Microsoft AI-900 Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 4 Q61-80

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Question 61:

A logistics company wants to automatically extract text from shipping labels and track packages in real time. Which Azure AI service should they use?

Answer:

A) Computer Vision OCR
B) Custom Vision
C) Text Analytics
D) Form Recognizer

Explanation:

Computer Vision OCR is the correct answer because it can accurately extract text from images, including shipping labels, barcodes, and handwritten notes. In logistics, packages are often labeled with printed or handwritten addresses, tracking numbers, and instructions. Manually processing this information is time-consuming, error-prone, and inefficient. OCR converts this text into a machine-readable format, allowing automated systems to track packages in real time.

Custom Vision (Option B) is for object detection and classification, not text extraction. Text Analytics (Option C) analyzes digital text but cannot process image-based text. Form Recognizer (Option D) is best suited for structured forms, such as invoices or receipts, rather than freeform shipping labels.

Computer Vision OCR supports a variety of text styles and layouts, including printed and handwritten text. Once extracted, the text can be integrated into package tracking systems, enabling automated notifications, route optimization, and workflow automation. The AI can also handle multiple languages and diverse label formats, which is crucial for global logistics operations.

By leveraging OCR, companies can reduce errors associated with manual data entry, improve delivery accuracy, and enhance operational efficiency. Integration with Azure IoT or Power Automate allows packages to be tracked from warehouse to delivery, with automated updates sent to customers. The AI model continually improves as it processes more data, recognizing diverse handwriting styles, label placements, and variations in format.

Additionally, OCR output can be fed into analytics dashboards, providing insights into delivery times, regional trends, and shipment bottlenecks. By automating label processing, companies reduce operational costs, improve accuracy, and enhance customer satisfaction. This AI-driven solution is essential for modern logistics organizations aiming to achieve high scalability, operational efficiency, and real-time tracking capabilities.

Question 62:

A healthcare provider wants to extract structured data from patient intake forms, including checkboxes, text fields, and signatures, to automate their workflows. Which Azure AI service should they use?

Answer:

A) Form Recognizer
B) Computer Vision OCR
C) Text Analytics
D) Custom Vision

Explanation:

Form Recognizer is the correct answer because it is designed to extract structured and semi-structured data from documents such as patient intake forms. These forms contain checkboxes for symptoms, text fields for patient information, and sometimes signatures. Manual data entry is time-consuming and prone to human error, whereas Form Recognizer automates extraction, enabling accurate and scalable workflows.

Computer Vision OCR (Option B) can extract text but does not identify structured fields like checkboxes or tables effectively. Text Analytics (Option C) analyzes text but cannot process forms. Custom Vision (Option D) detects objects in images but cannot extract structured form data efficiently.

Form Recognizer supports prebuilt models for forms, receipts, and invoices, as well as custom models trained on domain-specific documents. It identifies key-value pairs, tables, and checkboxes, producing structured outputs that can be integrated into electronic health records (EHRs), billing systems, or analytics platforms. Signatures can also be detected and validated to ensure compliance with legal and regulatory requirements.

This AI-driven solution improves operational efficiency by reducing manual processing, minimizing errors, and accelerating workflows. Integration with Logic Apps, Power Automate, or custom APIs allows automation of downstream processes such as insurance verification, appointment scheduling, or alerts to medical staff. Form Recognizer also provides confidence scores for extracted data, enabling human review for low-confidence fields to ensure data accuracy.

By using Form Recognizer, healthcare organizations can transform manual form processing into a digital, automated workflow. This ensures faster patient intake, accurate records, and enhanced compliance with healthcare regulations. Over time, models can be retrained to handle new form variations, handwriting styles, and languages, making the solution highly adaptable and scalable.

Overall, Form Recognizer enables healthcare providers to improve efficiency, reduce errors, and maintain secure, accurate patient records while supporting automation and AI-driven insights. The combination of structured data extraction, integration capabilities, and machine learning ensures reliable processing for large volumes of patient forms.

Question 63:

A company wants to detect unusual patterns in sensor data from industrial machines to predict potential failures. Which Azure AI service should they use?

Answer:

A) Anomaly Detector
B) Custom Vision
C) Form Recognizer
D) Text Analytics

Explanation:

Anomaly Detector is the correct answer because it analyzes time-series data from sensors to identify patterns that deviate from expected behavior. In industrial environments, machine sensors generate large volumes of numeric data such as temperature, vibration, pressure, and flow rate. Detecting anomalies early allows organizations to predict equipment failures, schedule maintenance proactively, and reduce downtime.

Custom Vision (Option B) processes images, not numeric data. Form Recognizer (Option C) extracts data from documents. Text Analytics (Option D) analyzes unstructured text, which is irrelevant for sensor data.

Anomaly Detector can process single or multiple time-series streams, identify seasonal trends, and detect subtle deviations that might indicate early signs of failure. Confidence scores help prioritize alerts, and the system can integrate with Logic Apps, Power Automate, or IoT solutions for automated maintenance workflows.

Over time, the AI system improves its detection capability by learning from historical data, reducing false positives, and enhancing predictive accuracy. Integration with visualization tools like Power BI enables real-time monitoring of equipment health, performance trends, and anomaly patterns.

This approach reduces operational costs, minimizes unplanned downtime, and extends equipment lifespan. Industrial organizations gain actionable insights into machine performance, enabling data-driven decision-making and proactive maintenance strategies. Anomaly Detector ensures scalability, processing large volumes of sensor data in real time while providing actionable intelligence for operational excellence.

A company that wants to detect unusual patterns in sensor data from industrial machines to predict potential failures should use Anomaly Detector. Anomaly Detector is an Azure AI service specifically designed to analyze time-series data and identify values that deviate from expected patterns. In industrial environments, machines generate continuous streams of numeric data from sensors, including measurements such as temperature, vibration, pressure, flow rate, and other operational metrics. Detecting anomalies in this data is critical for identifying early signs of equipment failure, enabling organizations to implement predictive maintenance strategies and prevent costly unplanned downtime. Custom Vision, by contrast, is designed for image classification and object detection and cannot process numeric sensor data. Form Recognizer focuses on extracting structured data from documents and is not applicable to real-time machine telemetry. Text Analytics analyzes unstructured text and does not provide capabilities for evaluating numeric or time-series data.

Anomaly Detector can handle single or multiple time-series streams and is capable of recognizing complex patterns, seasonal trends, and subtle deviations that might otherwise go unnoticed. It assigns confidence scores to detected anomalies, allowing maintenance teams to prioritize alerts and respond to critical issues more effectively. Integration with tools like Azure Logic Apps, Power Automate, or IoT solutions enables organizations to automate alerting, trigger maintenance workflows, or adjust operational parameters in real time. Over time, the system improves its accuracy by learning from historical data, reducing false positives, and enhancing predictive performance.

Furthermore, Anomaly Detector can be combined with visualization platforms such as Power BI to provide real-time dashboards displaying machine health, performance trends, and anomaly occurrences. This allows operational teams to monitor equipment proactively and make data-driven decisions regarding maintenance schedules, resource allocation, and operational planning. By leveraging Anomaly Detector, industrial organizations can reduce operational costs, minimize unplanned downtime, extend the lifespan of machinery, and improve overall equipment efficiency. The service is scalable and capable of processing large volumes of sensor data continuously, ensuring that insights into equipment performance are timely, actionable, and reliable. This makes Anomaly Detector the most suitable choice for predictive maintenance and operational intelligence in industrial settings.

Question 64:

A retail company wants to automatically classify customer-uploaded images to identify products and provide recommendations. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) Anomaly Detector

Explanation:

Custom Vision is the correct answer because it allows organizations to train AI models to classify images, detect multiple objects, and assign relevant tags. Retailers can use this to recognize products from customer-uploaded images, enabling personalized recommendations, improved search experiences, and enhanced engagement.

Computer Vision OCR (Option B) extracts text, which is irrelevant for object classification. Text Analytics (Option C) analyzes text, not images. Anomaly Detector (Option D) monitors numeric deviations, not visual data.

Custom Vision supports custom models that can learn from labeled images, detect multiple objects, and assign confidence scores to predictions. These models can be deployed in the cloud or on edge devices for real-time inference. Integration with recommendation engines enables automated, personalized suggestions based on detected products.

This AI-powered approach enhances customer engagement, increases cross-selling opportunities, and allows data-driven merchandising decisions. Over time, models improve as more images are collected and labeled, ensuring higher accuracy, adaptability to new product categories, and better recommendation quality. Dashboards and analytics provide insights into popular products, trends, and user preferences, supporting business strategy and inventory planning.

Question 65:

A company wants to analyze customer support chat logs to detect sentiment, extract topics, and identify frequently asked questions. Which Azure AI service should they use?

Answer:

A) Text Analytics
B) Custom Vision
C) Form Recognizer
D) QnA Maker

Explanation:

Text Analytics is the correct answer because it processes unstructured text from chat logs to extract actionable insights. Sentiment analysis determines whether interactions are positive, negative, or neutral, helping organizations measure customer satisfaction. Key phrase extraction identifies common topics, while entity recognition detects products, services, or other relevant information.

Custom Vision (Option B) analyzes images, not text. Form Recognizer (Option C) extracts structured data from forms. QnA Maker (Option D) provides answers to predefined questions but does not analyze sentiment or extract topics.

Text Analytics supports multilingual processing, allowing global companies to analyze chats in multiple languages. Integration with dashboards enables tracking trends over time, identifying recurring issues, and informing support strategies. Insights from sentiment analysis can trigger automated workflows for escalation or feedback collection, improving customer service quality.

The service also allows organizations to define custom entities and domains, improving relevance for industry-specific terminology. Over time, the AI model adapts to new patterns in customer communication, ensuring actionable insights remain accurate and meaningful.

By leveraging Text Analytics for chat logs, companies enhance operational efficiency, identify opportunities for service improvement, and deliver proactive, data-driven customer support. The combination of sentiment analysis, topic extraction, and entity recognition enables organizations to gain a holistic view of customer interactions and implement targeted improvements.

A company that wants to analyze customer support chat logs to detect sentiment, extract topics, and identify frequently asked questions should use Text Analytics. Text Analytics is an Azure AI service designed to process unstructured text, enabling organizations to gain actionable insights from written communications such as chat logs, emails, and social media messages. By applying sentiment analysis, the service can determine whether interactions are positive, negative, or neutral, helping organizations measure overall customer satisfaction and identify areas where support may need improvement. Key phrase extraction allows the system to detect recurring topics, while entity recognition identifies important information such as product names, service types, account numbers, or locations. Custom Vision, in contrast, is focused on image classification and object detection and cannot analyze text. Form Recognizer is intended for extracting structured data from forms and documents, which does not apply to free-form chat logs. QnA Maker is used to provide automated responses to predefined questions but does not offer capabilities for analyzing sentiment, topics, or extracting entities.

Text Analytics supports multiple languages, making it ideal for global organizations that receive customer communications in diverse languages. The service can integrate with dashboards and reporting tools, enabling teams to monitor trends over time, track recurring issues, and assess customer sentiment on a continuous basis. Insights from sentiment and topic analysis can trigger automated workflows, such as escalating negative interactions to supervisors, sending follow-up surveys, or creating alerts for common technical issues, thereby improving service quality and responsiveness. Organizations can also define custom entities and domains to enhance recognition of industry-specific terminology, ensuring that the insights remain relevant and accurate.

Over time, the AI model can learn from new chat patterns and evolving language, maintaining high accuracy in detecting sentiment and identifying topics. By leveraging Text Analytics for customer support chat logs, companies can streamline operations, reduce response times, and deliver more proactive and data-driven service. The combination of sentiment analysis, topic extraction, and entity recognition enables a comprehensive understanding of customer interactions, allowing organizations to address recurring problems, improve product or service offerings, and enhance overall customer satisfaction. Text Analytics thus provides a scalable, intelligent, and reliable solution for extracting insights from large volumes of unstructured text, making it the most appropriate choice compared to the other options.

Question 66:

A financial institution wants to extract key metrics from annual reports, such as revenue, net income, and shareholder equity, to feed into their analytics platform. Which Azure AI service should they use?

Answer:

A) Form Recognizer
B) Custom Vision
C) Text Analytics
D) QnA Maker

Explanation:

Form Recognizer is the correct answer because it extracts structured and semi-structured data from documents, including tables, key-value pairs, and numeric metrics. Annual reports contain important financial metrics, and manual extraction is inefficient and error-prone. Form Recognizer automates the process, ensuring accuracy, scalability, and integration with analytics platforms.

Custom Vision (Option B) handles images. Text Analytics (Option C) processes unstructured text. QnA Maker (Option D) builds knowledge bases.

Form Recognizer can use prebuilt or custom models, extracting financial tables, charts, and numeric fields. Integration with dashboards or BI tools enables real-time insights, trend analysis, and predictive modeling. The system improves over time as more reports are processed, adapting to layout variations and document complexity.

This automation reduces manual labor, accelerates decision-making, ensures compliance, and allows finance teams to focus on strategic analysis rather than repetitive tasks.

Question 67:

A company wants to automatically extract information from invoices received in various formats, including PDF, scanned images, and emails. Which Azure AI service should they use?

Answer:

A) Form Recognizer
B) Custom Vision
C) Text Analytics
D) Computer Vision OCR

Explanation:

Form Recognizer is the correct answer because it is specifically designed to extract structured and semi-structured data from documents such as invoices. Invoices contain important fields like invoice numbers, vendor names, dates, amounts, tax information, and line items. Processing these manually is time-consuming, error-prone, and inefficient. Form Recognizer automates the extraction process, converting invoice data into machine-readable, structured formats suitable for accounting systems, ERP platforms, or analytics workflows.

Custom Vision (Option B) is designed for image classification and object detection, not text extraction from documents. Text Analytics (Option C) analyzes unstructured text but cannot parse structured invoice data. Computer Vision OCR (Option D) extracts text from images but does not organize it into structured fields like Form Recognizer does.

Form Recognizer supports both prebuilt models, such as the invoice model, and custom models tailored to an organization’s specific invoice formats. Prebuilt models can recognize common fields out-of-the-box, while custom models can handle vendor-specific layouts, handwritten notes, or embedded tables. It can also process multilingual invoices, supporting global operations.

Integration with automation tools such as Azure Logic Apps or Power Automate allows extracted data to feed directly into accounting workflows, trigger approval processes, or update financial dashboards. By automating invoice processing, companies reduce manual entry errors, accelerate accounts payable processes, and ensure faster reconciliation of financial transactions.

Form Recognizer also provides confidence scores for each extracted field, allowing human validation where necessary. Over time, as more invoices are processed, the model becomes more accurate in handling diverse layouts, fonts, and handwriting styles. The AI system can identify anomalies, such as unexpected amounts, missing fields, or duplicate invoices, enhancing internal controls and compliance.

In addition, analytics generated from processed invoices provide insights into vendor performance, cash flow trends, and cost optimization opportunities. Organizations can analyze patterns in invoice submission times, payment delays, and recurring discrepancies to improve financial management.

By using Form Recognizer, companies achieve a scalable, efficient, and reliable solution for invoice processing. It ensures accurate data extraction, accelerates financial workflows, supports regulatory compliance, and allows staff to focus on higher-value tasks such as analysis and decision-making. Overall, Form Recognizer transforms invoice management into a data-driven, automated process that enhances operational efficiency, accuracy, and transparency.

Question 68:

A hospital wants to convert handwritten doctor notes into digital text and then analyze them to extract medical conditions, medications, and patient information. Which combination of Azure AI services should they use?

Answer:

A) Computer Vision OCR and Text Analytics
B) Custom Vision and Form Recognizer
C) Anomaly Detector and QnA Maker
D) Translator Text and Text Analytics

Explanation:

The correct answer is Computer Vision OCR combined with Text Analytics. Computer Vision OCR converts handwritten or printed medical notes into machine-readable text. Handwritten notes often contain critical patient information such as diagnoses, medications, treatment plans, and observations. Manual transcription is inefficient, prone to errors, and can lead to delays in patient care. OCR automates this process, producing high-quality digital text suitable for downstream analysis.

Text Analytics then processes the digitized text to extract key entities, including medical conditions, patient identifiers, medications, dosages, and procedures. This enables structured insights for electronic health records (EHRs), analytics, and clinical decision support. Text Analytics can also perform sentiment or risk analysis in certain scenarios, highlighting notes that indicate urgent medical conditions or follow-ups required.

Custom Vision and Form Recognizer (Option B) are less suitable because Custom Vision focuses on object detection, not text, and Form Recognizer works best with structured forms, not freeform handwritten notes. Anomaly Detector and QnA Maker (Option C) are unrelated because they focus on numeric anomalies and question-answer interactions. Translator Text (Option D) only translates text and does not perform entity extraction.

Computer Vision OCR supports recognition of a variety of handwriting styles and scanned document qualities. It uses advanced machine learning algorithms to parse characters, spacing, and layouts accurately. Text Analytics further enriches this output by applying domain-specific models trained on medical terminology to identify clinically relevant entities accurately.

Integration with hospital workflows allows extracted information to update EHRs automatically, trigger alerts for critical conditions, or feed dashboards for medical analytics. The AI system can detect patterns in patient conditions, medication prescriptions, and treatment effectiveness over time, providing insights into healthcare trends and patient outcomes.

Using this combination enhances operational efficiency by reducing manual data entry, minimizes errors in medical records, improves patient safety, and enables data-driven decision-making. It also supports compliance with healthcare regulations like HIPAA, ensuring sensitive patient data is handled securely. Continuous model training improves accuracy, allowing the system to adapt to new handwriting styles, medical terminology, and document formats.

In summary, the combination of Computer Vision OCR and Text Analytics provides a comprehensive AI-powered solution for digitizing handwritten medical notes and extracting actionable clinical insights. It improves efficiency, accuracy, and patient care while enabling scalable, secure, and automated processing of medical documentation.

A hospital that wants to convert handwritten doctor notes into digital text and analyze them to extract medical conditions, medications, and patient information should use a combination of Computer Vision OCR and Text Analytics. Computer Vision OCR is an Azure AI service capable of converting both handwritten and printed medical notes into machine-readable text. Handwritten notes often contain critical patient information, including diagnoses, medications, treatment plans, observations, and follow-up instructions. Manually transcribing this information is labor-intensive, time-consuming, and prone to errors, which can delay patient care or lead to mistakes in medical records. OCR automates this process, producing high-quality digital text that is suitable for downstream analysis, ensuring that vital clinical information is accurately captured.

Once the notes are digitized, Text Analytics processes the text to extract meaningful entities and insights. This includes identifying medical conditions, patient identifiers, medications, dosages, procedures, and other relevant clinical data. Text Analytics can also support sentiment or risk assessment, flagging notes that indicate urgent conditions, potential complications, or the need for follow-up care. This combination enables hospitals to integrate extracted information into electronic health records (EHRs), clinical decision support systems, and analytics dashboards. Custom Vision and Form Recognizer are less appropriate because Custom Vision focuses on image classification and object detection, while Form Recognizer is optimized for structured forms rather than freeform handwritten notes. Anomaly Detector and QnA Maker are unrelated, as they focus on numeric anomaly detection and knowledge-base question answering, respectively. Translator Text only converts text between languages and cannot perform entity extraction or text analysis.

Computer Vision OCR is designed to handle a variety of handwriting styles, scan qualities, and document layouts. It uses advanced machine learning models to accurately parse characters, spacing, and formatting. Text Analytics enhances this output by applying domain-specific models trained on medical terminology, ensuring that clinical entities are recognized reliably. Integration with hospital workflows allows the automated update of EHRs, alerts for critical conditions, and population-level analytics for trends in patient care, treatment effectiveness, and medication usage. Over time, continuous training improves recognition accuracy for new handwriting styles, document formats, and terminology.

By leveraging Computer Vision OCR with Text Analytics, hospitals can significantly reduce manual data entry, minimize errors in medical records, improve patient safety, and enable data-driven decision-making. This combination provides a scalable, secure, and automated solution for digitizing handwritten medical documentation while extracting actionable clinical insights to support better patient outcomes and operational efficiency.

Question 69:

A financial institution wants to detect fraudulent transactions by analyzing transaction patterns over time. Which Azure AI service should they use?

Answer:

A) Anomaly Detector
B) Custom Vision
C) Form Recognizer
D) Text Analytics

Explanation:

Anomaly Detector is the correct answer because it analyzes time-series data to detect patterns that deviate from expected behavior. Financial institutions deal with high volumes of transactions daily. Anomaly Detector can identify unusual patterns such as large withdrawals, unexpected transfers, or transactions from unusual locations, which may indicate potential fraud.

Custom Vision (Option B) works with images, not numeric data. Form Recognizer (Option C) extracts structured data from documents, not transaction patterns. Text Analytics (Option D) analyzes text, not numeric transaction data.

Anomaly Detector processes single or multiple time-series streams, accounting for seasonal patterns, trends, and noise. Confidence scores help prioritize alerts for human investigation. The system can be integrated with automated workflows to freeze accounts, send notifications, or trigger investigations immediately.

Over time, Anomaly Detector improves its predictive accuracy as it learns from historical transaction data, reducing false positives while identifying subtle indicators of fraud. Integration with dashboards like Power BI provides a real-time view of potential risks, trends, and anomalies.

By using Anomaly Detector, financial institutions reduce operational risk, improve fraud detection accuracy, and enhance customer trust. It provides scalability to monitor millions of transactions in real time while enabling actionable insights for decision-makers. This AI-powered approach allows proactive fraud prevention, regulatory compliance, and efficient operational management.

Question 70:

A retailer wants to classify products from images uploaded by customers to provide personalized recommendations. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) Anomaly Detector

Explanation:

Custom Vision is the correct answer because it allows training of AI models to classify objects in images. Retailers can recognize products from customer-uploaded photos and recommend similar or complementary items.

Computer Vision OCR (Option B) extracts text from images, which is irrelevant here. Text Analytics (Option C) analyzes text. Anomaly Detector (Option D) monitors numeric patterns.

Custom Vision supports incremental learning, multi-object detection, and confidence scoring. Integration with recommendation engines provides personalized shopping experiences. Over time, models improve as more images are processed and labeled, ensuring accurate classification, adaptability to new products, and effective personalization strategies.

This approach enhances customer engagement, drives sales, and provides actionable insights for marketing and merchandising teams. Analytics dashboards can reveal trending products, user preferences, and demand patterns, enabling data-driven decision-making.

Question 71:

A company wants to extract and analyze information from contracts, including dates, parties involved, and key clauses, to improve legal review processes. Which Azure AI service should they use?

Answer:

A) Form Recognizer
B) Custom Vision
C) Text Analytics
D) Computer Vision OCR

Explanation:

Form Recognizer is the correct answer because it extracts structured and semi-structured data from documents like contracts. Legal teams deal with contracts containing dates, parties, clauses, and other important information. Manual review is time-consuming, error-prone, and inefficient. Form Recognizer automates extraction, producing structured outputs that can be analyzed or integrated into legal management systems.

Custom Vision (Option B) classifies images but does not extract document text or clauses. Text Analytics (Option C) analyzes text but is best suited for unstructured data, not structured extraction from complex contracts. Computer Vision OCR (Option D) extracts text from images but does not structure it into key fields or identify clauses effectively.

Form Recognizer allows training custom models to handle domain-specific contract formats and terminology. Prebuilt layouts or custom templates detect fields such as dates, signatures, clause headings, and parties. Once extracted, data can be used for contract compliance checks, renewal alerts, risk assessment, and reporting.

Integration with workflow tools like Power Automate or Azure Logic Apps enables automated alerts for approaching deadlines, approvals, or legal reviews. Confidence scores allow human validation of low-confidence extractions, ensuring accuracy in critical decisions.

By automating contract analysis, organizations reduce manual labor, improve compliance, accelerate legal processes, and gain actionable insights. Over time, AI models improve as more contracts are processed, adapting to new layouts, clause structures, and languages. This ensures scalability, accuracy, and efficiency in legal document management.

Form Recognizer can also detect tables, checkboxes, and signatures, supporting full automation of complex contract workflows. Analytics dashboards provide insights into contract trends, obligations, and risk exposure. The solution ensures legal and operational efficiency while reducing potential errors and delays associated with manual processing.

In summary, Form Recognizer provides a scalable, accurate, and automated solution for analyzing contracts, enabling faster legal review, compliance assurance, and actionable insights into organizational agreements. It supports structured extraction, workflow automation, and continuous model improvement for long-term efficiency gains.

Question 72:

A customer support team wants to analyze emails to detect sentiment, identify complaints, and categorize requests for proper routing. Which Azure AI service should they use?

Answer:

A) Text Analytics
B) Custom Vision
C) Form Recognizer
D) Anomaly Detector

Explanation:

Text Analytics is the correct answer because it analyzes unstructured text, detects sentiment, extracts key phrases, and identifies entities. In customer support, emails often contain complaints, inquiries, or feedback. Text Analytics can automatically classify these emails, detect negative sentiment, and identify the nature of the request, enabling automated routing to the appropriate department.

Custom Vision (Option B) focuses on images, not text. Form Recognizer (Option C) extracts structured data from forms, not emails. Anomaly Detector (Option D) identifies numeric anomalies and cannot analyze text sentiment or context.

Text Analytics supports multilingual analysis, allowing companies to process emails in multiple languages and maintain consistent quality in global operations. Key phrase extraction identifies topics discussed in emails, while named entity recognition detects customers, products, dates, or locations. Sentiment scores indicate whether the email expresses satisfaction, dissatisfaction, or neutral feedback, enabling priority handling.

Integration with workflow tools like Logic Apps or Power Automate allows routing emails automatically to relevant teams, triggering alerts for urgent complaints, or updating CRM systems. Analytics dashboards can track customer sentiment trends, common complaints, and response times.

By automating email analysis, organizations reduce manual review, improve response times, and enhance customer satisfaction. Continuous learning improves accuracy as more emails are processed, adapting to evolving language patterns and customer behaviors.

Overall, Text Analytics provides a scalable, efficient, and automated solution for understanding customer communication, enabling actionable insights, and improving operational efficiency in customer support processes.

Question 73:

A logistics company wants to automatically detect package damages by analyzing images taken during transit. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) Form Recognizer

Explanation:

Custom Vision is the correct answer because it allows training AI models to detect specific objects and classify images. In logistics, packages may be damaged during handling or transit. Custom Vision can analyze images captured by cameras at warehouses, sorting facilities, or delivery checkpoints to identify damaged packages.

Computer Vision OCR (Option B) extracts text, which is not relevant here. Text Analytics (Option C) analyzes text content, not images. Form Recognizer (Option D) extracts structured data from documents, not visual defects.

Custom Vision supports supervised learning by training models on labeled images of damaged and intact packages. Once trained, the model can detect damages in real time, flagging packages for review or replacement. Confidence scores allow teams to prioritize verification of high-probability damage cases.

Integration with warehouse management systems enables automated alerts, workflow initiation for package inspection, and reporting for logistics operations. The model can continuously improve as more images are processed, adapting to new packaging materials, lighting conditions, and damage types.

Using Custom Vision for damage detection improves operational efficiency, reduces customer complaints, minimizes financial losses, and ensures timely intervention to replace damaged goods. Analytics dashboards provide insights into packaging trends, handling issues, and process optimization opportunities.

This AI-driven approach enables scalable, accurate, and proactive management of package conditions, ensuring high-quality service in logistics operations.

Question 74:

A healthcare organization wants to analyze medical research papers to identify trends, extract key topics, and detect emerging diseases. Which Azure AI service should they use?

Answer:

A) Text Analytics
B) Custom Vision
C) Form Recognizer
D) QnA Maker

Explanation:

Text Analytics is the correct answer because it analyzes unstructured text to extract key phrases, named entities, and sentiment. In medical research, papers contain valuable information on diseases, treatments, and trends. Text Analytics can automatically identify topics, extract entities such as medications, conditions, and research locations, and detect emerging health threats.

Custom Vision (Option B) analyzes images, not text. Form Recognizer (Option C) extracts structured data from documents but is not optimized for research papers. QnA Maker (Option D) builds conversational knowledge bases but does not analyze textual trends.

Text Analytics supports large-scale processing of documents, multiple languages, and domain-specific customization. By integrating with dashboards, organizations can track research trends, identify frequently studied conditions, and discover emerging health risks. Named entity recognition helps to link diseases, drugs, and organizations for trend mapping.

This AI-powered analysis reduces manual review, accelerates research insights, and enables proactive public health measures. Models can be continuously updated to improve accuracy, capture new terminology, and adapt to evolving research fields.

Overall, Text Analytics provides scalable, automated, and actionable insights from vast volumes of medical literature, supporting data-driven decisions in healthcare research and policy.

Question 75:

A bank wants to automate KYC (Know Your Customer) processes by extracting information from customer-submitted forms and identity documents. Which Azure AI service should they use?

Answer:

A) Form Recognizer
B) Computer Vision OCR
C) Text Analytics
D) Custom Vision

Explanation:

Form Recognizer is the correct answer because it extracts structured information from forms and identity documents, including names, addresses, ID numbers, dates, and other verification fields. Banks must comply with KYC regulations to verify customer identities. Manual processing is slow and error-prone, whereas Form Recognizer automates extraction, validation, and integration into banking systems.

Computer Vision OCR (Option B) extracts text but does not structure the information. Text Analytics (Option C) analyzes text sentiment and entities but is not optimized for structured form extraction. Custom Vision (Option D) detects objects in images but does not extract structured data.

Form Recognizer can handle various document types, including passports, driver’s licenses, and utility bills. Integration with automation workflows allows validation, fraud detection, and regulatory compliance reporting. Confidence scores help flag documents for manual review when necessary.

By automating KYC processes, banks reduce operational costs, improve accuracy, accelerate onboarding, and ensure compliance with regulatory standards. Models can be retrained as new document formats or regulatory requirements emerge, maintaining adaptability and reliability.

Form Recognizer provides analytics dashboards to monitor document processing times, error rates, and compliance adherence. This supports operational optimization, risk management, and improved customer experience.

In summary, Form Recognizer is the ideal solution for automated KYC, providing scalable, accurate, and compliant extraction of structured information from identity documents and forms.

Question 76:

A company wants to provide real-time translation for customer feedback submitted in multiple languages and analyze sentiment. Which combination of Azure AI services should they use?

Answer:

A) Translator Text and Text Analytics
B) Custom Vision and Form Recognizer
C) QnA Maker and Anomaly Detector
D) Computer Vision OCR and Custom Vision

Explanation:

Translator Text and Text Analytics is the correct answer because Translator Text converts feedback into a unified language, and Text Analytics performs sentiment analysis, key phrase extraction, and entity recognition. This enables consistent analysis of multilingual customer feedback and identification of trends, complaints, or emerging needs.

Custom Vision and Form Recognizer (Option B) are for images and forms, not text translation or analysis. QnA Maker and Anomaly Detector (Option C) focus on knowledge bases and numeric anomalies, not multilingual sentiment analysis. Computer Vision OCR and Custom Vision (Option D) process images but cannot translate or analyze text sentiment.

Translator Text uses neural machine translation to preserve context, while Text Analytics identifies sentiment and extracts critical entities. Integration with dashboards allows tracking of global feedback trends, identification of emerging issues, and measurement of customer satisfaction across markets.

This AI-powered workflow reduces manual translation, ensures consistent insights, improves operational efficiency, and enables data-driven customer engagement strategies. Continuous learning enhances translation accuracy and sentiment detection over time, adapting to new terminology, idioms, or market-specific language patterns.

Question 77:

A manufacturer wants to identify defective products by analyzing images captured on the production line. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) Form Recognizer

Explanation:

Custom Vision is the correct answer because it allows object detection and classification of images. In manufacturing, defects such as scratches, misalignments, or missing components can be automatically detected on the production line.

Computer Vision OCR (Option B) extracts text, which is irrelevant. Text Analytics (Option C) processes text, not images. Form Recognizer (Option D) extracts structured data from documents.

Custom Vision models can be trained on labeled images of defective and intact products, deployed for real-time detection, and integrated with quality control systems. Confidence scores prioritize verification, and continuous model improvement adapts to new defect types and variations. Analytics dashboards provide insights into defect trends, supporting operational optimization and quality assurance.

Question 78:

A bank wants to analyze unstructured text from customer feedback to detect complaints, identify key topics, and measure sentiment. Which Azure AI service should they use?

Answer:

A) Text Analytics
B) Custom Vision
C) Form Recognizer
D) QnA Maker

Explanation:

Text Analytics is the correct answer because it analyzes unstructured text, detects sentiment, extracts key phrases, and identifies entities. This allows banks to identify complaints, understand customer concerns, and take proactive measures.

Custom Vision (Option B) analyzes images. Form Recognizer (Option C) extracts structured form data. QnA Maker (Option D) provides question-answer functionality but does not perform sentiment analysis.

Text Analytics can handle multilingual feedback, extract domain-specific entities, and provide insights into recurring complaints. Integration with dashboards and workflow tools allows tracking trends, escalating critical issues, and optimizing customer experience. Continuous learning ensures accuracy and relevance of insights over time.

Question 79:

A logistics company wants to track vehicles and pedestrians in real-time using traffic camera footage. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) Form Recognizer

Explanation:

Custom Vision is the correct answer because it allows object detection in images and videos. Traffic cameras capture vehicles, pedestrians, and other objects, which Custom Vision can detect in real-time for traffic management, congestion analysis, and public safety.

Computer Vision OCR (Option B) extracts text. Text Analytics (Option C) analyzes text. Form Recognizer (Option D) extracts structured form data.

Custom Vision models can be trained on labeled images, deployed on edge devices or in the cloud, and integrated with traffic management dashboards. Confidence scores help prioritize alerts, and continuous learning improves detection of new patterns, vehicle types, and pedestrian behavior. Analytics provide insights into traffic trends, peak hours, and incidents, enabling data-driven urban planning and traffic optimization.

Question 80:

A company wants to provide a knowledge-based chatbot that can answer customer questions and improve over time. Which Azure AI service should they use?

Answer:

A) QnA Maker
B) Custom Vision
C) Anomaly Detector
D) Computer Vision

Explanation:

QnA Maker is the correct answer because it enables the creation of knowledge bases from FAQs, manuals, and documents, which chatbots can use to respond to customer queries. It supports continuous learning by logging unanswered questions and allowing administrators to update the knowledge base over time.

Custom Vision (Option B) analyzes images. Anomaly Detector (Option C) detects numeric anomalies. Computer Vision (Option D) extracts text from images.

Integration with Azure Bot Service allows deployment across websites, mobile apps, and messaging platforms. Multi-turn conversation capability allows chatbots to handle follow-up questions. Analytics on usage identifies knowledge gaps and improves customer support efficiency. Over time, QnA Maker ensures chatbots remain accurate, relevant, and effective in delivering automated customer service.