Microsoft AI-900 Azure AI Fundamentals Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21:

A company wants to monitor real-time sensor data from their manufacturing equipment to detect unexpected behavior or failures. Which Azure AI service should they use?

Answer:

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

Explanation:

Anomaly Detector is the correct answer because it is specifically designed to analyze time-series data and identify deviations from expected patterns. In manufacturing, sensors on equipment provide continuous streams of numeric data such as temperature, pressure, vibration, and operational metrics. Anomaly Detector uses machine learning to learn the normal behavior of the system and detect unusual patterns that may indicate potential equipment failures or operational issues. This enables organizations to proactively respond to problems before they escalate, reducing downtime, avoiding costly repairs, and maintaining operational efficiency. Computer Vision (Option B) is used for analyzing images and video and is not suitable for numeric sensor data. Text Analytics (Option C) is designed to extract insights from unstructured text, such as sentiment, key phrases, or language, and cannot detect anomalies in numeric data streams. QnA Maker (Option D) helps build knowledge bases and chatbots but does not monitor real-time data or detect anomalies. Anomaly Detector can be integrated with Azure Monitor, Logic Apps, or Power BI to provide automated alerts, dashboards, and actions, allowing teams to respond immediately to irregularities. The service also adapts over time, improving detection accuracy and reducing false positives. For example, seasonal variations or expected operational changes are accounted for, so true anomalies are accurately identified. This capability is critical in scenarios such as predictive maintenance, industrial IoT, and operational monitoring. By leveraging Anomaly Detector, businesses gain actionable insights that improve efficiency, safety, and reliability in their operations, ensuring proactive management of equipment and systems.

Question 22:

A retail company wants to analyze customer reviews on social media to understand public opinion about their products. Which Azure AI service is most appropriate?

Answer:

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

Explanation:

Text Analytics is the correct answer because it provides AI-powered capabilities to extract insights from unstructured text. This includes sentiment analysis, key phrase extraction, and language detection. Social media reviews, blog comments, and online feedback are all unstructured text sources, making Text Analytics ideal for understanding public opinion. It helps organizations quantify customer sentiment, identify trends, and detect emerging issues. Form Recognizer (Option B) extracts structured information from documents and forms, which is not applicable for social media text. Computer Vision (Option C) analyzes images and videos and cannot extract textual sentiment. QnA Maker (Option D) builds FAQ knowledge bases and conversational bots, which is unrelated to analyzing free-form text. Using Text Analytics, the company can determine positive, negative, or neutral sentiment in customer reviews and even identify key topics or frequently mentioned phrases. This can guide product development, marketing strategies, and customer support improvements. Additionally, integrating Text Analytics with Power BI allows visualization of trends and sentiment over time. The service can also detect multi-language content, helping global companies analyze feedback from diverse markets. By using Text Analytics, the retail company gains actionable insights from large volumes of unstructured data, enabling better decision-making, enhanced customer experience, and a proactive approach to reputation management.

Question 23:

A financial institution wants to extract key information such as account numbers, dates, and amounts from scanned invoices automatically. Which Azure AI service is most appropriate?

Answer:

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

Explanation:

Form Recognizer is the correct answer because it is specifically designed to extract structured data from forms and documents, including scanned invoices, receipts, and contracts. It uses machine learning models to identify fields, tables, and key-value pairs within documents, transforming them into machine-readable formats. Text Analytics (Option B) analyzes unstructured text for sentiment, key phrases, and language detection but cannot extract structured fields from documents. Custom Vision (Option C) focuses on image classification and object detection, not text extraction from documents. QnA Maker (Option D) builds knowledge bases for conversational responses and does not perform document analysis. Form Recognizer can be trained with labeled examples of invoices to improve accuracy in identifying specific fields like account numbers, billing amounts, dates, and line items. This allows financial institutions to automate data entry, reduce manual errors, and accelerate payment processing. Additionally, it supports multiple document formats, including PDFs and images, and can handle variations in layout and handwriting. Integration with Azure Logic Apps or Power Automate enables automated workflows, such as validating extracted data, updating databases, or generating reports. The service also supports prebuilt models for invoices, receipts, and business cards, reducing development time and effort. By leveraging Form Recognizer, organizations can streamline document processing, increase efficiency, reduce operational costs, and ensure more accurate financial recordkeeping. In industries such as banking, accounting, and insurance, this capability significantly improves productivity and minimizes the risk of human errors in handling critical information.

A financial institution that wants to automatically extract key information such as account numbers, dates, and amounts from scanned invoices should use Form Recognizer. Form Recognizer is an Azure AI service specifically designed to process forms and documents, converting unstructured or semi-structured data into machine-readable formats. It can identify key fields, tables, and key-value pairs in invoices, receipts, contracts, and other financial documents. Unlike Text Analytics, which focuses on analyzing unstructured text for sentiment, key phrases, or language detection, Form Recognizer is built to detect structured elements within documents, making it ideal for financial applications. Custom Vision is focused on image classification and object detection, and therefore does not provide the capability to extract text or tabular data from documents. QnA Maker is used to build conversational knowledge bases and cannot process forms or extract structured information from scanned documents.

Form Recognizer allows financial institutions to train custom models with labeled examples of their own invoices, improving the accuracy of field extraction for account numbers, billing amounts, dates, vendor names, and line items. This capability significantly reduces manual data entry and the potential for human error, streamlining workflows and accelerating financial processes such as payment approvals and reconciliation. The service supports a wide variety of document formats, including PDFs, scanned images, and handwritten notes, and can handle different layouts, fonts, and languages, which is important for institutions dealing with diverse client documentation. Additionally, Form Recognizer integrates seamlessly with automation tools like Azure Logic Apps and Power Automate, enabling end-to-end workflows where extracted data can be validated, stored in databases, or used to generate reports automatically.

Prebuilt models in Form Recognizer, such as those for invoices, receipts, and business cards, reduce development time and allow organizations to quickly deploy AI-powered document processing. By leveraging this service, financial institutions can improve operational efficiency, ensure more accurate financial recordkeeping, and enhance compliance with auditing requirements. Automating document analysis not only saves time and labor costs but also increases productivity and enables staff to focus on higher-value tasks such as financial analysis, fraud detection, and client service. Form Recognizer’s combination of accuracy, adaptability, and integration capabilities makes it the most appropriate choice for extracting structured financial data from scanned invoices and other business-critical documents.

Question 24:

A company wants to create a chatbot that can answer customer questions from its FAQ documents and improve automatically as new questions are asked. Which service should they use?

Answer:

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

Explanation:

QnA Maker is the correct answer because it is specifically designed to create a knowledge base from FAQs, documents, and URLs and enable a conversational interface through a chatbot. It allows organizations to automatically match user questions with pre-defined answers and supports multi-turn conversations for more complex queries. Custom Vision (Option B) is used for image classification and object detection and cannot provide conversational answers. Text Analytics (Option C) extracts insights from text but does not build an interactive question-answering system. Anomaly Detector (Option D) identifies unusual patterns in data but is unrelated to creating chatbots. QnA Maker enables organizations to continuously improve the knowledge base by logging unmatched questions, allowing the system to learn and provide better responses over time. It can be integrated with Azure Bot Service to deploy fully functional conversational bots across websites, apps, or messaging platforms. Additionally, it supports language understanding capabilities to enhance recognition of user intent. Using QnA Maker reduces the need for manual coding, accelerates deployment, and ensures consistent, accurate answers for customer support. This approach improves customer satisfaction by providing immediate, reliable responses, reduces workload on support teams, and allows scaling to handle large volumes of user queries efficiently. By leveraging AI to maintain and update the knowledge base automatically, businesses can adapt to evolving customer needs while maintaining high-quality service standards.

Question 25:

A hospital wants to digitize handwritten doctor notes for easier searching and integration into electronic health records. Which Azure AI service should they use?

Answer:

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

Explanation:

Computer Vision OCR is the correct answer because it can extract both printed and handwritten text from images and scanned documents, converting them into searchable, machine-readable text. Custom Vision (Option B) classifies or detects objects in images but does not extract text. QnA Maker (Option C) builds conversational knowledge bases and cannot process handwriting. Text Analytics (Option D) analyzes unstructured text but requires the text to be already digitized and cannot process handwritten notes directly. The OCR capability in Computer Vision is particularly important in medical workflows where doctor notes may be written in varied handwriting styles or on scanned paper forms. The service recognizes character shapes, spacing, and even noisy backgrounds to accurately convert handwriting into digital strings. Once converted, these notes can be indexed, searched, and integrated into electronic health records, improving access to patient information, reducing transcription errors, and enhancing clinical decision-making. It also supports regulatory compliance by maintaining accurate, digitized records and enabling analytics for research, reporting, or insurance purposes. By leveraging Computer Vision OCR, hospitals can streamline administrative tasks, improve patient care, and ensure efficiency in handling large volumes of handwritten documentation.

Question 26:

A company wants to translate user reviews from multiple languages into English to analyze customer feedback consistently. Which Azure AI service should they use?

Answer:

A) Translator Text
B) Text Analytics
C) Custom Vision
D) QnA Maker

Explanation:

Translator Text is the correct answer because it provides real-time translation of text between multiple languages. For analyzing global user reviews, it enables businesses to convert all content into a single language, such as English, so that consistent analysis can be performed. Text Analytics (Option B) can perform sentiment analysis and key phrase extraction but does not handle translation. Custom Vision (Option C) is focused on image classification and object detection and cannot process language translation. QnA Maker (Option D) is used for building knowledge bases and chatbots, not for translating content. Translator Text supports batch translations, automatic language detection, and integration with other services like Text Analytics for further analysis. By combining Translator Text with Text Analytics, organizations can perform multilingual sentiment analysis, extract key themes, and gain a global understanding of customer opinions. This service helps businesses overcome language barriers, streamline global customer support, and make data-driven decisions from diverse feedback sources. It is particularly useful in industries with an international presence, such as e-commerce, hospitality, and software, where understanding customer sentiment across languages is critical for product improvement and customer satisfaction.

A company that wants to translate user reviews from multiple languages into English to analyze customer feedback consistently should use Translator Text. Translator Text is an Azure AI service specifically designed to provide real-time translation of text across a wide range of languages, making it ideal for businesses that operate globally or receive user input in multiple languages. Unlike Text Analytics, which can analyze text for sentiment, extract key phrases, and recognize entities, Translator Text does not perform natural language analysis itself but focuses on converting text from one language to another accurately. Custom Vision is designed for image classification and object detection and has no capabilities for processing or translating text. QnA Maker is intended for building conversational knowledge bases and chatbots and is not equipped to handle translation tasks.

Translator Text supports both real-time translation and batch translation of large volumes of text, which makes it suitable for processing thousands of user reviews efficiently. It also includes automatic language detection, allowing businesses to handle content in unknown or mixed languages without manual intervention. Once the reviews are translated into a consistent language, such as English, they can be further analyzed using other services like Text Analytics to perform sentiment analysis, detect key topics, and extract actionable insights. This combination enables companies to gain a complete understanding of customer opinions worldwide and make data-driven decisions based on accurate, multilingual feedback.

The service is particularly useful in industries where understanding international customer sentiment is critical, including e-commerce, hospitality, software, and global marketplaces. Translator Text reduces language barriers, allowing customer support teams to respond to feedback more effectively and enabling marketing and product teams to identify patterns and preferences across regions. It integrates easily with other Azure services, workflows, and applications, allowing organizations to automate translation and analysis processes efficiently. By leveraging Translator Text, companies can ensure consistency in feedback analysis, improve global customer satisfaction, and enhance decision-making by incorporating insights from all regions, regardless of the original language of the reviews.

Question 27:

A retailer wants to automatically tag products in images uploaded by customers to identify items for recommendations. Which Azure AI service is most suitable?

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 and tag images based on custom labels. This is ideal for a retailer that wants to recognize products in customer-uploaded images and provide relevant recommendations. Computer Vision OCR (Option B) extracts text from images but cannot identify objects. Text Analytics (Option C) analyzes text for sentiment, key phrases, or language but does not handle images. Anomaly Detector (Option D) identifies unusual patterns in numeric data and is unrelated to image classification. Custom Vision enables the creation of models tailored to the organization’s product catalog and supports incremental learning, allowing models to improve over time as more images are collected. Integration with e-commerce platforms or recommendation engines allows retailers to automate product tagging, enhance user experience, and provide personalized suggestions. The service also supports object detection, which can locate items within images for further analysis. By leveraging Custom Vision, retailers can scale image recognition tasks efficiently, reduce manual effort, and improve the accuracy of product recommendations, ultimately boosting engagement and sales.

Question 28:

A company wants to analyze audio recordings of customer support calls to determine whether interactions are positive or negative. Which Azure AI service should they use?

Answer:

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

Explanation:

Speech Service with Text Analytics is the correct answer because the Speech Service can transcribe audio recordings into text, and Text Analytics can then perform sentiment analysis on the transcribed content. Form Recognizer (Option B) extracts structured data from documents but does not process audio. Custom Vision (Option C) handles image classification and object detection and cannot analyze audio content. Anomaly Detector (Option D) monitors numeric data for deviations and does not process audio. By combining Speech Service and Text Analytics, organizations can convert spoken conversations into text, detect sentiment, identify key issues, and gain actionable insights into customer interactions. This helps companies improve customer experience, train support staff, and detect emerging trends or complaints. Additionally, the service supports multiple languages, real-time transcription, and batch processing of recorded calls. Integrating these services with dashboards or reporting tools enables monitoring of call quality, response effectiveness, and overall customer satisfaction. This AI-powered approach allows businesses to scale analysis across thousands of interactions, providing consistent, accurate insights while reducing manual evaluation efforts.

Question 29:

A company wants to identify the intent behind customer emails automatically to route them to the appropriate department. 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 is designed to analyze natural language text, understand user intent, and extract relevant entities. By applying LUIS to customer emails, the system can determine whether a message relates to billing, technical support, product inquiries, or complaints, and route it to the correct department. Custom Vision (Option B) classifies images, not text. Form Recognizer (Option C) extracts structured data from documents, which is not applicable for free-form emails. Anomaly Detector (Option D) identifies unusual patterns in numeric data and does not interpret text. LUIS uses pre-built models that can be trained and customized for domain-specific intents, improving accuracy as the system processes more emails. It supports multi-turn conversations and integration with Azure Bot Service to automate response workflows. This AI approach reduces manual sorting of emails, improves response times, enhances customer experience, and allows companies to scale operations efficiently.

A company that wants to automatically identify the intent behind customer emails and route them to the appropriate department should use Language Understanding (LUIS). LUIS is an Azure AI service specifically designed to process natural language text, determine the underlying intent of a message, and extract relevant entities. This makes it highly suitable for analyzing unstructured content such as customer emails, where the purpose of each message must be understood before any automated routing or response can occur. By applying LUIS to email processing, the system can classify messages into categories such as billing inquiries, technical support requests, product questions, or complaints, and ensure they are directed to the appropriate team without manual intervention. Custom Vision, by contrast, is intended for image classification and object detection, and cannot analyze or interpret text. Form Recognizer extracts structured data from forms and documents but is not designed for free-form email content. Anomaly Detector identifies unusual patterns in numerical data and is not applicable for understanding written language.

LUIS enables companies to build models using prebuilt templates and then customize them for domain-specific intents, which improves accuracy as more data is processed. It can extract entities from the text, such as order numbers, product names, or account IDs, which further refines the routing process. The service also supports multi-turn conversations, allowing it to handle follow-up messages or context-dependent queries, and it integrates seamlessly with Azure Bot Service to automate responses and workflows. This combination of features allows businesses to reduce manual sorting and triaging of customer emails, which can significantly improve operational efficiency.

Using LUIS for email intent recognition also enhances customer experience by ensuring inquiries are addressed promptly and routed to knowledgeable personnel. It allows companies to scale operations efficiently, handling high volumes of messages without compromising response quality. Over time, the system learns from user interactions, continually improving intent recognition and entity extraction accuracy. By leveraging Language Understanding, organizations can implement a smart, AI-powered email management solution that reduces response times, minimizes errors in routing, and enables teams to focus on resolving customer issues effectively.

Question 30:

A hospital wants to extract patient information such as names, dates, and diagnosis from scanned medical forms. Which Azure AI service is most appropriate?

Answer:

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

Explanation:

Form Recognizer is the correct answer because it is designed to extract structured data from forms and documents automatically. In a hospital setting, medical forms contain critical patient data such as names, dates, and diagnoses, which need to be digitized for electronic health records. Custom Vision (Option B) classifies images but does not extract text. Text Analytics (Option C) analyzes unstructured text but requires it to already be in digital form. QnA Maker (Option D) provides knowledge-based responses but cannot process forms. Form Recognizer supports prebuilt models for invoices, receipts, and medical forms, as well as the ability to train custom models for specific layouts. It accurately identifies fields and tables, converts handwriting or printed text into machine-readable formats, and enables downstream workflows for storage, reporting, or analytics. Integration with Azure Logic Apps allows automated validation and data entry, improving efficiency and reducing manual errors. Using Form Recognizer, hospitals can streamline administrative processes, enhance data accuracy, and ensure compliance with regulations.

A hospital that wants to extract patient information such as names, dates, and diagnoses from scanned medical forms should use Form Recognizer. Form Recognizer is an Azure AI service specifically designed to automatically extract structured data from a variety of forms and documents. In healthcare settings, medical forms often contain critical information that needs to be digitized for electronic health records, billing, or reporting purposes. Manually entering this data is time-consuming and prone to errors, making automation through Form Recognizer highly valuable. Custom Vision, on the other hand, is focused on image classification and object detection and cannot extract textual data from documents. Text Analytics can process unstructured text but requires the content to already be in digital text format, meaning it cannot work directly with scanned or handwritten forms. QnA Maker is designed for building conversational knowledge bases and is not capable of processing documents or extracting structured information.

Form Recognizer offers prebuilt models tailored for invoices, receipts, and medical forms, and it also supports training custom models to handle specific layouts unique to an organization. It can detect key fields, tables, and handwritten or printed text, converting them into machine-readable formats that can be integrated into hospital information systems. This allows patient names, birth dates, appointment dates, diagnoses, and other important details to be accurately captured and routed to the appropriate databases. The service can handle variations in form layouts, fonts, and handwriting styles, which is essential in healthcare, where multiple departments may use different forms.

By integrating Form Recognizer with Azure Logic Apps or other workflow automation tools, hospitals can create end-to-end processes that automatically validate extracted data, update patient records, generate reports, or flag missing information. This significantly reduces administrative overhead, minimizes human error, and ensures compliance with healthcare regulations such as HIPAA. Automating the extraction of patient information also frees up staff to focus on direct patient care rather than paperwork, enhancing overall operational efficiency. Overall, Form Recognizer is the most appropriate Azure service for hospitals seeking to streamline document processing, improve data accuracy, and ensure timely access to critical patient information, making it an essential tool in modern healthcare management.

Question 31:

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 supports object detection, allowing organizations to identify and locate multiple objects in images or videos. For traffic monitoring, Custom Vision can be trained to detect cars, pedestrians, bicycles, and other objects in camera footage. Computer Vision OCR (Option B) extracts text from images and cannot detect objects. Text Analytics (Option C) analyzes text and is irrelevant for video/image object detection. QnA Maker (Option D) builds knowledge bases and does not analyze images. Custom Vision models can be trained with labeled traffic images, enabling the detection of objects in various scenarios, including different lighting or weather conditions. Integrating the service with analytics dashboards allows city planners to monitor congestion, optimize traffic signals, and enhance public safety. Custom Vision also supports exporting models to edge devices for real-time detection at traffic intersections. Using this AI-powered approach improves traffic management efficiency, reduces human monitoring costs, and supports smarter urban planning initiatives.

Question 32:

A company wants to analyze scanned contracts to extract important clauses, dates, and parties involved. Which Azure AI service is most suitable?

Answer:

A) Form Recognizer
B) Text Analytics
C) QnA Maker
D) Anomaly Detector

Explanation:

Form Recognizer is the correct answer because it is designed to extract structured information from documents, including contracts. It can identify key-value pairs, tables, and specific clauses in scanned documents. Text Analytics (Option B) extracts insights from unstructured text but requires digital text, not scanned images. QnA Maker (Option C) builds knowledge bases and does not process scanned documents. Anomaly Detector (Option D) identifies deviations in numeric data, unrelated to document analysis. Form Recognizer can be trained to detect specific sections in contracts, extract parties’ names, effective dates, renewal clauses, and payment terms. This enables legal teams to quickly analyze large volumes of contracts, automate review processes, and reduce manual errors. Integration with workflow automation tools allows extracted data to populate databases, trigger notifications, or generate reports. Leveraging Form Recognizer improves operational efficiency, ensures regulatory compliance, and enhances decision-making in contract management.

Question 33:

A customer support team wants to transcribe live audio calls and identify key topics for analytics. Which Azure AI service combination 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 and Text Analytics is the correct answer because Speech Service converts audio into text through speech-to-text transcription, and Text Analytics processes the transcribed text to extract key phrases, topics, and sentiment. Form Recognizer and Custom Vision (Option B) are for document extraction and image classification, not audio. Anomaly Detector and QnA Maker (Option C) detect numeric anomalies and build knowledge bases, respectively, not for analyzing live calls. Computer Vision OCR and Custom Vision (Option D) are for images, not audio or text analytics. By combining Speech Service with Text Analytics, organizations can analyze thousands of calls efficiently, identify recurring issues, improve training, and enhance customer experience. The system can support multiple languages, detect sentiment trends, and integrate with dashboards for monitoring performance metrics, ensuring a scalable and data-driven approach to customer support analytics.

A customer support team that wants to transcribe live audio calls and identify key topics for analytics should use a combination of Speech Service and Text Analytics. Speech Service is an Azure AI service that converts spoken audio into text using advanced speech-to-text models. It supports real-time transcription as well as batch processing of recorded audio, making it ideal for call centers and live customer interactions. Once the audio is transcribed into text, Text Analytics can process the content to extract meaningful insights such as key phrases, topics, sentiment, and entities. This combination allows organizations to not only capture what was said during customer calls but also analyze the underlying themes and trends, enabling data-driven decision-making. Form Recognizer and Custom Vision, in contrast, are designed for document extraction and image classification, respectively, and cannot process audio or perform text analytics. Anomaly Detector identifies unusual numeric patterns, and QnA Maker builds knowledge bases, but neither service is suitable for transcribing or analyzing live calls. Similarly, Computer Vision OCR and Custom Vision focus on images and cannot handle audio content or text analytics.

By using Speech Service and Text Analytics together, customer support teams can efficiently process large volumes of calls. Speech Service provides features such as punctuation insertion, speaker diarization, and noise reduction, ensuring accurate and readable transcripts even in noisy environments. Text Analytics then enables automatic identification of recurring issues, frequently asked questions, and trends in customer sentiment. This helps organizations improve agent training, detect areas for service improvement, and enhance the overall customer experience. The system can support multiple languages, making it scalable for global operations, and can be integrated with dashboards or reporting tools for real-time monitoring of performance metrics.

This approach also enables automation in quality assurance, compliance tracking, and workflow optimization. Insights generated from call transcripts can feed into AI models to predict customer needs, suggest resolutions, or prioritize critical issues. Combining Speech Service with Text Analytics provides a comprehensive solution for turning raw audio into actionable insights, making it the most effective choice for organizations seeking to improve efficiency, customer satisfaction, and operational intelligence in support environments.

Question 34:

A company wants to identify the language of user-generated content and translate it into a target language for sentiment analysis. Which Azure AI services should they use?

Answer:

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

Explanation:

Text Analytics and Translator Text is the correct answer because Text Analytics can detect the language and perform sentiment analysis, while Translator Text converts the content into a target language for unified analysis. Custom Vision and Form Recognizer (Option B) are unrelated to language processing. QnA Maker and Anomaly Detector (Option C) are designed for knowledge bases and anomaly detection, not multilingual text analysis. Computer Vision OCR and Custom Vision (Option D) focus on images and object detection. By combining these services, companies can analyze global user-generated content, understand sentiment consistently across languages, extract key insights, and inform product or marketing strategies. This approach enables multilingual analytics at scale, improving customer insights and strategic decision-making.

Question 35:

A company wants to detect unusual patterns in financial transactions to identify potential 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 or transactional data to identify patterns that deviate from normal behavior. In finance, detecting unusual transactions is critical for fraud prevention. Text Analytics (Option B) analyzes text and cannot detect numeric anomalies. Custom Vision (Option C) handles images, not financial data. QnA Maker (Option D) builds knowledge bases, which is unrelated. Anomaly Detector learns normal transaction patterns, assigns confidence scores to potential anomalies, and can trigger alerts. Integration with workflow automation ensures immediate response to suspicious activities. This AI approach reduces financial risks, improves compliance, and ensures secure operations by proactively identifying potentially fraudulent transactions.

Question 36:

A hospital wants to identify and redact sensitive patient information from medical documents to maintain compliance with privacy regulations. 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 is the correct answer because it can identify personally identifiable information such as names, addresses, Social Security numbers, and dates in text. Form Recognizer (Option B) extracts structured data but does not automatically redact sensitive information. Custom Vision (Option C) handles images and object detection. Anomaly Detector (Option D) identifies numeric anomalies. By detecting and redacting PII, hospitals ensure compliance with HIPAA and other privacy regulations. This capability allows secure sharing of documents for research, reporting, or analytics while protecting patient privacy. It can process large volumes of text efficiently and integrate with automated workflows to redact content before storage or sharing.

A hospital that wants to identify and redact sensitive patient information from medical documents to maintain compliance with privacy regulations should use Text Analytics for PII. Text Analytics for PII is an Azure AI service specifically designed to detect personally identifiable information in text, including names, addresses, phone numbers, dates of birth, Social Security numbers, and other sensitive data. This capability is essential in healthcare environments where patient privacy must be protected according to regulations such as HIPAA. Form Recognizer, while useful for extracting structured data from forms and documents, does not automatically detect or redact sensitive information. Custom Vision focuses on image classification and object detection and cannot process text to identify PII. Anomaly Detector identifies unusual patterns in numerical data and does not have any functionality for recognizing or protecting sensitive textual information.

By using Text Analytics for PII, hospitals can automatically scan medical documents, clinical notes, and patient correspondence to identify sensitive data that must be protected before storage, processing, or sharing. Once PII is detected, the system can redact or mask it to prevent unauthorized access while still allowing the rest of the content to be used for analytics, reporting, or research. This ensures that medical teams can safely share and analyze patient data without violating privacy laws. The service can handle large volumes of documents efficiently, reducing the need for manual review, which is time-consuming and prone to human error.

Text Analytics for PII can also be integrated into automated workflows, such as document management systems, electronic health records, or cloud storage pipelines. Hospitals can implement processes where documents are scanned, PII is detected and redacted, and sanitized versions are stored or shared with researchers, auditors, or administrative teams. This improves operational efficiency while maintaining compliance with privacy regulations. In addition, the service can process text in multiple languages, making it suitable for global healthcare environments.

Overall, Text Analytics for PII provides a reliable, scalable, and automated solution for protecting patient privacy. It allows healthcare organizations to maintain compliance, safeguard sensitive information, and streamline the secure handling of medical documents, ensuring that sensitive data is never inadvertently exposed while still supporting data-driven operations and research initiatives.

Question 37:

A company wants to build a recommendation system that suggests products based on images uploaded by users. Which Azure AI service should they use?

Answer:

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

Explanation:

Custom Vision is the correct answer because it allows training models to classify and tag images. By tagging products in user-uploaded images, a recommendation engine can suggest similar or complementary items. Computer Vision OCR (Option B) extracts text from images, which is not relevant. QnA Maker (Option C) builds knowledge bases. Text Analytics (Option D) processes text. Custom Vision supports object detection, incremental learning, and model export for real-time applications, enabling scalable recommendation systems.

Question 38:

A logistics company wants to analyze handwritten delivery forms to digitize the data for tracking shipments. 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 and printed text from images and scanned documents, converting it into machine-readable format. Custom Vision (Option B) focuses on image classification. Form Recognizer (Option C) works best with structured forms, but OCR is better for freeform handwriting. Text Analytics (Option D) analyzes text but cannot process images directly. Computer Vision OCR enables digitization of delivery forms, improving tracking, reducing manual entry errors, and ensuring faster shipment processing. Integration with logistics systems allows automated updates and efficient operations.

Question 39:

A company wants to extract entities such as dates, amounts, and product names from customer emails to automate workflows. Which Azure AI service is most suitable?

Answer:

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

Explanation:

Text Analytics is the correct answer because it can perform named entity recognition on unstructured text, extracting key information such as dates, amounts, product names, and locations. Custom Vision (Option B) handles images. Form Recognizer (Option C) works with structured forms but not unstructured emails. QnA Maker (Option D) builds knowledge bases. By using Text Analytics, companies can automate workflow triggers, categorize emails, and extract actionable information without manual intervention. Integration with automation tools allows routing, reporting, and analytics based on extracted entities.

Question 40:

A travel company wants to provide multilingual support to customers and analyze sentiment in their inquiries to improve service. Which combination of Azure AI services should they use?

Answer:

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

Explanation:

Translator Text and Text Analytics is the correct answer because Translator Text converts multilingual customer inquiries into a common language, and Text Analytics performs sentiment analysis and key phrase extraction. Custom Vision and Form Recognizer (Option B) handle images and documents, not language or sentiment. QnA Maker and Anomaly Detector (Option C) build knowledge bases and detect numeric anomalies. Computer Vision OCR and Custom Vision (Option D) are for images. This combination allows the company to scale multilingual customer support, understand sentiment across languages, and make data-driven improvements to service quality. Integration with dashboards and automation tools provides real-time insights into customer experience and satisfaction.