analytics, by contrast, is designed to analyze written text for sentiment, key phrases, entities, and language understanding, and it has no capability to process images or identify faces. Translator is built for converting text between languages and cannot perform any visual processing. Speech to Text converts spoken audio into text and does not provide functionality for detecting or recognizing faces.

Face Service enables transportation companies to implement security measures by verifying the identities of drivers before they operate company vehicles. It can be integrated with access control systems to ensure that only authorized personnel gain entry, helping to prevent unauthorized use and enhancing operational safety. The service also supports real-time monitoring through dashboards, which allows fleet managers to track vehicle access and maintain accountability. By accurately identifying individuals, the service helps reduce risks associated with vehicle misuse or theft and can even support compliance with safety and regulatory standards. Additionally, Face Service can detect multiple faces within a single frame, recognize changes in facial attributes such as age or expression, and group faces to streamline management of driver profiles.

The flexibility and scalability of Face Service make it suitable for deployment across large fleets, with the ability to process images captured from vehicle cameras or entry points efficiently. This ensures that recognition occurs in real time, minimizing delays and supporting smooth operations. The integration of AI-powered face recognition enhances both security and operational efficiency, providing a reliable method for verifying driver identity without manual intervention. Overall, Face Service is the ideal choice for transportation companies that need robust, prebuilt facial detection and recognition capabilities, allowing them to monitor and control access to vehicles while improving safety, accountability, and compliance.

Question 19:

An online retailer wants to use AI to detect the emotions of customers in product review videos. They need a service that can analyze facial expressions and audio together. Which Azure service offers this capability?

Answer:

A) Video Indexer
B) Text Analytics
C) Form Recognizer
D) Custom Vision

Explanation:

Video Indexer is the correct answer because it provides emotion detection using both visual and auditory signals within videos. Text Analytics cannot analyze video files or facial expressions. Form Recognizer extracts structured data from documents, not multimedia. Custom Vision focuses on static images and does not process video sequences or audio input. Video Indexer evaluates body language, tone of voice, and facial features to identify emotions such as happiness, anger, or frustration. Retailers can use this to enrich customer-experience analytics by understanding emotional responses to products or services. By combining speech recognition, face detection, and sentiment analysis, Video Indexer provides a comprehensive set of insights not available from any single-modality service. This makes Video Indexer the correct choice for analyzing emotional content in review videos.

Question 20:

A call center wants to analyze customer conversations and automatically extract key topics, important entities, and overall sentiment from audio recordings. Which Azure service can perform spoken language transcription and advanced text analytics together?

Answer:

A) Azure AI Speech with Text Analytics
B) Custom Vision
C) Anomaly Detector
D) QnA Maker

Explanation:

Azure AI Speech with Text Analytics is the correct answer because it allows audio to be transcribed into text using Speech to Text, after which Text Analytics performs sentiment analysis, key phrase extraction, and entity recognition. Custom Vision handles visual tasks only and cannot process audio or text sentiment. Anomaly Detector examines numerical patterns rather than spoken language. QnA Maker generates question–answer responses and cannot analyze sentiment or conversation topics. The combination of transcription and language analysis provides powerful insights for call-center performance, customer-service improvement, and quality monitoring. Organizations gain clarity on common customer concerns, emotional responses, and trending issues. This enables more effective training, operational improvements, and targeted support tactics. For these reasons, Azure AI Speech paired with Text Analytics offers the best-in-class solution for comprehensive call center conversation analysis.