Microsoft AI-900 Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81:

A company wants to extract structured data from receipts and automatically populate its expense management system. Which Azure AI service should they use?

Answer:

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

Explanation:

Form Recognizer is the correct answer because it can extract structured data from receipts, invoices, and similar financial documents. Receipts often contain information such as vendor names, dates, totals, taxes, and itemized purchases. Manual entry into expense management systems is error-prone and inefficient. Form Recognizer automates extraction, converting receipts into machine-readable formats compatible with ERP and accounting systems.

Custom Vision (Option B) classifies images but does not extract textual data. Computer Vision OCR (Option C) extracts text but does not structure it into fields like vendor name, total, or date. Text Analytics (Option D) analyzes unstructured text, not structured receipt data.

Form Recognizer supports prebuilt receipt models that identify key fields across various layouts and formats. Custom models can be trained for specialized receipts or vendor-specific formats. The AI provides confidence scores for each extracted field, allowing human validation for low-confidence items, improving reliability.

Integration with workflow automation tools like Power Automate or Logic Apps ensures receipts are automatically routed to accounting, approvals are triggered, and entries are reconciled in real-time. This reduces manual labor, improves accuracy, and accelerates financial processes.

Analytics dashboards provide insights into spending patterns, vendor behavior, and compliance metrics. Over time, models improve with more receipt data, adapting to new layouts, languages, and formats. The system supports scalability for organizations processing thousands of receipts daily.

By leveraging Form Recognizer, companies achieve efficient, automated, and accurate receipt processing, reducing costs, improving transparency, and enabling data-driven decision-making. It ensures compliance with corporate and tax regulations while streamlining expense management workflows.

Question 82:

A healthcare provider wants to analyze scanned patient forms to extract medical history, medications, and allergies. 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 data from forms and documents, including tables, checkboxes, and handwritten or printed text. Patient forms often include structured fields such as past medical history, current medications, allergies, and lifestyle information. Manual processing is labor-intensive and prone to errors. Form Recognizer automates extraction, producing structured outputs suitable for EHRs, analytics, and clinical decision support.

Computer Vision OCR (Option B) extracts text but cannot organize it into structured fields effectively. Text Analytics (Option C) analyzes unstructured text but cannot process forms directly. Custom Vision (Option D) identifies objects in images but cannot extract text or structured data from forms.

Form Recognizer can be trained on domain-specific templates or use prebuilt models for common medical forms. It identifies key-value pairs, tables, and checkboxes, generating structured outputs for automated workflows. Signatures and consent fields can also be detected, supporting compliance requirements.

Integration with hospital systems enables automated updating of patient records, alerts for missing information, and analytics on medical trends, treatment outcomes, or risk factors. Over time, continuous learning improves accuracy for diverse handwriting, layout changes, and multilingual forms.

Using Form Recognizer reduces manual data entry, improves patient care, accelerates clinical workflows, and ensures compliance with healthcare regulations. Dashboards provide insights into common conditions, medication trends, and patient demographics, supporting data-driven decision-making.

In summary, Form Recognizer provides a comprehensive, automated solution for extracting structured data from patient forms, enhancing operational efficiency, patient safety, and compliance while enabling scalable healthcare analytics.

A healthcare provider that wants to analyze scanned patient forms to extract medical history, medications, and allergies should use Form Recognizer. Form Recognizer is an Azure AI service designed to automatically extract structured data from forms and documents, including printed and handwritten text, tables, checkboxes, and other structured fields. Patient forms typically contain critical information such as past medical history, current medications, allergies, lifestyle information, and consent signatures. Manually processing these forms is time-consuming, labor-intensive, and prone to errors, which can lead to delays in updating patient records and potential risks in clinical decision-making. Form Recognizer automates this process, producing structured outputs that are suitable for integration with electronic health records, analytics tools, and clinical decision support systems.

Computer Vision OCR can extract text from images and scanned documents but lacks the ability to organize extracted data into structured fields, making it less suitable for processing complex patient forms. Text Analytics analyzes unstructured text but cannot directly handle scanned forms or extract key-value pairs and tables. Custom Vision focuses on identifying objects in images and is not designed for text extraction or structured data processing. Form Recognizer, however, can use prebuilt models for common medical forms or be trained on domain-specific templates to handle hospital-specific layouts. It accurately identifies key-value pairs, tables, checkboxes, and signatures, generating structured data outputs that can feed into automated workflows.

Integration with hospital systems allows the automated updating of patient records, verification of missing or inconsistent information, and analytics on medical trends, treatment outcomes, and risk factors. Form Recognizer can also handle handwriting, multiple languages, and variations in form layouts, and continuous learning improves accuracy over time. By automating the extraction of structured data from patient forms, hospitals can reduce manual data entry, minimize errors, accelerate clinical workflows, and ensure compliance with healthcare regulations such as HIPAA. Dashboards can provide insights into common medical conditions, medication usage patterns, and patient demographics, supporting data-driven decision-making.

In summary, Form Recognizer offers a comprehensive solution for automating the analysis of patient forms, extracting critical clinical data efficiently, enhancing operational efficiency, improving patient care, and enabling scalable healthcare analytics. Its ability to process structured and semi-structured forms makes it the most suitable Azure AI service for this scenario.

Question 83:

A financial institution wants to detect anomalies in real-time stock trading data to prevent fraudulent activity. 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 numeric time-series data to detect deviations from expected patterns. In stock trading, real-time data streams include prices, volumes, and transaction frequencies. Unusual patterns may indicate fraudulent activity, market manipulation, or system errors. Anomaly Detector identifies these anomalies promptly, enabling immediate action.

Custom Vision (Option B) processes images, not numeric data. Form Recognizer (Option C) extracts structured document data. Text Analytics (Option D) analyzes unstructured text, which is irrelevant for detecting anomalies in trading data.

Anomaly Detector supports multiple time-series streams and accounts for trends, seasonal variations, and noise. It generates confidence scores, allowing prioritization of alerts for human review. Integration with dashboards provides real-time monitoring and visualization of anomalies across markets or portfolios.

The system improves predictive accuracy over time by learning from historical data, reducing false positives while capturing subtle indicators of unusual trading activity. Integration with automated workflows can trigger immediate preventive actions, regulatory reporting, or alerts to compliance teams.

By using Anomaly Detector, financial institutions reduce operational risk, improve fraud detection, ensure compliance with trading regulations, and maintain trust with investors. It scales to monitor large volumes of transactions and delivers actionable insights to support proactive decision-making. Continuous learning ensures models adapt to new market conditions, trading behaviors, and financial instruments.

Overall, Anomaly Detector provides a reliable, scalable, and automated solution for real-time anomaly detection in financial markets, enhancing operational efficiency, security, and regulatory compliance.

Question 84:

A retail company wants to provide personalized recommendations based on images of products uploaded by customers. 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 models to classify products, detect multiple objects in images, and assign relevant tags. This enables personalized recommendations when customers upload product images or photos of items they like.

Computer Vision OCR (Option B) extracts text, which is not relevant for visual product recognition. Text Analytics (Option C) analyzes text content, not images. Anomaly Detector (Option D) identifies numeric deviations, not visual patterns.

Custom Vision supports incremental learning, multi-object detection, and confidence scoring. Models can be deployed in the cloud or at the edge for real-time inference. Integration with recommendation engines ensures customers receive relevant, personalized suggestions based on their uploaded images.

This AI solution improves customer engagement, drives cross-selling opportunities, and provides actionable insights into customer preferences. Analytics dashboards reveal trends in popular products, user behavior, and visual search patterns. Over time, models improve as more images are labeled, adapting to new products and categories.

By leveraging Custom Vision, retailers create a scalable, automated solution for visual product recognition, enhancing personalization, increasing customer satisfaction, and supporting data-driven merchandising decisions.

A retail company that wants to provide personalized recommendations based on images of products uploaded by customers should use Custom Vision. Custom Vision is an Azure AI service that allows organizations to build, train, and deploy custom image classification and object detection models. Retailers can use this service to recognize different products, detect multiple items in a single image, and assign relevant tags or categories. When a customer uploads a photo of a product they like, Custom Vision can analyze the image and match it to the catalog of available items, enabling personalized recommendations that are highly relevant to the user’s preferences. Computer Vision OCR, by contrast, is designed to extract text from images and documents and does not provide capabilities for recognizing visual product features. Text Analytics focuses on analyzing textual content and cannot process images, while Anomaly Detector is intended for identifying unusual patterns in numeric data rather than visual patterns in images.

Custom Vision supports incremental learning, which allows models to be continuously improved as new images are labeled, ensuring accuracy remains high even as new products are introduced. Multi-object detection enables recognition of several items within a single image, which is important in retail scenarios where customers may upload photos containing multiple products. The service also provides confidence scoring, helping determine how likely a detected object matches a specific product, and models can be deployed both in the cloud and at the edge to provide real-time inference and recommendations. Integration with recommendation engines and e-commerce platforms allows retailers to provide instant, personalized suggestions, improving customer engagement and increasing opportunities for cross-selling and upselling.

Additionally, the insights generated from Custom Vision can be used to inform merchandising strategies, track trends in popular products, and understand user behavior and preferences. Dashboards can display visual search patterns, customer interests, and emerging trends, helping retail teams make data-driven decisions on inventory and marketing campaigns. Over time, as the system processes more images, it becomes increasingly accurate and adaptable, learning to recognize new products, styles, and variations. By leveraging Custom Vision, retailers can create a scalable, automated, and AI-powered solution for visual product recognition, enhancing personalization, increasing customer satisfaction, and supporting smarter merchandising and marketing strategies.

Question 85:

A company wants to extract key metrics from annual reports, such as revenue, net income, and shareholder equity. 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 data from documents, including tables, key-value pairs, and numeric metrics. Annual reports contain financial information in tables, charts, and narratives. Manual extraction is slow, error-prone, and inefficient. Form Recognizer automates this process, producing structured outputs compatible with analytics platforms, dashboards, and reporting tools.

Custom Vision (Option B) identifies objects in images. Text Analytics (Option C) analyzes unstructured text. QnA Maker (Option D) is for building knowledge bases.

Form Recognizer allows use of prebuilt or custom models, capable of recognizing complex layouts, tables, and numeric fields. Integration with BI tools such as Power BI provides real-time insights, trend analysis, and predictive modeling. Over time, models improve as more reports are processed, adapting to layout changes, fonts, and complex tables.

Automated extraction reduces manual labor, accelerates decision-making, ensures compliance, and enables finance teams to focus on strategic analysis. Organizations can analyze financial performance, identify trends, and make data-driven decisions efficiently.

Question 86:

A company wants to monitor social media posts to detect emerging trends, customer sentiment, and brand perception. 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 social media posts, extracting key phrases, sentiment, and entities. This allows companies to detect emerging trends, gauge customer perception, and identify potential issues or opportunities.

Custom Vision (Option B) analyzes images, not text. Form Recognizer (Option C) extracts structured data from forms or documents. QnA Maker (Option D) provides knowledge-based answers but does not analyze social media trends.

Text Analytics supports multilingual text processing, sentiment scoring, and domain-specific entity recognition. Integration with analytics platforms allows dashboards to track sentiment over time, identify trending topics, and measure brand perception. Continuous learning ensures models adapt to slang, hashtags, and evolving language patterns.

This AI-powered solution enables organizations to respond proactively, optimize marketing strategies, and make data-driven decisions regarding products, services, or campaigns. Insights can be used to improve customer satisfaction, address complaints promptly, and capitalize on positive sentiment.A company that wants to monitor social media posts to detect emerging trends, customer sentiment, and brand perception should use Text Analytics. Text Analytics is an Azure AI service designed to process unstructured text and extract actionable insights. Social media posts are often informal, diverse in language, and contain a mixture of slang, hashtags, and abbreviations. Text Analytics can analyze these posts to detect sentiment, identify key phrases, and extract entities such as product names, locations, or customer references. This enables organizations to understand how customers perceive their brand, identify emerging trends, and respond to issues proactively. Custom Vision, by contrast, focuses on image classification and object detection and cannot analyze textual content. Form Recognizer is used for extracting structured data from forms and documents, which does not apply to social media monitoring. QnA Maker provides answers to predefined questions but does not analyze unstructured text or detect sentiment trends.

Text Analytics supports multilingual processing, allowing global organizations to monitor social media posts in multiple languages. Sentiment scoring can categorize posts as positive, negative, or neutral, providing a quantitative measure of customer perception over time. Key phrase extraction highlights topics or themes that are frequently mentioned, helping marketing teams and product managers identify trends and areas for improvement. Entity recognition detects specific mentions of products, competitors, or influencers, providing a deeper understanding of brand perception. Integration with analytics platforms and dashboards enables real-time monitoring of trends, allowing organizations to visualize shifts in sentiment, track campaign effectiveness, and prioritize responses to customer feedback.

Continuous learning ensures that the AI models adapt to evolving language patterns, new slang, and emerging hashtags, maintaining accuracy as social media language changes. Insights derived from Text Analytics can guide data-driven marketing strategies, optimize campaigns, enhance customer engagement, and improve overall customer satisfaction. Companies can respond promptly to complaints, capitalize on positive sentiment, and identify opportunities for product improvement or service enhancements. By leveraging Text Analytics for social media monitoring, organizations gain a scalable, automated solution that provides actionable intelligence from vast amounts of unstructured text, supporting proactive brand management, competitive analysis, and informed decision-making.

Question 87:

A logistics company wants to detect and classify packages with damaged labels or tampering 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 can detect and classify objects in images. Damaged or tampered packages can be automatically identified using images captured during transit or handling. Custom Vision models can be trained on labeled images of damaged and intact packages to detect issues in real time.

Computer Vision OCR (Option B) extracts text but does not detect visual defects. Text Analytics (Option C) analyzes unstructured text. Form Recognizer (Option D) extracts structured data from documents, not package images.

Integration with logistics workflows enables automated alerts for damaged packages, routing for inspection or replacement, and reporting of damage trends. Over time, models improve detection accuracy for new packaging types, lighting conditions, and damage patterns. Analytics dashboards provide operational insights for process improvement and customer satisfaction.

Question 88:

A bank wants to detect unusual transactions and prevent fraudulent activity by monitoring time-series financial data. 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 numeric time-series data to detect deviations from normal patterns. Banks process millions of transactions daily, and anomalies such as large withdrawals, unusual locations, or unexpected patterns can indicate fraud. Anomaly Detector identifies these in real time, enabling immediate intervention.

Custom Vision (Option B) analyzes images. Form Recognizer (Option C) extracts structured document data. Text Analytics (Option D) analyzes text content.

Anomaly Detector accounts for trends, seasonal effects, and noise. It provides confidence scores, reduces false positives, and integrates with automated workflows to alert compliance teams, freeze accounts, or trigger investigations. Over time, the model improves by learning from historical data and adjusting thresholds for anomalies.

This AI-powered solution enhances fraud detection, reduces risk, improves compliance, and ensures financial security. Integration with dashboards provides real-time monitoring and actionable insights.

A bank that wants to detect unusual transactions and prevent fraudulent activity by monitoring time-series financial data should use Anomaly Detector. Anomaly Detector is an Azure AI service specifically designed to analyze numeric data over time and identify deviations from expected patterns. Financial institutions process millions of transactions every day, including deposits, withdrawals, transfers, and payments. Detecting anomalies such as unusually large withdrawals, transactions from unexpected locations, rapid succession of payments, or patterns that differ from a customer’s typical behavior is critical for identifying potential fraud. Custom Vision, in contrast, is designed for image classification and object detection and is not applicable to numeric financial data. Form Recognizer extracts structured data from documents, such as invoices or forms, and cannot analyze transaction patterns. Text Analytics focuses on unstructured text and is not suitable for time-series numerical analysis.

Anomaly Detector uses advanced statistical models and machine learning techniques to account for trends, seasonal variations, and noise in time-series data. It can process single or multiple streams of financial metrics, providing real-time alerts when anomalies are detected. Confidence scores help compliance and risk teams prioritize investigations, ensuring that the most suspicious transactions are addressed quickly. Integration with automated workflows allows banks to trigger actions such as freezing accounts, sending alerts to customers, or initiating further review of flagged transactions. Over time, the service improves accuracy by learning from historical transaction data, adjusting anomaly thresholds, and reducing false positives, which enhances operational efficiency.

Using Anomaly Detector enables financial institutions to enhance fraud detection capabilities, reduce financial risk, and maintain compliance with regulatory requirements. Integration with dashboards and reporting tools provides real-time monitoring of transaction patterns, helping risk teams visualize trends, identify suspicious behavior, and make informed decisions. This scalable, AI-powered solution ensures that anomalies are detected quickly, supporting timely intervention and protecting both the bank and its customers from potential financial losses. By leveraging Anomaly Detector, banks gain actionable insights from transaction data, improve fraud prevention, and strengthen overall financial security.

Question 89:

A retailer wants to analyze customer reviews to detect sentiment, identify popular products, and extract key features mentioned. 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, extracts key phrases, detects entities, and measures sentiment. Customer reviews contain insights about product features, satisfaction, and complaints. By analyzing this data, retailers can identify popular products, areas for improvement, and overall customer sentiment.

Custom Vision (Option B) analyzes images, not text. Form Recognizer (Option C) extracts structured data from documents. QnA Maker (Option D) creates conversational knowledge bases but does not perform sentiment or entity analysis.

Text Analytics supports multiple languages and domain-specific customization. Integration with dashboards enables trend tracking, monitoring of product feedback, and sentiment over time. Continuous learning ensures analysis adapts to new terminology, slang, and customer expressions.

Retailers can use insights to inform product development, marketing strategies, and personalized recommendations. Automation reduces manual review, improves accuracy, and accelerates decision-making.

Question 90:

A company wants to build a chatbot that answers customer questions using a knowledge base and improves 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 creation of knowledge bases from FAQs, documents, or manuals. The chatbot uses this knowledge base to answer customer queries. QnA Maker supports continuous learning, logging unanswered questions, and updating content to improve chatbot responses over time.

Custom Vision (Option B) detects and classifies images. Anomaly Detector (Option C) monitors numeric deviations. Computer Vision (Option D) extracts text from images.

Integration with Azure Bot Service allows deployment across websites, apps, or messaging platforms. Multi-turn conversations enable follow-up questions. Analytics on interactions identify knowledge gaps and enhance chatbot effectiveness. Over time, QnA Maker ensures the chatbot remains accurate, relevant, and capable of delivering automated customer service efficiently.

A company that wants to build a chatbot capable of answering customer questions using a knowledge base and improving over time should use QnA Maker. QnA Maker is an Azure AI service designed to create knowledge bases from structured content such as FAQs, product manuals, company documents, and web pages. By leveraging QnA Maker, organizations can enable chatbots to provide accurate and timely responses to customer queries without human intervention, improving service efficiency and customer satisfaction. Custom Vision, by contrast, focuses on image classification and object detection, which is unrelated to conversational AI. Anomaly Detector analyzes numeric data for unusual patterns, which is not applicable for building chatbots. Computer Vision extracts text from images, which does not provide conversational knowledge or answer customer questions.

QnA Maker supports continuous learning by tracking unanswered questions and logging them for review, allowing organizations to update the knowledge base and improve chatbot accuracy over time. This ensures that the chatbot adapts to changing customer needs, resolves new types of inquiries, and reduces the number of unanswered questions. The service also supports multi-turn conversations, enabling the chatbot to handle follow-up questions, clarify context, and provide more complete responses. Integration with Azure Bot Service allows deployment of the chatbot across multiple platforms, including websites, mobile applications, Microsoft Teams, and other messaging channels, creating a seamless omnichannel customer service experience.

Analytics capabilities built into QnA Maker provide insights into how users interact with the chatbot, highlighting knowledge gaps, frequently asked questions, and areas where additional content may be required. This data-driven approach allows organizations to optimize the knowledge base continuously and ensure the chatbot remains relevant and effective. By combining QnA Maker with natural language understanding and AI-driven insights, companies can automate customer service, reduce response times, and allocate human agents to more complex or high-value tasks. Over time, this results in enhanced customer satisfaction, increased efficiency, and scalable support operations.

Overall, QnA Maker provides a comprehensive solution for building intelligent chatbots that learn and improve continuously, delivering accurate and context-aware responses to customer inquiries. Its integration with Azure Bot Service, analytics, and content management ensures a scalable, adaptive, and efficient approach to automated customer support, making it the most suitable Azure AI service for this scenario.

Question 91:

A healthcare provider wants to extract handwritten prescriptions and convert them into digital text for pharmacy processing. 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 extracts both printed and handwritten text. Handwritten prescriptions often contain medication names, dosages, and instructions. Manual transcription is error-prone and inefficient. OCR converts handwriting into machine-readable text, enabling accurate pharmacy processing.

Custom Vision (Option B) detects objects in images. Text Analytics (Option C) analyzes unstructured text but cannot process handwritten notes. Form Recognizer (Option D) is best for structured forms.

OCR supports diverse handwriting styles and noisy scans. Integration with pharmacy systems allows automated verification, error detection, and workflow acceleration. Continuous learning improves recognition accuracy. Using OCR improves operational efficiency, reduces transcription errors, and ensures patient safety.

Question 92:

A financial company wants to automatically extract tables from investment reports for analysis and forecasting. 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 data, including tables, key-value pairs, and numeric metrics from complex documents like investment reports. This enables automated analysis, forecasting, and integration with analytics platforms.

Custom Vision (Option B) processes images. Text Analytics (Option C) analyzes unstructured text. Computer Vision OCR (Option D) extracts text but does not structure tables effectively.

Form Recognizer can handle diverse report layouts, detect nested tables, and produce machine-readable outputs. Integration with BI dashboards enables trend analysis, predictive modeling, and automated reporting. Continuous model improvement ensures adaptability to new formats, improving efficiency and accuracy over time.

Question 93:

A retail company wants to analyze social media images to detect product placement and branding effectiveness. 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 classifies and detects objects in images. Retailers can analyze social media photos to detect brand logos, product placement, or competitor activity. This provides insights into marketing effectiveness and brand visibility.

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

Custom Vision supports model training on labeled images, edge or cloud deployment, and confidence scoring. Insights can be visualized on dashboards, informing marketing strategy, campaigns, and product promotion. Continuous learning improves detection accuracy and adapts to evolving trends, lighting conditions, or new products.

Question 94:

A bank wants to analyze unstructured customer complaints to detect patterns 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 extracts entities, key phrases, and sentiment from unstructured text. Banks can identify patterns, measure satisfaction, and detect negative experiences.

Custom Vision (Option B) processes images. Form Recognizer (Option C) extracts structured data. QnA Maker (Option D) builds knowledge bases but does not analyze sentiment.

Text Analytics supports multilingual processing, domain-specific customization, and integration with dashboards for real-time insights. Continuous learning ensures relevance as new complaints arise. Automated routing of critical complaints improves response time, customer satisfaction, and operational efficiency.

A bank that wants to analyze unstructured customer complaints to detect patterns and measure sentiment should use Text Analytics. Text Analytics is an Azure AI service designed to process unstructured text, extracting actionable insights such as key phrases, entities, and sentiment. In a banking context, customer complaints often contain valuable information about service issues, product problems, or areas where the customer experience can be improved. By applying Text Analytics, banks can automatically identify recurring themes, categorize complaints by topic, and measure the overall sentiment expressed in customer communications. Custom Vision, by contrast, focuses on image classification and object detection, which is not relevant for analyzing text-based complaints. Form Recognizer extracts structured data from documents or forms but cannot process free-form unstructured text. QnA Maker is designed to provide answers to predefined questions in a knowledge base but does not analyze sentiment or detect patterns in customer feedback.

Text Analytics supports multilingual processing, making it suitable for banks with international operations that receive complaints in various languages. Sentiment analysis allows organizations to quantify customer satisfaction and identify complaints that require urgent attention, while key phrase extraction highlights common issues, product mentions, or service-related concerns. Entity recognition detects references to specific accounts, products, or services, providing deeper context for decision-making. Integration with analytics dashboards and reporting tools enables real-time monitoring of trends, allowing management teams to visualize the volume, types, and sentiment of complaints over time.

Continuous learning ensures that Text Analytics models remain accurate and relevant as new complaint topics emerge, adapting to changing language, slang, or terminology. Automated workflows can be triggered to route high-priority complaints to specialized teams, improving response times, reducing customer frustration, and enhancing overall satisfaction. The insights generated from analyzing customer complaints can inform process improvements, product updates, and service enhancements, enabling banks to proactively address issues and improve operational efficiency.

By leveraging Text Analytics, banks gain a scalable, automated solution to monitor, analyze, and act on unstructured customer feedback. This approach provides a comprehensive understanding of customer sentiment, uncovers emerging patterns in complaints, supports data-driven decision-making, and ensures that critical issues are addressed promptly. Text Analytics ultimately enhances customer experience, strengthens brand loyalty, and contributes to more efficient and responsive banking operations.

Question 95:

A logistics company wants to detect congestion and unusual activity at warehouses using 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 can detect and classify objects in images and video frames. Monitoring warehouse activity allows identification of congestion, misplaced goods, or safety hazards.

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

Custom Vision models can be deployed at the edge or in the cloud, providing real-time alerts. Confidence scores allow prioritization of critical events. Analytics dashboards track warehouse activity trends, enabling operational improvements, optimized layouts, and safety monitoring. Continuous model learning ensures accuracy in changing conditions.

Question 96:

A retailer wants to analyze product images to detect defects before shipment. 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 enables object detection and classification. Retailers can detect scratches, missing parts, or misalignments in products before shipping.

Computer Vision OCR (Option B) extracts text, not defects. Text Analytics (Option C) analyzes text. Form Recognizer (Option D) extracts structured data.

Custom Vision supports real-time analysis, confidence scoring, and continuous learning. Integration with quality control systems allows automated alerts, trend monitoring, and operational efficiency improvements. Dashboards provide insights into defect patterns, production bottlenecks, and corrective measures.

A retailer that wants to analyze product images to detect defects before shipment should use Custom Vision. Custom Vision is an Azure AI service designed for object detection and image classification, making it well-suited for quality control applications in retail and manufacturing. By training Custom Vision models on images of products, retailers can automatically identify defects such as scratches, dents, missing components, misalignments, or other irregularities. Detecting these defects before products are shipped ensures higher customer satisfaction, reduces returns, and minimizes the costs associated with defective merchandise reaching the market. Computer Vision OCR, in contrast, is designed to extract text from images and cannot detect visual defects. Text Analytics focuses on analyzing text, which is not applicable to visual inspection tasks. Form Recognizer is used for extracting structured data from documents and forms, making it unsuitable for detecting physical product defects.

Custom Vision supports real-time analysis, allowing images captured during production or packaging to be immediately assessed. The service provides confidence scoring for predictions, enabling quality control teams to prioritize items that are more likely to be defective. Continuous learning is a key feature, allowing models to improve over time as more images are labeled and new types of defects are encountered. This ensures that the system adapts to changing product designs, variations in materials, and production line updates.

Integration with quality control systems enables automated alerts when defects are detected, helping staff take immediate corrective action. Dashboards can visualize defect trends, production bottlenecks, and recurring issues, providing actionable insights for operations management. By analyzing defect patterns over time, companies can identify systemic production issues and implement preventive measures to improve product quality.

Using Custom Vision in this way enhances operational efficiency by reducing manual inspection, improving consistency in defect detection, and ensuring that only high-quality products are shipped to customers. It supports scalable inspection processes across multiple production lines and locations, delivering both cost savings and improved brand reputation. Overall, Custom Vision provides a reliable, automated solution for visual quality control, making it the ideal Azure AI service for analyzing product images to detect defects before shipment.

Question 97:

A company wants to translate customer reviews from multiple languages and analyze sentiment to improve products. Which Azure AI service combination 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 converts reviews into a common language, while Text Analytics analyzes sentiment, key phrases, and entities. This combination ensures consistent understanding across multilingual feedback.

Custom Vision and Form Recognizer (Option B) process images and documents. QnA Maker and Anomaly Detector (Option C) provide knowledge base responses and numeric anomaly detection. Computer Vision OCR and Custom Vision (Option D) process images.

Integration with dashboards enables tracking of customer satisfaction, product trends, and emerging issues. Continuous learning improves translation quality and sentiment detection, ensuring actionable insights.

Question 98:

A financial institution wants to extract key insights from quarterly earnings reports, including revenue, expenses, and net profit. 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 data from complex documents like earnings reports. It identifies tables, key-value pairs, and numeric metrics, enabling automated analytics and reporting.

Custom Vision (Option B) detects objects in images. Text Analytics (Option C) analyzes unstructured text. QnA Maker (Option D) creates knowledge bases.

Form Recognizer supports diverse layouts and nested tables, integrating with BI tools for real-time dashboards. Continuous model improvement ensures adaptability to new report formats. Automated extraction reduces manual effort, accelerates decision-making, and enhances financial planning.

Question 99:

A company wants to automatically process handwritten feedback forms and extract key suggestions and complaints. Which Azure AI service should they use?

Answer:

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

Explanation:

Computer Vision OCR is the correct answer because it can extract handwritten text from scanned feedback forms. The extracted text can then be further processed for analysis.

Custom Vision (Option B) detects objects in images. Form Recognizer (Option C) works best with structured forms. Text Analytics (Option D) analyzes text but requires input from OCR for handwritten content.

OCR supports diverse handwriting styles, enabling large-scale automated processing. Integration with analytics dashboards allows detection of recurring complaints, trends, and improvement opportunities. Continuous learning improves recognition accuracy, reducing manual intervention and enhancing operational efficiency.

Question 100:

A company wants to build a chatbot that answers customer product queries and continuously improves by learning from interactions. 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 builds a knowledge base from FAQs, manuals, and documents. Chatbots use this knowledge base to answer customer queries and can be updated continuously based on logged unanswered questions or feedback.

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

Integration with Azure Bot Service enables deployment across websites, mobile apps, and messaging platforms. Multi-turn conversations allow follow-ups, and analytics dashboards track chatbot performance. Continuous learning ensures accurate, relevant, and effective automated customer service over time.