Implement natural language processing solutions

A company is building a chatbot using Azure Cognitive Service for Language. They need to ensure that user utterances are correctly mapped to the appropriate intent in a custom question answering project. What should they configure?

A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?

A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?

A company wants to use Azure AI Translator to translate customer emails from English to French. They need to ensure that the translation preserves the tone and formality of the original text. What should they configure in the request?

A development team is using the Azure Cognitive Service for Language to perform sentiment analysis on social media posts. They notice that the returned sentiment scores are often neutral for posts that are clearly positive or negative. What is the most likely reason?

Which TWO actions should you take to optimize a custom text classification model in Azure Cognitive Service for Language?

Which THREE components are required to build a custom named entity recognition (NER) model in Azure Cognitive Service for Language?

A company is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot needs to handle user intents for booking flights and checking flight status. After testing, the chatbot frequently fails to distinguish between the two intents when users mention flight numbers. Which action should the engineer take to improve intent recognition?

A hospital uses Azure Cognitive Service for Language to extract medical entities from clinical notes. The extraction accuracy for medication names and dosages is low. The engineer needs to improve performance without adding new training data. Which solution should the engineer implement?

A developer is using Azure Cognitive Service for Language to perform sentiment analysis on customer reviews. The service returns sentiment labels (positive, negative, neutral) and confidence scores. For a particular review, the service returns 'positive' with a confidence score of 0.55. The developer wants to ensure that only high-confidence results are used. What should the developer do?

Which THREE actions should an engineer take when deploying a custom question answering project in Azure Cognitive Service for Language?

You are analyzing a document using Azure Cognitive Service for Language named entity recognition. The exhibit shows a partial JSON response for entity extraction. The engineer notices that 'Jane Smith' has a low confidence score of 0.45. Which action should the engineer take to improve the confidence score for similar entities?

A large retail company deploys a custom text classification model using Azure Cognitive Service for Language to categorize customer support tickets into 'Billing', 'Technical', and 'General' categories. The model is trained on 10,000 labeled tickets from the past year. After deployment, the model performs well on new tickets but shows a significant drop in accuracy for tickets submitted during holiday seasons, where the volume of billing issues spikes. The engineering team suspects concept drift. They need to maintain high accuracy without manual retraining every season. Which action should the engineer take?

A company is building a chatbot using Azure Language Service and wants to ensure that the chatbot can understand user intents and extract entities from user utterances. The chatbot must be able to handle multiple intents in a single utterance and must support pre-built entities such as numbers and dates. Which action should the developer take to configure the Language service accordingly?

A healthcare company is developing a solution to analyze patient feedback using Azure AI Language. The solution must extract key phrases, detect sentiment, and identify personally identifiable information (PII) such as patient names and medical record numbers from unstructured text. The company has strict compliance requirements: all text processing must occur within the United States region, and no data may leave the Azure geography. The development team has provisioned a Language resource in the East US region and has been testing the solution. During testing, the team notices that the PII detection feature is returning results, but the key phrase extraction and sentiment analysis are failing with a 403 error. The error message indicates that the resource is not allowed to access these features. The team has verified that the resource is in the S0 tier. What should the team do to resolve the issue?

Drag and drop the steps to deploy a custom language model using Azure AI Language into the correct order.

Match each Azure AI term to its definition.

A healthcare organization is building a clinical decision support system that must extract medical entities (e.g., symptoms, diagnoses, medications) from unstructured clinical notes. The solution must be able to detect relationships between entities, such as 'medication X treats symptom Y'. Which Azure AI service should be used?

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

A company wants to analyze customer reviews to determine whether sentiment is positive, negative, or neutral. The solution must also extract key phrases such as 'great battery life' and 'poor camera quality'. Which Azure AI feature should be used?

You are building a multilingual support chatbot using Azure AI Language. The chatbot must understand user queries in English, Spanish, and French, and respond in the same language. The solution should minimize latency and cost. What is the recommended approach?

A financial services company uses Azure AI Language's custom text classification to categorize loan applications as 'Approved', 'Denied', or 'Review Required'. The model is trained on historical data but is producing poor accuracy on new applications. The data scientist suspects data leakage between training and test sets. What should the data scientist do to validate this?

A news organization wants to automatically summarize long articles into short, coherent summaries. The solution must preserve the original meaning and key points. Which Azure AI service should be used?

You are developing a conversational agent using Microsoft Copilot Studio that must handle complex multi-turn conversations. The agent needs to maintain context across multiple user inputs. Which feature should you use?

A legal firm uses Azure AI Language's custom NER to extract party names, dates, and clauses from contracts. The model performs well on English contracts but poorly on French contracts. The firm wants to improve performance without retraining from scratch. What is the most efficient approach?

A company wants to build a solution that can identify and redact personally identifiable information (PII) from customer support transcripts. The solution must handle multiple languages. Which Azure AI service should be used?

Which TWO Azure AI services can be used to build a multilingual question-answering bot that retrieves answers from a knowledge base of documents?

Which THREE factors should be considered when choosing between Azure AI Language's pre-built sentiment analysis and custom sentiment analysis for a specialized domain?

Which TWO capabilities are provided by Azure AI Language's pre-built entity recognition?

Refer to the exhibit. You submit this request to the Azure AI Language service. What is the expected response?

Refer to the exhibit. You are defining a custom entity recognition model in Azure AI Language. The exhibit shows a partial configuration. What is the relationship between 'Laptop' and 'Electronics'?

Refer to the exhibit. You submit this request to Azure AI Language's conversational language understanding (CLU) for the 'FlightBooking' project. The model correctly identifies the intent as 'BookFlight' and extracts entities: 'Seattle' as FromCity, 'New York' as ToCity, and 'June 15th' as Date. What is the next step for the application?

A company deploys a custom question answering project in Azure AI Language. Users report that the bot sometimes returns irrelevant answers. The knowledge base contains hundreds of QnA pairs. You need to improve answer relevance without retraining the model. What should you do?

You are building a chatbot that uses Azure AI Language to extract intents and entities from user utterances. The bot must recognize custom entities like product names that are not in the default model. Which feature should you use?

A company uses Azure AI Language for sentiment analysis on customer feedback. They notice that the sentiment scores for mixed reviews are often neutral when they should be slightly positive. They need to improve the accuracy for these mixed reviews without labeling new data. Which approach should you recommend?

A developer is building a multilingual chatbot using Azure AI Language. The bot must detect the user's language automatically and route the query to the appropriate language-specific model. Which Azure AI Language feature should the developer use?

A company uses Azure AI Language to analyze customer call transcripts. They need to identify specific entities such as product names and issue types. The prebuilt entity recognition does not cover their custom entities. Which approach should they take to extract both standard and custom entities from the transcripts?

A team is developing a solution to automatically summarize long documents using Azure AI Language. Which feature should they use?

A company uses Azure AI Language's conversational language understanding (CLU) to build a customer support bot. They want to integrate the bot with Microsoft Teams and need to ensure that user authentication is handled by Microsoft Entra ID. However, users report that the bot sometimes fails to respond when they are not signed into Microsoft Entra ID. What is the most likely cause?

A company uses Azure AI Language's custom text classification to categorize support tickets. The model was trained with 5000 labeled examples and achieves 90% accuracy. However, for a specific category (e.g., 'billing'), the model frequently misclassifies tickets that contain both billing and technical issues. Which action should you take to improve classification for this category?

You are using Azure AI Language to analyze customer reviews. You need to determine whether each review expresses a positive, negative, or neutral sentiment. Which API should you call?

Which TWO actions should you take to improve the performance of a custom named entity recognition (NER) model in Azure AI Language?

Which THREE components are required to deploy a bot using Azure AI Language's conversational language understanding (CLU) and Azure Bot Service?

Which TWO features are available in Azure AI Language's extractive summarization?

Refer to the exhibit. You are calling the Azure AI Language API for extractive summarization. What will be the output of this request?

Refer to the exhibit. You are calling the Azure AI Language API for conversational language understanding (CLU). The CLU project 'SupportBot' has an intent 'CancelOrder' with an entity 'OrderNumber' of type 'Number'. The deployment 'production' is active. What is the expected output?

Refer to the exhibit. You are calling the Azure AI Language API for entity linking. What is the primary purpose of this request?

A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?

A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?

A healthcare organization deploys an Azure AI Language Service custom entity recognition model to extract medical conditions from clinical notes. During testing, the model fails to recognize rare diseases mentioned in the training data. What is the most likely cause?

A company uses Azure AI Language Service to summarize long documents. They need to generate concise summaries that capture the main points. Which feature should they use?

A company is developing a conversational AI solution using Microsoft Copilot Studio. They want the copilot to answer questions based on a knowledge base of technical documents. Which data source integration should they use?

A company uses Azure AI Language Service for custom text classification. The model is trained to classify support tickets into categories. After deployment, the model performs well on the test set but poorly on new incoming tickets. Which action should be taken to improve generalization?

A company needs to extract personally identifiable information (PII) from customer support transcripts stored in Azure Blob Storage. Which Azure AI service should they use?

A company is implementing a question-answering system using Azure AI Language Service. They have a set of FAQ documents in PDF format. Which feature should they use to automatically generate question-answer pairs?

A company uses Azure AI Language Service with Custom Entity Recognition to extract invoice fields. The model correctly extracts invoice numbers but fails to extract dates in the format 'dd/mm/yyyy'. The training data includes dates in 'mm/dd/yyyy' format. What is the most likely issue?

Which TWO actions should you take to ensure that an Azure AI Language Service custom entity recognition model complies with data privacy regulations?

Which THREE components are required to build a custom question-answering solution using Azure AI Language Service?

Which TWO Azure AI services can be used to analyze sentiment in text data?

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Refer to the exhibit. You deploy this ARM template to create an Azure AI Language Service resource. After deployment, you try to call the Language Service API from your application but receive a 403 Forbidden error. What is the most likely cause?

Refer to the exhibit. You are creating a Conversational Language Understanding (CLU) project using the Azure AI Language Service REST API. You want the project to support both English and Spanish utterances. Which parameter in the request body enables this?

A company is building a chatbot using Azure AI Language. The chatbot must detect user intent from utterances and also extract key entities like dates and product names. The solution must minimize latency for real-time conversation. Which approach should the team use?

You are developing a custom text classification model using Azure AI Language. The model must classify customer support tickets into 15 categories. You have 10,000 labeled examples. After training, the model shows 95% accuracy on the test set but only 60% on a small sample of new tickets. What is the most likely cause?

You need to extract key phrases from a large collection of customer reviews using Azure AI Language. The solution should be cost-effective and process up to 1,000 documents per day. Which pricing tier should you choose?

A healthcare organization uses Azure AI Language to analyze clinical notes. They need to detect protected health information (PHI) such as patient names and dates of birth, and also identify medical conditions. Which Azure AI Language feature should they use?

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

You are using Azure AI Language to analyze sentiment in customer feedback. The analysis returns a sentiment label of 'mixed' for a review that contains both positive and negative statements. The overall sentiment score is 0.75 (positive). What does this indicate?

You are deploying a custom named entity recognition (NER) model using Azure AI Language. The model must extract product codes that follow a specific pattern (e.g., 'PRD-12345'). You have 5,000 labeled examples. After training, the model extractor works well on development data but fails to extract product codes from new data. What is the most likely issue?

You need to analyze customer service call transcripts to identify common issues. The solution must extract key phrases, detect sentiment, and identify the language used. The transcripts are stored in Azure Blob Storage. Which Azure AI Language feature should you use to process them asynchronously?

A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?

You are building a custom question answering solution using Azure AI Language. Which TWO actions are required to deploy the solution?

You are designing a solution to detect personally identifiable information (PII) in documents using Azure AI Language. The solution must also handle documents in multiple languages. Which THREE features should you use?

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

You are debugging a CLU application. The JSON above shows a request to the Azure AI Language runtime API. The response returns an intent of "BookFlight" with a confidence of 0.95, but no entities are extracted. The training data includes entities like "Location" and "DateTime". What is the most likely cause?

You run the PowerShell script above to call the Text Analytics API. The response shows a sentiment label of 'positive' with a score of 0.99. However, you expected 'negative' because the word 'excellent' was meant to be sarcastic. What is the most likely reason for this result?

You deploy the ARM template above to create an Azure AI Language resource. After deployment, you try to use the custom question answering feature but it is not available. What is the most likely reason?

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

You are building a solution to extract key phrases from customer reviews using Azure AI Language. Which feature should you use?

You are designing an NLP solution to analyze legal documents. The solution must identify specific clauses and parties involved. Which Azure AI service is most appropriate?

You need to translate a large volume of documents from English to French while preserving the original formatting. Which Azure service should you use?

You are deploying a question answering solution using Azure AI Language. The solution must be able to provide answers from a set of frequently asked questions (FAQs) in PDF format. What should you do?

You are building a conversational AI solution that must handle multiple intents in a single user utterance. Which Azure AI feature should you use?

Your NLP solution uses custom text classification in Azure AI Language. You need to improve the model's accuracy. Which action should you take?

You need to analyze customer call transcripts to identify positive and negative sentiment. Which Azure AI Language feature should you use?

You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?

Which TWO actions can you take to improve the performance of a Conversational Language Understanding model?

Which THREE factors should you consider when selecting a region for an Azure AI Language resource?

Which TWO capabilities are provided by the Azure AI Language service?

You are testing a Conversational Language Understanding application. You send the JSON request shown in the exhibit. What is the purpose of this request?

You deploy the ARM template shown in the exhibit. After deployment, you need to allow access to the Language service from your on-premises application. What should you do?

You submit the request shown in the exhibit to the Azure AI Language service. What will the response contain?

Your company uses Azure AI Language to analyze customer feedback. You need to extract key phrases from reviews in multiple languages. Which feature should you use?

You are building a chatbot using Azure AI Language and need to handle user intents that are not covered by the predefined intents. What should you implement?

Your organization uses Azure AI Language for custom text classification. You have deployed a model to a dedicated endpoint. After updating the training data, you retrain and redeploy the model. Users report that the endpoint still returns predictions from the old model. What is the most likely cause?

Which TWO Azure AI services can be used to perform sentiment analysis on text?

Which THREE components are required to build a custom question answering solution using Azure AI Language?

Your organization uses Azure AI Language to analyze customer support tickets. You need to ensure that personally identifiable information (PII) is detected and redacted before further processing. Which TWO features should you use?

You deploy an Azure AI Services resource using the ARM template shown in the exhibit. You need to test the Language service API from your local machine. What should you do first?

A PowerShell script (exhibit) attempts to update the endpoint for an Azure AI Language resource. What is the outcome of running this script?

You are designing a conversational AI solution using Microsoft Copilot Studio. The exhibit shows part of a topic configuration. What is the purpose of the 'triggers' section?

Your company uses Azure AI Document Intelligence to process invoices. You need to extract the invoice date and total amount. Which model should you use?

You are building a custom named entity recognition (NER) model using Azure AI Language. After labeling 200 documents, you train the model and achieve 85% precision but only 60% recall. Which action is most likely to improve recall?

Which TWO components are required to create a custom text classification model in Azure AI Language?

Your organization needs to analyze customer call transcripts to extract key insights, including sentiment, issues, and resolution. Which THREE Azure AI Language features should you use?

Your team uses Azure AI Language in a multi-region architecture. You need to ensure that the solution is resilient to regional outages. Which THREE actions should you take?

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

You are building a chatbot that must understand user intents from free-text input. You have a small set of labeled examples. Which Azure AI Language feature should you use to classify intents with minimal effort?

You need to transcribe customer service calls into text for analysis. Which Azure service should you use?

You are deploying a custom Named Entity Recognition (NER) model using Azure AI Language. You have 500 labeled documents. After training, the model shows high precision but low recall. Which action is most likely to improve recall?

Your application needs to extract key phrases from customer reviews to identify common topics. Which Azure AI Language feature should you use?

You are using Azure AI Translator to translate documents from English to French. Some technical terms must remain untranslated. How should you handle this?

You need to analyze customer feedback to determine whether the sentiment is positive, negative, or neutral. Which Azure AI service should you use?

You are building a multilingual chatbot using Azure AI Language. For a given user utterance, you need to first detect the language, then route to the appropriate language-specific intent model. Which combination of Azure AI Language features should you use?

You are using Azure AI Language to analyze medical records. The built-in NER model does not recognize some medical terms. What should you do?

You need to translate a large batch of documents from English to multiple languages. Which Azure service should you use?

Which TWO Azure AI Language features can you use to extract structured data from unstructured text?

Which THREE factors should you consider when choosing between a pre-built model and a custom model in Azure AI Language?

Which TWO Azure services can be used to implement a conversational AI solution that understands user intent and responds appropriately?

Refer to the exhibit. You send this request to the Conversational Language Understanding API. The response includes the intent 'BookFlight' with entities 'FromCity: Seattle' and 'ToCity: Boston', but the 'Date' entity is missing. What is the most likely cause?

Refer to the exhibit. You are designing a Data Factory pipeline to perform sentiment analysis on a text column. The pipeline fails with a 'BadRequest' error. What is the most likely issue?

Refer to the exhibit. You are calling the Azure AI Language NER API. The response returns no entities. What is the most likely reason?

You are building a chatbot that must handle customer inquiries about order status. The solution must use Azure AI Language and support multiple languages. You need to configure the project to detect language automatically from user input. Which setting should you enable?

You are designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?

Your organization needs to analyze customer feedback from social media posts to determine the sentiment (positive, negative, neutral). The solution must process up to 10,000 posts per day and provide a confidence score for each sentiment. Which Azure AI service should you use?

You are developing a multilingual chatbot that must understand user intents in English, Spanish, and French. You are using the Azure AI Language service with a Conversational Language Understanding (CLU) project. What is the recommended approach to handle multiple languages?

You have a custom Named Entity Recognition (NER) model trained using Azure AI Language. The model is performing poorly on new data. You need to improve its accuracy. Which action should you take first?

You need to build a solution that can answer questions based on a set of PDF documents, such as product manuals. The solution should allow users to ask questions in natural language and receive answers with citations. Which Azure AI service should you use?

You are deploying a Conversational Language Understanding (CLU) model to production. You need to monitor the model's performance and detect when retraining is needed due to concept drift. Which metric should you monitor?

Your organization has a large corpus of legal documents that need to be analyzed for specific clauses. You need to extract key information such as party names, dates, and monetary amounts. The solution must be able to handle varying document formats (PDF, Word, scanned images). Which combination of Azure AI services should you use?

You want to use the Azure AI Language service to summarize long customer support conversations into a short summary. Which feature should you use?

You are building a solution that must translate customer chat messages from Spanish to English in real-time. The solution must also detect the language of incoming messages to handle cases where users write in other languages. Which TWO Azure AI service features should you use?

You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?

You are using the Azure AI Language service to process customer reviews. You need to extract the following insights: overall sentiment, key phrases, and entity types (such as product names). Which THREE operations should you call?

You need the project to support English, Spanish, and French. What change should you make to the command?

Based on the exhibit, which entity should you focus on improving by adding more labeled examples?

You notice a spike in errors (HTTP 429) on a specific day. What is the most likely cause?

You are building a chat bot that uses Azure AI Language to process customer support tickets. The bot must extract entities like order numbers (e.g., ORD-12345) and issue categories. You need to choose the best approach for entity extraction to minimize development effort and ensure high accuracy.

You are developing a solution that uses Azure AI Language to analyze customer feedback. You need to determine whether the sentiment of a given sentence is positive, negative, or neutral. Which Azure AI Language feature should you use?

Your company uses a custom question answering knowledge base in Azure AI Language to answer employee questions about HR policies. You need to update the knowledge base with a new set of FAQ documents that contain tables and images. What is the best way to ingest the new content?

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

You are using Azure AI Language to analyze customer reviews. You need to identify specific aspects (e.g., 'price', 'service') and their associated sentiment. Which feature should you use?

You are building a custom text classification solution in Azure AI Language. You have a dataset with 10 categories and 1000 labeled documents. You need to choose the best project type. What should you use?

You need to summarize a large document using Azure AI Language. Which feature should you use?

You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?

You are building a solution that uses Azure AI Language to analyze customer support transcripts. You need to detect personally identifiable information (PII) and also redact the detected PII from the text. Which TWO features should you use? (Select TWO.)

You are deploying a custom question answering solution in Azure AI Language. You need to ensure that the knowledge base can handle synonyms and alternative phrasings for questions. Which THREE strategies should you implement? (Select THREE.)

You are using Azure AI Language to analyze social media comments. You need to identify the language of each comment and then extract key phrases. Which TWO features should you use? (Select TWO.)

Refer to the exhibit. You are configuring an Azure AI Language resource using an ARM template. The settings include PII recognition with domain set to 'phi'. What is the effect of this setting?

Refer to the exhibit. You have an Azure AI Language resource named MyLangService. You need to call the conversational language understanding (CLU) API. Which URL should you use?

You are a developer at a large retail company. The company receives thousands of product reviews daily. You need to build a solution that automatically categorizes reviews into positive, negative, and neutral sentiments, and also extracts key product features mentioned (e.g., battery life, screen quality) along with their associated sentiments. The solution must be scalable and cost-effective. You have access to Azure AI Language. You decide to use the built-in sentiment analysis and opinion mining features. However, after initial testing, you find that the opinion mining feature does not always correctly associate sentiments with the correct product features. For example, in the review 'The battery life is great but the screen is terrible', opinion mining might incorrectly associate 'terrible' with 'battery life'. You need to improve the accuracy of feature-sentiment association. What should you do?

A company is building a chatbot using Azure AI Language. The chatbot must understand user intents and extract entities like dates and locations. The solution should minimize manual labeling effort. Which feature should the team use?

A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?

A multinational corporation uses Azure AI Language to analyze customer feedback in multiple languages. The solution must detect the language of incoming text and then perform sentiment analysis. Which approach minimizes latency and cost?

A company uses Azure AI Speech to transcribe customer service calls. They need to identify callers by name and account number. Which feature should be enabled?

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and redact protected health information (PHI). Which capability should the team configure?

A developer is building a multilingual translation solution using Azure AI Translator. The solution must translate text to French, German, and Spanish. Which parameter should the developer set to specify the target language?

A company uses Azure AI Speech for real-time captioning during live events. They notice a delay of 5 seconds between speech and caption display. Which action should they take to reduce latency?

A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?

A developer is creating a custom question answering project in Azure AI Language. The knowledge base contains product manuals in PDF format. Which step is essential before importing the PDFs?

A company is building a bot using Microsoft Copilot Studio (formerly Power Virtual Agents). They want to use Azure AI Language to understand user intents. Which TWO components are required?

A company uses Azure AI Language to analyze customer reviews. They need to detect sentiment, extract key phrases, and identify named entities. Which THREE capabilities should they combine?

A developer is deploying a custom text classification model in Azure AI Language. The model must be accessible via a REST API with low latency. Which TWO actions should the developer take?

A company uses Azure AI Speech to provide real-time transcription for customer support calls. The solution must handle multiple languages and filter out profanity. Which THREE configurations are needed?

You are an AI engineer at a global e-commerce company. The company uses Azure AI Language to analyze product reviews in English, Spanish, and French. The current solution calls the sentiment analysis API for each review individually, resulting in high latency and cost. You need to design a new solution that processes reviews in batches, reduces the number of API calls, and still supports multiple languages. The solution must also extract key phrases and detect the language automatically. You have the following options: Option A: Use the Azure AI Language synchronous API with the 'multi-language' parameter set to true. Send reviews one by one. Option B: Use the Azure AI Language asynchronous batch API. Combine all reviews into a single batch request, but only for one language at a time. Option C: Use the Azure AI Language asynchronous batch API. Send a single batch request with all reviews, setting the 'language' parameter to 'multi' to auto-detect language, and specify sentiment analysis, key phrase extraction, and language detection as tasks. Option D: Use the Azure AI Translator service to translate all reviews to English, then use the Azure AI Language batch API for English-only sentiment and key phrase extraction.

You are an AI developer at a legal firm. The firm uses Azure AI Language to extract entities from legal documents. The current custom NER model is trained on a small dataset and has low accuracy for certain entity types like 'Statute' and 'Case Citation'. You need to improve the model's performance without increasing the labeling effort significantly. You have the following options: Option A: Add more labeled examples for the underperforming entity types by manually labeling additional documents. Option B: Use the prebuilt entity recognition model from Azure AI Language and map its outputs to custom entities. Option C: Enable active learning in the custom NER project and review the suggested labels from the model. Option D: Train a new model using the Azure Machine Learning automated ML (AutoML) for text classification.

You are a solution architect at a media company. The company uses Azure AI Speech to generate subtitles for videos. The current solution uses the batch transcription API and takes several hours to process a 1-hour video. The business requires near-real-time subtitles for live streaming events. You need to design a new solution that provides low-latency transcription. You have the following options: Option A: Use the batch transcription API with a higher priority queue. Option B: Use the Speech-to-text REST API for real-time streaming with the Speech SDK. Option C: Use the Azure AI Language API to transcribe audio from a file. Option D: Use Azure AI Video Indexer to generate subtitles.

A company is building a chatbot using Azure OpenAI Service to handle customer inquiries. The bot sometimes responds with incorrect or fabricated information. The team wants to ground the model responses using their own product documentation stored in Azure Cognitive Search. Which configuration should they implement?

You are designing a solution to analyze customer call transcripts using Azure AI Language. The solution must extract key phrases, detect sentiment per utterance, and identify the customer's intent (e.g., 'cancel subscription', 'technical support'). The data is stored in Azure Blob Storage and processed in near real-time. Which combination of Azure AI Language features and processing pattern should you use?

A healthcare organization needs to redact personally identifiable information (PII) from patient records before using them for research. They have large volumes of unstructured text in multiple languages. Which Azure AI service should they use?

Your company uses Azure AI Language to analyze customer feedback from surveys. The current pipeline extracts key phrases and sentiment. The data science team wants to identify emerging topics over time, such as new product complaints or feature requests. You need to modify the pipeline to track topic evolution. Which Azure AI Language feature should you enable?

You are building a multilingual customer support chatbot using Azure AI Language. The bot must understand user intents in English, Spanish, and French. You have pre-existing labeled data in English only. The solution should minimize manual labeling effort. Which approach should you recommend?

You are deploying an Azure AI Language custom text classification model. You need to ensure the model meets performance requirements before promoting it to production. Which two actions should you take? (Choose two.)

Your organization uses Azure AI Language to perform sentiment analysis and opinion mining on product reviews. You notice that the sentiment scores are often neutral even when the review text contains clearly positive or negative opinions. You suspect the model is not capturing the nuances. Which three actions could improve the sentiment analysis accuracy? (Choose three.)

You are building a solution to extract custom entities from legal contracts using Azure AI Language. You have a small set of labeled documents. Which two features should you use to build and improve the custom NER model? (Choose two.)

You are a developer at a large financial institution. The compliance team needs to automatically analyze quarterly earnings call transcripts to extract forward-looking statements (e.g., 'we expect revenue to grow') and flag any that are overly optimistic or lack necessary disclaimers. The transcripts are stored as text files in Azure Blob Storage. You need to design a solution using Azure AI Language services that meets the following requirements: 1) Extract all forward-looking statements from each transcript. 2) For each statement, determine if it contains optimistic language (e.g., 'strong growth', 'excellent performance') and if it includes a disclaimer (e.g., 'this is a forward-looking statement'). 3) Output a structured JSON file per transcript with the statements, optimism score, and disclaimer presence. 4) Minimize development effort and avoid custom machine learning model training. Which approach should you take?

Your company runs a global e-commerce platform. You are building a chatbot using Azure AI Language's conversational language understanding (CLU) to handle customer requests in multiple languages. The bot must support English, German, and Japanese. You have labeled training data in English only. The deadline is tight, and you want to minimize manual labeling. You also need to ensure that the bot can gracefully handle unsupported languages (e.g., French) by directing the user to a human agent. You have access to Azure AI Translator. Which approach should you take?

You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?

Your organization runs a popular news website. You want to use Azure AI Language to automatically generate summaries of news articles for the homepage. The summaries must be concise (under 100 words), extractive (selecting key sentences from the article), and available in both English and Spanish. You have a large corpus of articles in both languages. You need to implement a solution that requires minimal custom development and leverages Azure AI Language's prebuilt capabilities. Which approach should you take?

You are building a solution to analyze customer feedback from multiple sources: emails, chat logs, and survey responses. You need to detect the overall sentiment trend over time and identify the most frequently mentioned topics. The solution must also allow the business analyst to ask natural language questions about the data (e.g., 'Show me complaints about shipping in the last month'). You have all data in Azure Blob Storage. You need to implement a solution with minimal custom code. Which combination of Azure services should you use?

Your company uses Azure AI Language to process legal documents. You have built a custom NER model to extract parties, dates, and obligations. The model performs well on English documents but now you need to support French and German documents. You have no labeled data in those languages. You want to use the existing English model as a starting point. The solution must be cost-effective and avoid manual labeling as much as possible. You also need to ensure that the model can be retrained quickly when new document types are added. Which approach should you take?

A healthcare organization is deploying a solution using Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and support the following requirements: extract medication names, dosages, and frequencies; identify patient conditions; and recognize negated terms (e.g., 'no sign of infection'). Which THREE Azure AI Language features should the organization use?

A legal firm is using Azure AI Language to analyze contracts. They need to extract key clauses, parties involved, and dates. The solution must be customizable to their specific contract types. Which TWO Azure AI Language features should they use?

Refer to the exhibit. You have a Custom Question Answering project configured with the JSON shown. When you test the project in Azure AI Language Studio, the query 'How many vacation days do I get?' returns no answer. What is the most likely cause?

You are a developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. What should you do?

A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?

A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?

A legal compliance team needs to automatically redact personally identifiable information (PII) from legal documents before sharing them with external auditors. The documents are stored in Azure Blob Storage. The solution must use Azure AI Language to detect PII and then redact the identified entities. The redaction must be performed on the original documents, and the redacted versions must be saved to a separate container. You need to design a serverless solution with minimal latency. What should you do?

Refer to the exhibit. You have created a Text Analytics resource and retrieved its keys. You want to use the key1 to call the Sentiment Analysis API from a Python application. Which endpoint URL should you use?

Refer to the exhibit. You called the Key Phrase Extraction API on two documents. What is the total number of key phrases extracted?

Refer to the exhibit. You deployed a custom model for Language service. Which command should you run to check if the deployment is ready to accept inference requests?

Refer to the exhibit. You called the Named Entity Recognition API on a document. Which entity type is "Seattle"?

Refer to the exhibit. You receive this error when calling an Azure Cognitive Services API. What is the most likely cause?