Microsoft AI-900 Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 3 Q41-60

Visit here for our full Microsoft AI-900 exam dumps and practice test questions.

Question 41:

A global e-commerce company wants to analyze customer feedback in multiple languages to understand sentiment trends and improve product recommendations. 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:

The correct answer is A) Translator Text and Text Analytics because this combination allows organizations to process multilingual customer feedback efficiently and perform sentiment analysis at scale. Translator Text converts content from various languages into a target language, such as English, ensuring that sentiment analysis is consistent and comparable across regions. Text Analytics then performs advanced AI-driven analysis on the unified text, identifying sentiment, extracting key phrases, detecting language, and categorizing topics. This dual approach is particularly useful for global organizations that receive customer feedback in multiple languages and want to extract actionable insights to inform marketing, product development, and customer support strategies.

Translator Text offers real-time translation and batch processing, allowing high-volume content to be analyzed automatically. It supports over 100 languages and uses neural machine translation for accuracy and context preservation, which is critical when analyzing nuanced customer sentiment. Text Analytics complements this by identifying positive, negative, and neutral sentiment, highlighting emerging trends, and enabling organizations to focus on areas of concern proactively.

By integrating Translator Text and Text Analytics, businesses can generate multilingual dashboards, track sentiment trends over time, and segment feedback by product, region, or customer demographics. This combination also allows for automated workflows; for instance, negative feedback in any language can trigger notifications to customer service teams, while positive feedback can be used for marketing campaigns or product reviews. Additionally, Text Analytics can extract key phrases and entities such as product names, locations, or dates, enabling a deeper understanding of customer concerns and preferences.

The solution ensures that all text-based feedback, regardless of language, can be analyzed uniformly, reducing manual effort and errors associated with human translation. It also enhances scalability, as the system can process thousands of messages daily without the need for additional staff. The data can be visualized using tools like Power BI, providing executives and analysts with clear insights into customer sentiment trends and product performance.

In a global context, organizations can leverage this solution to detect regional trends, identify cultural nuances in sentiment, and adapt marketing and support strategies accordingly. For example, if a new product is well-received in one region but generates negative feedback in another, management can investigate and address the issues promptly. This approach also supports proactive decision-making; predictive analytics can be applied to sentiment trends to anticipate product issues or shifts in consumer preferences.

Furthermore, integrating Translator Text and Text Analytics with other Azure services such as Logic Apps or Azure Functions allows automated routing of feedback, workflow triggers, and reporting. For example, sentiment analysis results can trigger customer outreach for highly negative reviews or escalate urgent issues for immediate attention. The AI models used are continuously improving, meaning translation accuracy and sentiment detection become more precise over time as the system processes more data.

Overall, the combination of Translator Text and Text Analytics provides a robust, scalable, and automated solution for understanding global customer sentiment, enabling companies to enhance customer satisfaction, optimize product offerings, and maintain a competitive advantage in international markets. This approach exemplifies the use of Azure AI services to transform unstructured, multilingual data into actionable insights that drive informed business decisions and operational excellence.

Question 42:

A healthcare provider wants to extract information from patient forms, including structured data such as patient names, dates of birth, medical history, and lab results. They also want the system to handle different formats of forms. 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 is specifically designed to extract structured and semi-structured data from forms and documents automatically. Healthcare organizations deal with a wide variety of forms, including patient intake forms, lab results, insurance claims, and medical questionnaires. Each of these documents can have a different format, layout, or handwriting style, making manual data entry labor-intensive, error-prone, and inefficient. Form Recognizer uses AI-powered machine learning models to identify key-value pairs, tables, checkboxes, and other data fields in these forms, converting them into structured, machine-readable data that can be integrated into electronic health records (EHRs), databases, or workflow systems.

Custom Vision (Option B) is focused on image classification and object detection but does not extract textual information from forms. Text Analytics (Option C) analyzes unstructured text for sentiment, key phrases, or entity extraction but requires the text to be in a digital format; it does not work on scanned forms or handwritten notes directly. QnA Maker (Option D) is designed to build conversational knowledge bases and answer questions but cannot process or extract data from forms.

Form Recognizer supports both prebuilt and custom models. Prebuilt models, such as those for invoices or receipts, can handle common formats out-of-the-box, while custom models can be trained using a small set of labeled examples to extract data from specialized forms, such as patient intake forms unique to a hospital. The service also supports handwriting recognition, enabling the extraction of medical notes or lab results written manually. By using Form Recognizer, healthcare organizations can drastically reduce the time required to process documents, minimize transcription errors, and ensure more accurate patient records.

Integration with Azure Logic Apps, Power Automate, or custom APIs allows organizations to automate end-to-end workflows. For example, extracted data can automatically populate a patient management system, trigger insurance claims processing, or alert medical staff if specific health conditions are detected. Security and compliance are also critical; Form Recognizer operates within the secure Azure environment and supports HIPAA-compliant configurations, ensuring sensitive patient data is handled appropriately.

Additionally, Form Recognizer provides confidence scores for extracted data, allowing human operators to review low-confidence fields and ensure data quality. This hybrid approach, combining AI automation with human validation, enhances accuracy while still improving efficiency. Hospitals and clinics can analyze trends from structured data, such as average patient visit duration, common conditions, or lab result patterns, using Power BI or other analytics tools. Over time, as more forms are processed, the AI models improve, becoming more accurate in handling variations in form layouts, handwriting styles, and document quality.

Overall, Form Recognizer enables healthcare providers to transform paper-based processes into efficient digital workflows, improve patient care through accurate data capture, reduce administrative overhead, and maintain compliance with regulatory requirements. Its ability to handle diverse form formats, recognize handwriting, and integrate with automated workflows makes it an indispensable tool for modern healthcare organizations aiming to leverage AI for operational excellence and data-driven decision-making.

Question 43:

A logistics company wants to track and analyze handwritten delivery notes to automate shipment updates in their system. 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 both handwritten and printed text from images or scanned documents. In logistics, handwritten delivery notes often contain critical information such as recipient names, addresses, delivery instructions, package identifiers, and dates. Processing these notes manually is time-consuming, prone to human error, and inefficient. Using Computer Vision OCR, organizations can convert handwritten and printed text into machine-readable formats, enabling automated workflows that update shipment tracking systems in real time.

Custom Vision (Option B) is intended for object detection and image classification, not text extraction. Form Recognizer (Option C) works best with structured forms but is less effective for freeform handwriting on unstructured notes. Text Analytics (Option D) can analyze text but cannot process images or handwritten content directly.

Computer Vision OCR leverages advanced optical character recognition algorithms to recognize various handwriting styles, even in noisy or poorly scanned images. It processes character shapes, spacing, and context to ensure high accuracy. Once the text is digitized, the logistics system can automatically extract key entities like addresses, recipient names, and package IDs, eliminating manual data entry. This integration allows for faster shipment processing, reduces errors in tracking, and improves customer satisfaction by providing accurate and timely updates.

Organizations can also combine OCR outputs with other Azure services such as Logic Apps, Power Automate, or Text Analytics to analyze patterns in delivery instructions, detect recurring issues, or monitor efficiency. For instance, the system could flag incomplete or ambiguous delivery instructions for human review or track common issues across multiple delivery routes to optimize operations. The AI system can continuously improve as more data is processed, learning new handwriting styles or variations in note formats.

By using Computer Vision OCR, logistics companies not only save time and reduce costs but also gain actionable insights from previously unstructured data. The service supports a wide range of image qualities, including smartphone photos, scanned documents, and faxed notes, making it highly versatile for field operations. Additionally, integrating the digitized data into dashboards or analytics platforms enables management to track performance metrics, delivery times, and exception patterns more effectively.

In summary, Computer Vision OCR transforms handwritten delivery notes into digital, actionable information, enabling automated workflows, better operational efficiency, and improved customer service. It is a critical tool for logistics organizations aiming to leverage AI for end-to-end process automation and data-driven decision-making.

Question 44:

A company wants to create an AI-powered customer support chatbot that can understand natural language queries and provide answers from its internal knowledge base. 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 allows organizations to create a knowledge base from existing FAQs, manuals, documents, and web pages and connect it to a conversational interface through a chatbot. By leveraging QnA Maker, the company can provide instant responses to customer inquiries without requiring human agents for every interaction. Custom Vision (Option B) is focused on image classification and object detection, which does not apply to text-based queries. Anomaly Detector (Option C) monitors numeric data for deviations and is unrelated to customer support. Computer Vision (Option D) processes images but does not understand text-based queries.

QnA Maker can automatically extract question-and-answer pairs from documents, providing a foundation for the chatbot to respond accurately. It also supports multi-turn conversations, enabling the chatbot to handle follow-up questions and clarify user intent. By integrating with Azure Bot Service, the chatbot can be deployed across multiple platforms, including websites, mobile apps, and messaging platforms such as Microsoft Teams or WhatsApp.

One key advantage of QnA Maker is its continuous learning capability. The system logs unmatched questions, allowing administrators to review and update the knowledge base. Over time, this improves the chatbot’s accuracy and ensures it can handle evolving customer inquiries. Combined with analytics dashboards, QnA Maker provides insights into common questions, user behavior, and knowledge gaps, enabling proactive content updates.

From an operational perspective, implementing a QnA Maker-powered chatbot reduces the workload on human support teams, speeds up response times, and enhances customer satisfaction. It also allows businesses to scale customer support without significant additional resources. By integrating with other Azure AI services, such as Text Analytics for sentiment analysis or Translator Text for multilingual support, organizations can further enhance the chatbot’s capabilities.

Overall, QnA Maker offers a robust, scalable, and flexible solution for automating customer support, improving response quality, and generating actionable insights from user interactions, making it an essential tool for companies aiming to leverage AI in customer service operations.

Question 45:

A manufacturing company wants to detect defects in products on a production line using images captured by cameras. 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 enables organizations to train AI models to detect and classify objects in images. In a manufacturing environment, cameras can capture images of products on the production line, and Custom Vision can identify defects such as scratches, misalignments, missing parts, or color inconsistencies. This automation improves quality control efficiency, reduces human error, and minimizes the risk of defective products reaching customers.

Computer Vision OCR (Option B) extracts text from images but cannot identify defects. Text Analytics (Option C) analyzes text data, which is irrelevant for visual inspection. Anomaly Detector (Option D) monitors numeric patterns and is not suitable for image analysis.

Custom Vision allows training models using a relatively small set of labeled images. These images can represent both normal and defective products, enabling the AI model to learn patterns associated with defects. Once trained, the model can be deployed to the production line for real-time defect detection. Integration with IoT devices and Azure Edge services allows AI models to run locally on the factory floor, ensuring minimal latency and immediate feedback.

The system can also generate analytics and reporting dashboards, providing insights into defect frequency, types, and potential causes. This allows management to optimize production processes, reduce waste, and improve overall product quality. Over time, as more images are collected and labeled, the model can be retrained to improve accuracy and detect new types of defects.

Implementing Custom Vision in manufacturing enhances operational efficiency, ensures consistent quality, and reduces costs associated with manual inspection. It also supports predictive maintenance by detecting anomalies in equipment operation through visual cues, complementing numeric anomaly detection systems. Overall, Custom Vision provides a comprehensive AI-powered solution for automated visual inspection and quality assurance in manufacturing environments.

Question 46:

A bank wants to analyze customer emails to determine whether they contain urgent requests, complaints, or general inquiries, and route them to the appropriate department automatically. Which Azure AI service should they use?

Answer:

A) Language Understanding (LUIS)
B) Custom Vision
C) Form Recognizer
D) Anomaly Detector

Explanation:

Language Understanding (LUIS) is the correct answer because it allows organizations to extract user intent and key entities from unstructured text. In this scenario, customer emails may contain requests ranging from urgent account issues to general questions. LUIS can be trained to classify these intents accurately, such as “urgent request,” “billing inquiry,” “technical issue,” or “complaint.” Once the intent is identified, the system can automatically route the email to the correct department, ensuring faster resolution and improved customer satisfaction.

Custom Vision (Option B) is designed for image classification and object detection, which is not applicable for analyzing email text. Form Recognizer (Option C) extracts structured data from forms and documents but cannot understand intent in unstructured emails. Anomaly Detector (Option D) identifies unusual numeric patterns and cannot interpret natural language.

LUIS allows developers to create domain-specific models by defining intents and entities relevant to the organization. It also supports prebuilt entities, such as dates, numbers, and email addresses, enabling the extraction of key information that can be used to automate workflows. Integration with Azure Logic Apps or Power Automate allows these automated decisions to be executed seamlessly, such as creating support tickets, sending notifications to teams, or triggering alerts for urgent issues.

Moreover, LUIS provides multi-turn conversation capabilities, which is particularly useful if emails contain follow-up questions or require context-based interpretation. Over time, LUIS models can be retrained using real customer email data to improve accuracy, ensuring the system adapts to evolving customer language patterns and organizational needs.

Using LUIS for email routing improves operational efficiency by reducing manual sorting, ensuring critical issues are addressed promptly, and freeing up human agents for more complex tasks. It also provides analytics on customer communications, helping management identify recurring problems, measure response times, and evaluate customer satisfaction trends.

In highly regulated industries like banking, accurate email handling and classification are crucial for compliance, risk management, and customer service excellence. LUIS provides a scalable, AI-driven solution that ensures consistency, reliability, and efficiency in processing large volumes of unstructured customer communications. By leveraging LUIS, banks can automate the interpretation of emails, deliver faster resolutions, and optimize operational workflows while maintaining high-quality customer interactions.

Question 47:

A retail company wants to analyze images uploaded by customers to identify product types and 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 organizations to train AI models for image classification and object detection. In a retail context, customers may upload photos of products they own or are interested in. Custom Vision can classify these products, identify features, and tag images with relevant labels that enable personalized recommendations.

Computer Vision OCR (Option B) is designed to extract text from images and cannot identify objects or product types. Text Analytics (Option C) analyzes text and does not process visual content. Anomaly Detector (Option D) detects unusual numeric patterns, which is unrelated to image classification.

Custom Vision supports both classification and object detection, enabling the AI model to not only categorize entire images but also locate and identify multiple products within a single image. Retailers can train models with images of their product catalog to ensure accurate identification and improve recommendation algorithms.

Integration with recommendation engines allows real-time suggestions based on detected products. For example, if a customer uploads an image of a shoe, the system can recommend similar styles, matching accessories, or complementary products. Over time, the model can be retrained with new customer-uploaded images to improve accuracy and adapt to changing trends.

Custom Vision models can also be deployed on the edge for real-time inference in stores or mobile apps, providing immediate recommendations without relying on cloud connectivity. By leveraging this AI solution, retailers can enhance customer engagement, increase cross-selling opportunities, and improve overall shopping experiences.

The service provides confidence scores for each prediction, allowing businesses to filter recommendations or escalate uncertain cases for manual review. Detailed analytics on image submissions can reveal popular products, emerging trends, and customer preferences, providing strategic insights to marketing and merchandising teams.

In summary, Custom Vision empowers retailers to transform customer-uploaded images into actionable insights, enabling personalized product recommendations, improving customer satisfaction, and supporting data-driven marketing strategies. This approach leverages AI for scalable, accurate, and context-aware visual product recognition.

Question 48:

A financial organization wants to detect unusual patterns in transactions to prevent fraud. Which Azure AI service should they use?

Answer:

A) Anomaly Detector
B) Text Analytics
C) Custom Vision
D) QnA Maker

Explanation:

Anomaly Detector is the correct answer because it analyzes time-series data to identify patterns that deviate from expected behavior. In financial applications, transactions often follow predictable patterns based on user behavior, time of day, transaction amounts, and account types. Anomaly Detector leverages machine learning algorithms to detect deviations that may indicate fraudulent activity.

Text Analytics (Option B) processes text data, which is not applicable for analyzing numeric financial transactions. Custom Vision (Option C) is used for image classification and object detection and cannot process numeric patterns. QnA Maker (Option D) builds knowledge bases and conversational bots, which are unrelated to fraud detection.

Anomaly Detector can process large volumes of transaction data in real time and assign confidence scores to potential anomalies, enabling timely alerts. Organizations can integrate the service with workflow automation tools such as Azure Logic Apps or Power Automate to trigger notifications, freeze suspicious accounts, or initiate investigations.

The service also supports seasonal trend detection, ensuring that normal variations in transactions (e.g., holiday spending spikes) are not flagged as anomalies. It can detect anomalies in single or multiple time series, allowing a holistic view of user behavior, account activity, or system performance.

Over time, Anomaly Detector improves as more transaction data is processed, reducing false positives and increasing detection accuracy. By combining anomaly detection with other AI techniques, such as predictive modeling or entity recognition from transaction descriptions, financial institutions can build robust fraud prevention systems.

Using Anomaly Detector in finance enhances operational efficiency, mitigates risk, ensures regulatory compliance, and protects customers from fraud. The AI-powered approach allows institutions to scale fraud detection efforts without proportionally increasing human oversight, enabling proactive monitoring and rapid response to suspicious activities.

Question 49:

A hospital wants to automatically identify and redact personally identifiable information (PII) in patient documents before sharing them for research purposes. Which Azure AI service should they use?

Answer:

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

Explanation:

Text Analytics for PII (Personally Identifiable Information) is the correct answer because it is specifically designed to detect and redact sensitive information such as names, addresses, phone numbers, social security numbers, and other identifiers. Hospitals and research organizations often need to share medical data for research, analytics, or reporting purposes while ensuring compliance with regulations such as HIPAA. Manually redacting PII is error-prone, time-consuming, and inconsistent. Text Analytics for PII automates this process, enabling scalable, accurate, and consistent redaction across large volumes of documents.

Form Recognizer (Option B) extracts structured data from forms but does not focus on automatically identifying PII for redaction purposes. Custom Vision (Option C) works with images to classify objects or detect anomalies, not text-based PII. Anomaly Detector (Option D) is designed to identify unusual numeric patterns and cannot identify sensitive information in text.

Text Analytics for PII can process unstructured text from a variety of sources, including scanned documents (after OCR conversion), emails, reports, and chat logs. The service identifies sensitive entities, provides confidence scores, and allows organizations to automatically redact or mask them. By integrating this service with workflows in Azure Logic Apps or Power Automate, hospitals can ensure that documents are automatically sanitized before they leave secure systems.

In addition to compliance, PII detection supports operational efficiency. Hospitals can share anonymized datasets with researchers to accelerate medical studies without compromising patient privacy. The AI model continuously improves as more data is processed, increasing accuracy in identifying diverse forms of PII, including cultural variations in names or unusual formats for identifiers.

Text Analytics for PII also enables detailed reporting and audit logs. Organizations can track which documents were processed, what PII was detected, and how it was handled. This level of transparency is essential for regulatory compliance and for demonstrating adherence to privacy standards during audits.

By leveraging AI for PII detection, hospitals not only protect patient privacy but also enable secure data sharing, faster research initiatives, and improved operational efficiency. The service ensures consistent redaction, reduces human errors, and scales to process thousands of documents automatically. Overall, Text Analytics for PII is critical for healthcare organizations that need to balance data utility with stringent privacy requirements.

Question 50:

A retailer wants to extract key information from receipts uploaded by customers, including total amounts, items purchased, and purchase dates. 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 designed to extract structured and semi-structured data from forms, receipts, invoices, and similar documents. Retailers receive receipts in various formats and layouts, making manual data extraction inefficient and error-prone. Form Recognizer uses prebuilt models for receipts to identify key fields such as merchant name, total amount, purchase date, tax, and line items.

Custom Vision (Option B) focuses on image classification and object detection and cannot extract structured text. Text Analytics (Option C) analyzes unstructured text but requires digital text input, which is not always available in scanned or photographed receipts. Computer Vision OCR (Option D) can extract text from images but does not parse or structure the extracted information into usable data fields.

Form Recognizer supports training custom models for specific receipt layouts, enabling higher accuracy when working with receipts from particular vendors or regions. It can also handle multiple languages and currency formats. Once extracted, data can be automatically integrated into accounting systems, customer reward programs, or analytics dashboards.

This automation streamlines expense tracking, customer loyalty processing, and financial reporting. Retailers can also analyze aggregated receipt data to gain insights into purchasing trends, popular items, seasonal variations, and regional sales patterns. Integration with workflow automation tools such as Azure Logic Apps or Power Automate allows data to be validated, stored, or used for triggering promotions or notifications to customers.

By leveraging Form Recognizer, retailers reduce manual data entry, improve operational efficiency, and gain actionable business insights from receipt data at scale. The system’s AI models continuously improve as more receipts are processed, enhancing accuracy and adapting to changes in receipt formats or layout variations. This solution also supports regulatory compliance by maintaining accurate records of transactions and enabling traceability for auditing purposes.

Question 51:

A customer support team wants to analyze live audio calls to identify topics discussed and measure customer sentiment. Which combination of Azure AI services should they use?

Answer:

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

Explanation:

Speech Service combined with Text Analytics is the correct answer because Speech Service transcribes live or recorded audio into text, and Text Analytics can then analyze the transcribed text for sentiment, key phrases, and topics. Customer support teams handle large volumes of calls daily, and manual analysis is not scalable. By leveraging this combination, organizations can automatically extract insights from audio interactions, identify recurring issues, and assess customer satisfaction.

Form Recognizer and Custom Vision (Option B) are unrelated because they focus on document and image processing. Anomaly Detector and QnA Maker (Option C) are designed for detecting numeric anomalies and building knowledge bases, respectively, and cannot analyze audio content. Computer Vision OCR and Custom Vision (Option D) process images but not audio or text.

Speech Service provides real-time transcription with high accuracy and supports multiple languages. The transcribed text can be further enriched with speaker identification, punctuation, and timestamps, allowing precise analysis of conversations. Text Analytics then applies sentiment analysis to determine whether the conversation is positive, negative, or neutral. Named entity recognition extracts relevant information such as product names, locations, and dates, which can be used to improve workflows or trigger automated responses.

Integration with dashboards enables supervisors to monitor trends, identify frequently raised concerns, and evaluate the performance of support agents. Sentiment trends can also be correlated with product launches, marketing campaigns, or service issues, allowing proactive measures to improve customer experience. The AI system can learn over time, improving transcription accuracy for industry-specific terms and detecting emerging issues.

Using Speech Service and Text Analytics together provides a scalable, automated, and data-driven approach to analyzing audio interactions. It reduces manual effort, enables real-time monitoring, and provides actionable insights to improve customer service quality and operational efficiency. Organizations can also use these insights to train agents, optimize support scripts, and ensure high levels of customer satisfaction.

Question 52:

A company wants to translate customer feedback from multiple languages into a single language for sentiment analysis. 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 enables organizations to convert text from multiple languages into a target language, and Text Analytics performs sentiment analysis, key phrase extraction, and entity recognition on the unified text. This combination ensures that multilingual feedback can be consistently analyzed and compared across markets.

Custom Vision and Form Recognizer (Option B) focus on image and document analysis, which is irrelevant for textual feedback. QnA Maker and Anomaly Detector (Option C) are used for knowledge bases and numeric anomaly detection, not text translation or sentiment analysis. Computer Vision OCR and Custom Vision (Option D) process images but do not provide sentiment insights or translations.

Translator Text uses neural machine translation, preserving context and nuance in text, which is essential for accurately interpreting sentiment. Text Analytics then identifies whether feedback is positive, negative, or neutral and extracts critical insights such as mentioned products, locations, or features. Organizations can integrate these insights with dashboards and analytics tools, enabling real-time tracking of customer sentiment across regions and languages.

This AI-driven workflow reduces manual translation and analysis efforts, increases scalability, and ensures consistent interpretation of global feedback. Businesses can identify emerging trends, detect customer dissatisfaction early, and improve product offerings or marketing strategies. The system can continuously learn from new data, improving translation quality and sentiment detection over time, ensuring actionable insights remain accurate and relevant.

By leveraging Translator Text and Text Analytics together, organizations gain a unified, automated solution to analyze global customer sentiment, improve decision-making, and enhance customer experience at scale.

Question 53:

A company wants to extract key phrases, entities, and sentiment from unstructured text such as product reviews, social media posts, and emails. 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 is specifically designed to analyze unstructured text and extract actionable insights. In this scenario, product reviews, social media posts, and emails are rich sources of information but are often messy and unstructured. Text Analytics applies natural language processing (NLP) to identify key phrases, extract named entities such as product names, locations, or dates, and determine the sentiment of the text, whether positive, negative, or neutral.

Custom Vision (Option B) handles images, which is irrelevant here. Form Recognizer (Option C) extracts structured data from forms, not free-form text. QnA Maker (Option D) is used to build conversational knowledge bases and answer questions but does not perform in-depth text analysis.

Text Analytics supports multiple languages and can process large volumes of text quickly, making it suitable for global companies collecting feedback from diverse regions. The sentiment analysis capability helps organizations understand overall customer perception and identify areas requiring improvement. Key phrase extraction allows companies to detect trends in topics being discussed, such as product features, service quality, or pricing concerns. Entity recognition provides insights into which products, brands, or competitors are mentioned, enabling competitive analysis and targeted improvements.

Integration with dashboards such as Power BI or custom analytics solutions allows organizations to visualize trends, track changes in customer sentiment over time, and correlate sentiment with business metrics such as sales or support tickets. Text Analytics can also be combined with other Azure services like Translator Text for multilingual analysis, Logic Apps for automated workflow triggers, or QnA Maker for automated customer responses.

Over time, the models improve as more text is processed, reducing errors and increasing accuracy in detecting sentiment, key phrases, and entities. Organizations can leverage this to proactively address negative sentiment, identify emerging customer needs, and improve products and services. For example, if a spike in negative sentiment is detected for a specific product feature, the company can investigate and implement corrective actions, preventing broader dissatisfaction.

Text Analytics also supports customization, allowing organizations to define domain-specific entities and key phrases, which is crucial for industries with specialized terminology, such as healthcare, finance, or technology. This ensures that insights are relevant, precise, and actionable.

In summary, Text Analytics provides a comprehensive, scalable, and automated solution for analyzing unstructured text, enabling organizations to extract insights, improve customer satisfaction, and make informed business decisions. Its ability to process multiple sources and languages, combined with actionable sentiment and entity data, makes it an essential tool for modern, data-driven enterprises.

Question 54:

A company wants to detect and classify defects in products on a production line using images captured by high-speed cameras. 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 enables organizations to train AI models to detect specific objects and classify them accurately. In manufacturing, high-speed cameras capture images of products on a production line, and Custom Vision can identify defects such as scratches, misalignments, or missing components. Automating defect detection increases efficiency, reduces waste, and ensures consistent product quality.

Computer Vision OCR (Option B) extracts text from images and cannot identify visual defects. Text Analytics (Option C) analyzes text data, which is irrelevant in this scenario. Anomaly Detector (Option D) identifies numeric deviations but is not suitable for image-based quality control.

Custom Vision allows training models using labeled images, where each image is annotated with the presence or absence of defects. This enables the AI system to learn visual patterns and recognize anomalies. Once trained, models can be deployed to edge devices for real-time inference, ensuring immediate feedback on production quality without latency issues.

Integration with IoT sensors and monitoring systems allows defective products to be flagged instantly for removal or review. Additionally, Custom Vision can be retrained over time as new defect patterns emerge, ensuring continuous improvement in detection accuracy.

Manufacturers benefit from reduced labor costs, faster production, higher throughput, and improved quality assurance. Dashboards and analytics generated from defect detection provide insights into production trends, recurring issues, and process improvements. Over time, the AI system enhances operational efficiency, reduces defects reaching customers, and supports proactive maintenance strategies by detecting early signs of equipment wear or process deviations.

In summary, Custom Vision offers a scalable, precise, and adaptable AI solution for automated defect detection in manufacturing, enabling higher quality, operational efficiency, and cost reduction while supporting real-time quality monitoring.

Question 55:

A healthcare organization wants to analyze doctors’ handwritten notes and convert them into searchable digital text for patient records. Which Azure AI service should they use?

Answer:

A) Computer Vision OCR
B) Custom Vision
C) Text Analytics
D) QnA Maker

Explanation:

Computer Vision OCR is the correct answer because it is designed to extract text from both printed and handwritten documents. Handwritten doctors’ notes often contain vital information such as patient symptoms, diagnoses, medications, and treatment plans. Manually transcribing these notes is time-consuming, error-prone, and inefficient. Computer Vision OCR automates this process, converting handwritten text into machine-readable digital formats that can be integrated into electronic health records (EHRs).

Custom Vision (Option B) focuses on image classification and object detection, not text extraction. Text Analytics (Option C) analyzes digital text but cannot process handwritten notes directly. QnA Maker (Option D) builds knowledge bases but does not extract or analyze handwritten content.

Computer Vision OCR supports a variety of handwriting styles, recognizing different character shapes, spacing, and noisy input. The output is machine-readable text that can be searched, indexed, and analyzed for downstream applications, such as analytics dashboards, decision support systems, or compliance reporting.

Integration with other Azure AI services allows healthcare organizations to perform additional processing on the transcribed notes. For instance, Text Analytics can detect entities such as medications, patient names, or medical conditions, and perform sentiment analysis or risk scoring. Form Recognizer can extract structured data from forms if notes are combined with other patient forms.

Digitizing handwritten notes improves patient care by making historical records searchable, reducing delays in accessing information, and minimizing errors caused by misreading handwriting. The system can also support regulatory compliance by maintaining accurate records and ensuring secure handling of sensitive information.

Over time, Computer Vision OCR models improve through exposure to diverse handwriting styles and medical terminology, enhancing accuracy and efficiency. Hospitals can automate workflows, streamline documentation, and enable data-driven decision-making. This AI solution significantly reduces administrative burdens on healthcare professionals, allowing them to focus more on patient care.

In summary, Computer Vision OCR is essential for converting handwritten medical notes into searchable, structured digital text. It enhances operational efficiency, patient safety, and regulatory compliance while enabling advanced analytics and AI-driven insights in healthcare.

Question 56:

A bank wants to automatically analyze financial reports to extract key metrics, such as revenue, expenses, and net income. 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 can extract structured and semi-structured data from documents, including tables, key-value pairs, and other relevant fields. Financial reports often contain tabular data with varying formats, such as revenue, expenses, net income, and balance sheets. Manual extraction is time-consuming and prone to error. Form Recognizer automates this process, transforming document data into structured formats suitable for analysis, reporting, and decision-making.

Custom Vision (Option B) handles image classification and object detection, which is irrelevant here. Text Analytics (Option C) analyzes unstructured text but is not designed for structured numeric data extraction. QnA Maker (Option D) builds knowledge bases, which is unrelated to extracting metrics from financial reports.

Form Recognizer supports prebuilt models for invoices and receipts and allows the creation of custom models tailored to the company’s specific financial report formats. Once extracted, the data can feed dashboards, BI tools like Power BI, or automated workflows for regulatory compliance, reporting, or trend analysis.

By leveraging Form Recognizer, banks reduce manual labor, improve accuracy, and enable faster insights into financial performance. Over time, the system adapts to variations in report formats and layout changes, ensuring consistent data extraction. Automated integration with analytics platforms enables trend detection, anomaly identification, and predictive forecasting.

This solution empowers financial analysts to focus on decision-making rather than repetitive data entry, improves operational efficiency, and ensures compliance with reporting standards. Overall, Form Recognizer provides a robust, scalable solution for extracting critical financial metrics accurately and efficiently.

Question 57:

A company wants to provide a chatbot that can answer customer questions based on a knowledge base and improve over time as new questions are asked. 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 allows organizations to create a knowledge base from FAQs, documents, and manuals, which a chatbot can then use to respond to customer queries. The service supports continuous learning, logging unmatched questions, and enabling administrators to update the knowledge base over time.

Custom Vision (Option B) handles image classification, not text-based queries. Anomaly Detector (Option C) analyzes numeric patterns, which is unrelated. Computer Vision (Option D) processes images and text from images, but not interactive question-answer workflows.

QnA Maker can be integrated with Azure Bot Service to deploy chatbots on websites, mobile apps, or messaging platforms. Multi-turn conversation capability allows the chatbot to handle follow-up questions or clarify user intent. Analytics on user interactions helps organizations identify knowledge gaps, improve content quality, and track engagement.

This AI-driven approach reduces support costs, improves response times, and enhances customer satisfaction. The system scales to handle large volumes of queries, making it suitable for organizations of any size. Over time, QnA Maker evolves with the business, ensuring the chatbot remains accurate, relevant, and effective.

Question 58:

A company wants to detect objects such as cars and pedestrians in traffic camera footage to monitor city traffic flow. Which Azure AI service should they use?

Answer:

A) Custom Vision
B) Computer Vision OCR
C) Text Analytics
D) QnA Maker

Explanation:

Custom Vision is the correct answer because it allows object detection in images or video frames. Traffic cameras capture high volumes of footage, and Custom Vision can be trained to identify vehicles, pedestrians, and other objects to monitor traffic flow. This automation improves traffic management, reduces congestion, and enhances public safety.

Computer Vision OCR (Option B) extracts text from images, which is not applicable. Text Analytics (Option C) analyzes text data, not images. QnA Maker (Option D) builds knowledge bases for conversational agents, not object detection.

Custom Vision supports training models on labeled datasets and can detect multiple object types in real time. Integration with analytics dashboards provides insights into traffic patterns, peak hours, or incidents. Models can also be deployed on edge devices for low-latency inference.

This approach allows city planners to make data-driven decisions, optimize traffic lights, and enhance urban mobility. Real-time object detection reduces the need for manual monitoring and supports intelligent city initiatives.

Question 59:

A company wants to convert scanned documents into machine-readable text for indexing and search purposes. 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 text from images and scanned documents, converting them into machine-readable formats. This enables indexing, search, and downstream analytics. Custom Vision (Option B) identifies objects, not text. Text Analytics (Option C) analyzes existing text but cannot extract it from images. Form Recognizer (Option D) works best with structured forms but is less effective for freeform documents.

OCR processes character shapes, spacing, and layout to produce accurate digital text. Once processed, documents can be searched, indexed, or integrated into workflow systems for improved efficiency. The AI can handle multiple languages, handwriting, and varying image quality.

By automating this process, companies reduce manual entry errors, improve accessibility, and enable faster information retrieval across large document repositories.

Question 60:

A global e-commerce company wants to provide personalized recommendations based on images uploaded by users. 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 the AI model to classify images, detect multiple objects, and tag items based on custom labels. For e-commerce, this enables users to upload images of products they like, and the system can provide recommendations for similar or complementary products.

Computer Vision OCR (Option B) extracts text, not object information. Text Analytics (Option C) analyzes text data. Anomaly Detector (Option D) identifies numeric anomalies.

Custom Vision supports incremental learning, model export for edge deployment, and integration with recommendation engines. It allows real-time personalization, enhanced shopping experience, and actionable insights into customer preferences. Over time, the model improves as more images are labeled and uploaded, ensuring accuracy and relevance.