Courseiva

CCNA Implement natural language processing solutions Questions

75 of 194 questions · Page 1/3 · Implement natural language processing solutions · Answers revealed

Practice these questionsDomain overviewAll questions
1
MCQhard

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?

A.The stringIndexType should be 'Utf16CodeUnit'
B.The API version does not support entity extraction
C.The endpoint is pointing to the wrong deployment
D.The model was not trained to recognize date entities
AnswerD

If the Date entity was not included in training, the model will not extract it.

Why this answer

The utterance includes 'next Tuesday' but the model might not have been trained with date entities or the date entity is not defined in the schema. The API request looks correct. The missing date is not due to a different endpoint or API version.

Practice this question →
2
MCQhard

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?

A.The training data does not contain examples with the 'dd/mm/yyyy' format
B.The dates exceed the maximum entity length
C.The language detection is incorrectly identifying the locale
D.The model is overfitting to invoice numbers
AnswerA

The model learns formats from training data; missing format leads to failure.

Why this answer

The correct answer is C because the model learns the date format from training data; if training data uses a different format, it will not recognize other formats. A (entity length) is not the issue. B (overfitting) would still recognize dates if format matches.

D (language detection) is irrelevant.

Practice this question →
3
MCQhard

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.Add more training data with annotated entities.
B.Use custom entity recognition with a prebuilt healthcare entity component.
C.Retrain the Text Analytics for Health model with additional labeled data.
D.Increase the confidence threshold for entity extraction.
AnswerB

This combines custom and prebuilt entities to improve accuracy.

Why this answer

Option B is correct because the engineer can use custom entity recognition with a prebuilt healthcare entity component, which leverages the existing Text Analytics for Health model's pre-trained entities (including medication names and dosages) without requiring additional training data. This approach combines the prebuilt healthcare model's high accuracy for medical entities with custom entity recognition to fine-tune extraction for specific clinical notes, improving performance without adding new annotated data.

Exam trap

The trap here is that candidates may assume 'Text Analytics for Health' is a trainable model (like custom NER) and choose Option C, not realizing it is a prebuilt, non-retrainable service that can only be extended via custom entity recognition with a prebuilt component.

How to eliminate wrong answers

Option A is wrong because adding more training data with annotated entities directly contradicts the requirement 'without adding new training data' and would require manual annotation effort. Option C is wrong because retraining the Text Analytics for Health model with additional labeled data is not supported—the Text Analytics for Health model is a prebuilt, non-trainable model that cannot be retrained with custom data; it can only be used as-is or combined with custom entity recognition. Option D is wrong because increasing the confidence threshold for entity extraction would reduce the number of entities returned, potentially missing valid medication names and dosages, and does not improve the underlying model's accuracy—it only filters results more aggressively.

Practice this question →
4
Multi-Selecteasy

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

Select 2 answers
A.Text translation.
B.Speech-to-text conversion.
C.Custom text classification.
D.Image captioning.
E.Key phrase extraction.
AnswersC, E

Custom text classification is a feature.

Why this answer

Options B and D are correct. Custom text classification and key phrase extraction are capabilities. Option A is incorrect because Azure AI Language does not do speech recognition.

Option C is incorrect because image captioning is Computer Vision. Option E is incorrect because text translation is Azure AI Translator.

Practice this question →
5
MCQhard

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?

A.Lower the confidence threshold
B.Increase the training hours
C.Increase the number of labeled documents, especially those containing the target entities
D.Switch to a different Azure AI Language feature
AnswerC

More examples improve recall.

Why this answer

Recall is the ability to find all relevant instances. Adding more labeled examples with the target entities will help the model recognize more patterns. Increasing training hours doesn't guarantee improvement; adjusting confidence threshold lowers precision; using a different service is not necessary.

Practice this question →
6
MCQmedium

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

A.Organization
B.Location
C.Person
D.City
AnswerB

Directly from the exhibit.

Why this answer

The JSON shows "type": "Location" for Seattle.

Practice this question →
7
MCQhard

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?

A.Use Azure AI Language's PII detection feature and a custom Azure Function to redact.
B.Use Azure AI Search with cognitive skills for PII detection.
C.Use Azure OpenAI to detect and redact PII.
D.Use Text Analytics for Health and then manually redact.
AnswerA

PII detection identifies PII, custom function redacts.

Why this answer

Option A is correct because Azure AI Language's PII detection can detect PII, and a custom function can redact it. Option B is incorrect because Azure AI Search does not redact. Option C is incorrect because Text Analytics for Health does not redact.

Option D is incorrect because Azure OpenAI may not have built-in redaction.

Practice this question →
8
Multi-Selectmedium

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.)

Select 2 answers
A.Evaluate the model on a held-out test set that was not used during training.
B.Review the confusion matrix to understand which classes are frequently misclassified.
C.Ensure the model achieves at least 95% accuracy on a cross-validation split.
D.Use the training set to compute accuracy and ensure it is above 90%.
E.Compare the model's performance to a baseline model that always predicts the most common class.
AnswersA, B

A held-out test set gives an unbiased estimate of real-world performance.

Why this answer

Options A and D are correct because evaluating on a held-out test set provides an unbiased performance estimate, and reviewing the confusion matrix helps identify specific misclassifications. Option B is wrong because using the training set for evaluation overestimates performance. Option C is wrong because precision and recall are more informative than just accuracy.

Option E is wrong because comparing to a baseline is good practice but not specific to model evaluation before promotion.

Practice this question →
9
MCQeasy

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?

A.Azure AI Content Safety
B.Azure AI Document Intelligence
C.Azure AI Translator
D.Azure AI Language - PII Detection
AnswerD

PII Detection identifies and can redact PII in multiple languages.

Why this answer

Option C is correct because Azure AI Language's PII detection and redaction supports multiple languages. Option A is wrong because Azure AI Content Safety is for harmful content. Option B is wrong because Document Intelligence is for forms.

Option D is wrong because Azure AI Translator is for translation.

Practice this question →
10
MCQmedium

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?

A.Use a single CLU project with English data only. Translate the English training data into German and Japanese using Azure AI Translator, then train a single multilingual model by including the translated data.
B.Use a single CLU project with English data only. Before calling CLU, translate non-English user input to English using Azure AI Translator. For unsupported languages, detect language and route to human agent.
C.Use a single CLU project with multilingual option enabled, train on English data only. Configure the bot to detect the language of user input; if it is English, German, or Japanese, route to CLU; otherwise, route to a human agent.
D.Build separate CLU projects for English, German, and Japanese. Label training data in each language by translating the English data using Azure AI Translator.
AnswerC

The multilingual option allows the model to predict intents in English, German, and Japanese without additional labeled data. Language detection ensures unsupported languages are handled appropriately.

Why this answer

Option D is correct because it uses the multilingual CLU model for English, German, and Japanese (the model can predict in those languages without additional labeled data) and uses a language detection step to route unsupported languages to a human agent. Option A is wrong because building separate projects for each language requires labeling in each language, which is not minimal effort. Option B is wrong because translating user input introduces latency and potential translation errors.

Option C is wrong because translating the English data and training separate models increases effort and may not work well due to translation quality.

Practice this question →
11
Multi-Selectmedium

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?

Select 3 answers
A.PII detection
B.Prebuilt NER for Healthcare
C.Prebuilt NER for Finance
D.Negation detection
E.Custom Named Entity Recognition (NER)
AnswersB, D, E

Prebuilt NER for Healthcare recognizes common clinical entities such as conditions, symptoms, and procedures.

Why this answer

The correct features are: Custom Named Entity Recognition (NER) to extract custom medical entities like medication names, dosages, and frequencies; Prebuilt NER for Healthcare to identify patient conditions and other clinical entities; and Negation detection to identify negated terms. The other options are not relevant: Prebuilt NER for Finance handles financial entities; PII detection extracts personal information, not clinical entities.

Practice this question →
12
MCQhard

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.Increase the training dataset size and retrain the model.
B.Use k-fold cross-validation during training.
C.Adjust the classification confidence threshold.
D.Split the data chronologically and ensure no overlapping data between train and test sets.
AnswerD

Chronological split prevents future data from leaking into training.

Why this answer

Option B is correct because evaluating the model with a holdout set that was never used in training is the standard way to detect data leakage. Option A is wrong because more training data won't fix leakage. Option C is wrong because cross-validation can still have leakage if leakage is in the data splitting.

Option D is wrong because adjusting confidence thresholds doesn't fix leakage.

Practice this question →
13
MCQmedium

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?

A.Use Azure AI Search with cognitive skills.
B.Create a custom question answering project and add the PDF as a source.
C.Use Azure OpenAI with a system prompt containing the PDFs.
D.Use the pre-built question answering in Azure AI Language.
AnswerB

Custom question answering can ingest PDFs.

Why this answer

Option C is correct because custom question answering can ingest PDFs and extract Q&A pairs. Option A is incorrect because Azure AI Search is for search, not question answering. Option B is incorrect because Azure OpenAI can answer questions but requires prompt engineering and may not use the PDFs directly.

Option D is incorrect because pre-built question answering is for common scenarios, not custom PDFs.

Practice this question →
14
MCQeasy

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?

A.Named entity recognition
B.Language detection
C.Key phrase extraction
D.Sentiment analysis
AnswerC

Key phrase extraction extracts the main concepts from text.

Why this answer

Key phrase extraction is the correct feature for identifying main points in text. Sentiment analysis detects positive/negative tone, entity recognition identifies named entities, and language detection identifies the language.

Practice this question →
15
MCQhard

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?

A.The response will contain an error because the deployment is not active.
B.The response will contain the entity 'OrderNumber' with value '#12345' because the hash is part of the entity.
C.The response will contain only the top intent, but no entities because the entity type is not recognized.
D.The response will contain the top intent 'CancelOrder' and the entity 'OrderNumber' with value '12345'.
AnswerD

The model correctly identifies intent and entity.

Why this answer

Option D is correct because the CLU model will predict the 'CancelOrder' intent and extract the entity 'OrderNumber' with value '12345'. Option A is wrong because the intent is indeed recognized. Option B is wrong because the entity is extracted.

Option C is wrong because the entity is correctly extracted as a number.

Practice this question →
16
MCQhard

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?

A.The training data size is insufficient.
B.The product code pattern is too complex for the model to learn.
C.The model is overfitting to the training data.
D.The labeling is inconsistent across the dataset.
AnswerC

Overfitting causes good performance on training data but poor on new data.

Why this answer

Option D is correct because the model likely overfits to the training data, failing to generalize to variations. Option A is wrong because insufficient training data would cause poor performance on development data too. Option B is wrong because labeling consistency issues would affect training and development performance.

Option C is wrong because the model is designed to extract patterns, but overfitting is the issue.

Practice this question →
17
MCQmedium

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.Deploy the Language service in a different Azure region
B.Increase the confidence threshold for sentiment classification
C.Create a custom sentiment analysis model using Custom Text Classification
D.Enable Key Phrase Extraction to preprocess the text
AnswerC

A custom model can be trained on domain-specific data to improve accuracy.

Why this answer

The correct answer is C because sentiment analysis accuracy can be improved by providing a custom model trained on domain-specific data. A (increasing confidence threshold) would reduce false positives but not address mislabeling. B (changing to a different region) does not affect model accuracy.

D (using key phrase extraction) is unrelated to sentiment.

Practice this question →
18
Multi-Selecteasy

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

Select 2 answers
A.Identifying domain-specific medical terms
B.Identifying names of people
C.Extracting key phrases from text
D.Identifying organization names
E.Determining overall sentiment of the text
AnswersB, D

Pre-built entity recognition includes Person entities.

Why this answer

Options A and D are correct. Pre-built entity recognition can identify people (A) and organizations (D). Option B is wrong because custom entities require custom NER.

Option C is wrong because key phrase extraction is a separate feature. Option E is wrong because sentiment analysis is a separate feature.

Practice this question →
19
MCQeasy

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

A.Sentiment analysis.
B.Key phrase extraction.
C.Extractive summarization.
D.Entity recognition.
AnswerC

Extracts key sentences to create a summary.

Why this answer

Option A is correct because the extractive summarization feature in Azure AI Language extracts key sentences from documents. Option B is wrong because key phrase extraction extracts phrases, not a summary. Option C is wrong because sentiment analysis determines sentiment, not summary.

Option D is wrong because entity recognition identifies entities.

Practice this question →
20
MCQhard

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

A.Date
B.OrderNumber
C.All entities need improvement.
D.ProductName
AnswerD

Low recall (0.65) indicates many ProductName entities are missed.

Why this answer

Option A is correct because ProductName has the lowest recall (0.65) and F1 score (0.76), indicating the model is missing many instances. Adding more positive examples for ProductName would likely improve recall. Option B is wrong because OrderNumber has high recall and F1.

Option C is wrong because Date has high precision and F1. Option D is wrong because all entities may not need improvement; ProductName is the weakest.

Practice this question →
21
Multi-Selectmedium

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

Select 2 answers
A.Use prebuilt entity recognition models instead of custom
B.Increase the number of training epochs
C.Enable diagnostic logging for audit trails
D.Configure data retention policies to delete data after processing
E.Anonymize or remove PII from training data
AnswersD, E

Retention policies ensure data is not stored longer than needed.

Why this answer

The correct answers are B and D. B ensures that training data does not contain PII that could be exposed. D ensures that processed data is not stored longer than necessary.

A (enabling logging) does not directly address privacy. C (using prebuilt models) may not be relevant. E (increasing compute) is unrelated.

Practice this question →
22
MCQmedium

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.Call a separate booking API with the extracted entities to complete the reservation.
B.Use the CLU response to directly book the flight via the Azure AI Language service.
C.Prompt the user to rephrase the request because the intent is ambiguous.
D.Send another request to CLU to confirm the booking details.
AnswerA

The application must use the extracted information to call an external API.

Why this answer

Option B is correct. After CLU extracts intent and entities, the application must call a booking API to complete the action. Option A is wrong because the CLU model does not perform actions.

Option C is wrong because the response already contains the entities. Option D is wrong because the intent is already identified.

Practice this question →
23
MCQmedium

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.Translator API.
B.Conversational language understanding (CLU) with multilingual project.
C.Language detection API.
D.Custom text classification model.
AnswerC

Identifies the language of the input text.

Why this answer

Option A is correct because language detection identifies the language of the input text, allowing routing to the correct model. Option B is wrong because translation changes the text but does not detect language. Option C is wrong because the multilingual CLU model handles multiple languages in one project but does not detect language.

Option D is wrong because the custom text classification model is for classification, not language detection.

Practice this question →
24
MCQmedium

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?

A.Use custom text classification for category and key phrase extraction for product name.
B.Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.
C.Use custom text classification for category and custom named entity recognition for product name extraction.
D.Use custom text classification for category and prebuilt named entity recognition for product name extraction.
AnswerC

Both custom text classification and custom NER are available in Azure AI Language and can be trained on the labeled dataset to meet the requirements.

Why this answer

Option C is correct because custom text classification can handle the classification task, and custom NER can extract product names, both within the same Azure AI Language resource. Option A is wrong because the prebuilt NER cannot extract custom product names. Option B is wrong because key phrase extraction may not reliably extract product names.

Option D is wrong because conversational language understanding is designed for conversational flows, not single-turn ticket classification.

Practice this question →
25
MCQmedium

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

A.Network connectivity issues.
B.The number of calls exceeded the rate limit for the service tier.
C.Authentication tokens expired.
D.Invalid API keys were used.
AnswerB

HTTP 429 indicates rate limiting.

Why this answer

Option C is correct because HTTP 429 indicates rate limiting, meaning the number of calls exceeded the allowed limit for the service tier. Option A is wrong because invalid API keys would return 401, not 429. Option B is wrong because authentication failures return 401 or 403.

Option D is wrong because network issues cause timeouts or 5xx errors, not 429.

Practice this question →
26
MCQhard

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?

A.Conversational Language Understanding (CLU)
B.Key Phrase Extraction
C.Prebuilt Text Classification API
D.Custom text classification (single or multi-label)
AnswerD

Custom text classification can be trained on your own categories and labels.

Why this answer

Option A is correct because Custom text classification is designed for custom categories and can handle multiple labels per document if needed. Option B is wrong because the built-in Text Classification API only supports predefined categories. Option C is wrong because Conversational Language Understanding is for intents and entities, not text classification.

Option D is wrong because Key Phrase Extraction is not a classification feature.

Practice this question →
27
Multi-Selecteasy

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

Select 2 answers
A.A set of labeled documents
B.A QnA Maker knowledge base
C.A project in Azure AI Language
D.A Language Understanding (LUIS) app
E.An Azure Functions app
AnswersA, C

Labeled documents are required for training.

Why this answer

A custom text classification project requires a project in Azure AI Language and a set of labeled documents for training. A LUIS app and a QnA maker knowledge base are different services. An Azure Functions app is not required.

Practice this question →
28
MCQhard

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.Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.
B.Use Azure Cognitive Search with semantic ranking to index the QnA pairs.
C.Add synonyms in the project for French and German terms to improve matching.
D.Deploy the same project to multiple regions and use traffic manager.
AnswerA

Separate projects ensure optimal language-specific models and accurate answers.

Why this answer

Custom Question Answering is language-specific. Each project is optimized for a single language. Mixing multiple languages in one project degrades performance because the model's language detection and matching algorithms are tuned for one language.

The correct approach is to create separate projects per language and route queries based on detected language. Option A is correct. Option B is incorrect because adding synonyms does not solve the fundamental language mismatch.

Option C is incorrect because the same data will still be mixed. Option D is incorrect because Cognitive Search does not provide the same QnA matching capabilities.

Practice this question →
29
MCQeasy

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

A.Language Detection
B.Named Entity Recognition
C.Key Phrase Extraction
D.Sentiment Analysis
AnswerD

Sentiment Analysis detects positive/negative sentiment.

Why this answer

Option A is correct because Sentiment Analysis is designed for that purpose. Option B is incorrect because Key Phrase Extraction extracts key phrases. Option C is incorrect because Entity Recognition identifies entities.

Option D is incorrect because Language Detection identifies language.

Practice this question →
30
MCQeasy

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.Prebuilt entity recognition component.
B.Key phrase extraction.
C.Custom named entity recognition (NER) component.
D.List entity in a conversational language understanding (CLU) project.
AnswerC

Allows training a model to extract custom entities.

Why this answer

Option C is correct because a custom named entity recognition (NER) component allows you to train a model to extract custom entities. Option A is wrong because the prebuilt entity recognition only recognizes common entities. Option B is wrong because a list entity in a conversational language understanding (CLU) project is for matching exact terms, not for training a model.

Option D is wrong because key phrase extraction does not extract entities.

Practice this question →
31
MCQhard

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.Create a separate custom NER project for French and train from scratch using French contracts.
B.Retrain the English model with a mix of English and French contracts.
C.Use Azure AI Translator to translate French contracts to English, then use the English model.
D.Use the multilingual option in Azure AI Language custom NER to extend the existing project to include French.
AnswerD

Multilingual projects allow extending to other languages leveraging existing training.

Why this answer

Option D is correct because Azure AI Language supports multilingual projects that can learn from pre-existing English data and transfer to French. Option A is wrong because it requires separate management. Option B is wrong because translation may lose legal nuances.

Option C is wrong because Translator adds latency and cost.

Practice this question →
32
MCQmedium

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?

A.Use Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.
B.Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.
C.Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.
D.Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.
AnswerB

Azure Cognitive Search can index the feedback with extracted metadata, and its semantic search can interpret natural language queries like 'complaints about shipping' and return relevant documents.

Why this answer

Option C is correct because Azure AI Language can perform sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic ranker can enable natural language querying over the indexed data. Option A is wrong because Azure OpenAI Service alone does not provide built-in analytics over stored data. Option B is wrong because Power BI is for visualization, not natural language querying over text.

Option D is wrong because Azure AI Language's question answering is for FAQ, not for querying indexed data.

Practice this question →
33
MCQhard

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?

A.Custom Named Entity Recognition in Azure AI Language
B.Pre-built Named Entity Recognition in Azure AI Language
C.Text Analytics for Health
D.Immersive Reader
AnswerA

Custom NER can be trained to recognize domain-specific entities.

Why this answer

Option B is correct because Custom NER allows you to train a model to recognize custom entities like clauses and parties. Option A is incorrect because pre-built NER only recognizes common entities. Option C is incorrect because text analytics for health is focused on medical data.

Option D is incorrect because the Immersive Reader is for reading support, not entity extraction.

Practice this question →
34
MCQhard

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

A.Set the includeUntranslated parameter to true
B.Train a custom translation model that ignores those terms
C.Post-process the output to revert translations of those terms
D.Provide a dictionary with the terms and their translations set to the same word
AnswerD

The dictionary allows forcing a specific translation; setting the target same as source prevents translation.

Why this answer

Azure AI Translator supports a dictionary to specify words that should not be translated. The 'includeUntranslated' parameter does not exist. Post-editing is inefficient.

Using a custom model requires training data.

Practice this question →
35
Multi-Selecteasy

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.)

Select 2 answers
A.Use active learning to automatically suggest new labels from unlabeled documents.
B.Use the prebuilt NER model as a base and extend it with custom entities.
C.Configure an orchestration workflow to route documents to the best model.
D.Add synonyms for each entity to improve recognition of variations.
E.Extract key phrases from the documents and use them as features.
AnswersA, D

Active learning identifies uncertain predictions and suggests them for labeling, reducing manual effort.

Why this answer

Options A and B are correct because active learning helps label additional data efficiently, and using synonyms improves entity recognition for varied phrasings. Option C is wrong because key phrase extraction is not part of custom NER training. Option D is wrong because prebuilt NER cannot be extended with custom entities.

Option E is wrong because orchestration workflow is for intent detection, not NER.

Practice this question →
36
MCQhard

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?

A.https://mytextanalytics.cognitiveservices.azure.com/sentiment/v3.1
B.https://mytextanalytics.api.cognitive.microsoft.com/text/analytics/v3.1/sentiment
C.https://mytextanalytics.cognitiveservices.azure.com/analyze
D.https://mytextanalytics.cognitiveservices.azure.com/text/analytics/v3.1/sentiment
AnswerD

This is the correct endpoint for sentiment analysis.

Why this answer

The exhibit shows the endpoint is https://mytextanalytics.cognitiveservices.azure.com/. For Text Analytics, the sentiment endpoint is appended with /text/analytics/v3.1/sentiment. The correct URL is https://mytextanalytics.cognitiveservices.azure.com/text/analytics/v3.1/sentiment.

Practice this question →
37
MCQeasy

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?

A.Language detection API.
B.Entity recognition API.
C.Sentiment analysis API.
D.Key phrase extraction API.
AnswerC

Returns sentiment labels and scores.

Why this answer

Option B is correct because the sentiment analysis API in Azure AI Language returns sentiment labels (positive, negative, neutral) and confidence scores. Option A is wrong because the language detection API detects language. Option C is wrong because the entity recognition API extracts entities.

Option D is wrong because the key phrase extraction API extracts key phrases.

Practice this question →
38
MCQeasy

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?

A.The text is negative overall.
B.The text contains both positive and negative sentiments.
C.The text is neutral overall.
D.The analysis is inconclusive.
AnswerB

Mixed label indicates both positive and negative.

Why this answer

Option C is correct because a 'mixed' label indicates that the text contains both positive and negative sentiments, even if the overall score leans positive. Option A is wrong because 'mixed' does not indicate a neutral sentiment. Option B is wrong because the overall score is positive, not negative.

Option D is wrong because a 'mixed' label is a valid output.

Practice this question →
39
Multi-Selecteasy

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?

Select 3 answers
A.Key Phrase Extraction
B.Language Detection
C.Sentiment Analysis
D.PII Detection
E.Entity Recognition
AnswersA, C, E

Extracts key phrases.

Why this answer

Options A, B, and C are correct because Sentiment Analysis, Key Phrase Extraction, and Entity Recognition are the operations that provide the required insights. Option D is wrong because Language Detection detects the language, not needed here. Option E is wrong because PII Detection detects personal information, not needed here.

Practice this question →
40
MCQhard

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'?

A.Laptop is a type of Electronics.
B.There is no defined relationship.
C.Electronics is a type of Laptop.
D.Laptop is a part of Electronics.
AnswerA

The relations show InstanceOf, meaning Laptop is an instance of Electronics.

Why this answer

Option A is correct. The relation type 'InstanceOf' indicates that Laptop is an instance of (i.e., a type of) Electronics. Option B is wrong because Laptop is not a part of Electronics in the model.

Option C is wrong because the relation is not 'HasPart'. Option D is wrong because the relation is defined.

Practice this question →
41
MCQmedium

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?

A.Reduce the number of categories to simplify the classification.
B.Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.
C.Increase the number of training epochs to further train the model.
D.Use a different classification algorithm, such as a neural network.
AnswerB

More training data for the problematic category improves model performance.

Why this answer

Option B is correct because adding more labeled examples for the 'billing' category, especially those that are mixed with other categories, will help the model learn to distinguish them better. Option A is wrong because reducing the number of categories may not address the specific confusion. Option C is wrong because increasing the training epochs may lead to overfitting.

Option D is wrong because using a different algorithm is not an option in Azure AI Language's custom text classification.

Practice this question →
42
MCQeasy

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.Option C
B.Option B
C.Option D
D.Option A
AnswerB

Speech SDK with real-time streaming provides low-latency transcription.

Why this answer

Option B is correct. The Speech SDK with real-time streaming provides low-latency transcription suitable for live events. Option A is wrong because batch transcription is not near-real-time.

Option C is wrong because Azure AI Language does not transcribe audio. Option D is wrong because Video Indexer is for indexing, not real-time streaming.

Practice this question →
43
Multi-Selectmedium

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

Select 2 answers
A.Add more varied utterances to each intent.
B.Reduce the number of intents.
C.Use the 'Evaluate' feature to review model predictions.
D.Change the Azure region of the resource.
E.Disable active learning.
AnswersA, C

More utterances improve accuracy.

Why this answer

Options B and D are correct. Adding more utterances improves accuracy. Using the 'Evaluate' feature helps identify issues.

Option A is incorrect because reducing the number of intents may not help. Option C is incorrect because changing to a different region doesn't affect performance. Option E is incorrect because disabling active learning reduces performance.

Practice this question →
44
MCQhard

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.Switch to a prebuilt text classification model
B.Increase the number of training epochs
C.Reduce the confidence threshold for classification
D.Add more labeled data from actual production tickets
AnswerD

Diverse data helps the model learn patterns present in production.

Why this answer

The correct answer is C because adding more diverse training data from production helps the model generalize. A (increasing the number of training epochs) may lead to overfitting. B (reducing the confidence threshold) does not address generalization.

D (using a prebuilt model) may not fit the custom categories.

Practice this question →
45
MCQeasy

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

A.Azure AI Language - Key Phrase Extraction
B.Azure AI Language - Named Entity Recognition
C.Azure AI Language - Sentiment Analysis
D.Azure AI Language - Language Detection
AnswerC

Sentiment Analysis returns sentiment scores and labels.

Why this answer

Azure AI Language provides Sentiment Analysis as a built-in feature. Language Detection, Key Phrase Extraction, and NER are different capabilities.

Practice this question →
46
Multi-Selectmedium

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

Select 2 answers
A.Ensure that training examples for different labels do not have overlapping content.
B.Use a stratified split of training and testing data.
C.Oversample the minority classes to balance the dataset.
D.Remove all stop words from the training data.
E.Remove examples with neutral sentiment to focus on positive and negative classes.
AnswersA, B

Overlapping content confuses the model.

Why this answer

Option A is correct because overlapping content between labels (e.g., the same text appearing in both 'positive' and 'negative' training examples) confuses the custom text classification model, leading to poor decision boundaries. Azure Cognitive Service for Language uses a multi-class or multi-label classifier that learns distinct patterns for each label; overlapping content introduces ambiguity, reducing precision and recall. Ensuring distinct, non-overlapping training examples per label helps the model learn clear, separable features.

Exam trap

The trap here is that candidates often confuse general data preprocessing techniques (like oversampling or stop word removal) with the specific optimization requirements of Azure Cognitive Service for Language's custom text classification, where the service's internal architecture already handles many of these concerns, and the key optimization is ensuring label distinctness and proper data splitting.

Practice this question →
47
Multi-Selectmedium

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

Select 2 answers
A.Use a balanced dataset with similar numbers of examples for each entity.
B.Increase the training time of the model.
C.Reduce the number of entity types to simplify the model.
D.Label more examples with entity annotations.
E.Use only prebuilt entity types to avoid training from scratch.
AnswersA, D

Balanced data prevents bias towards certain entities.

Why this answer

Options A and C are correct because labeling more examples with entity annotations provides more training data, and using a balanced dataset helps the model learn all entities equally. Option B is wrong because increasing the training time is not a user-controlled parameter in Azure AI Language. Option D is wrong because using only prebuilt entities does not improve a custom model.

Option E is wrong because reducing the number of entities may lose needed information.

Practice this question →
48
MCQhard

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?

A.Use the prebuilt named entity recognition (NER) to identify entities related to financial terms, then apply sentiment analysis to the entire transcript to determine overall optimism.
B.Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.
C.Use custom question answering to create a knowledge base of typical forward-looking statements and query the transcript for matches.
D.Use key phrase extraction to identify important phrases, then run sentiment analysis on each sentence to detect optimism.
AnswerB

Custom NER can be trained to identify forward-looking statements; custom text classification can then analyze each statement for optimism and disclaimer. Both are available in Azure AI Language and require only labeled data, not custom ML training.

Why this answer

Option B is correct because it combines custom NER to extract forward-looking statements and custom text classification to classify each statement for optimism and disclaimer presence, both using Azure AI Language's pre-built capabilities that require only labeling effort. Option A is wrong because the prebuilt entity recognition does not include forward-looking statements as an entity type. Option C is wrong because key phrase extraction cannot identify specific statements, and sentiment analysis gives an overall score, not per-statement.

Option D is wrong because question answering is designed for Q&A, not extraction and classification of statements.

Practice this question →
49
MCQhard

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?

A.Use QnA Maker with a custom question-answer pair for each intent and entity.
B.Use the Language Understanding (LUIS) service with a single call for both intent and entity extraction.
C.Use two separate calls to the Azure AI Language API: one for intent recognition and one for entity extraction.
D.Use the Conversational Language Understanding (CLU) feature of Azure AI Language, which supports both intent and entity extraction in a single API call.
AnswerD

CLU combines both tasks, minimizing latency.

Why this answer

Option B is correct because using the Conversational Language Understanding (CLU) feature of Azure AI Language with a single API call to trigger both intent detection and entity extraction minimizes latency by combining both tasks in one request. Option A is wrong because using separate calls for intent and entity increases latency. Option C is wrong because LUIS is being replaced by CLU and does not support the latest features.

Option D is wrong because QnA Maker is for FAQ-style Q&A, not for intent recognition.

Practice this question →
50
MCQmedium

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

A.Only the first sentence because the document is short.
B.An abstractive summary generated from the text.
C.The three sentences ranked by confidence score.
D.The three sentences in the order they appear in the document.
AnswerD

sortBy: Offset returns sentences in original order.

Why this answer

Option A is correct because the request specifies 'sortBy': 'Offset', which means sentences are returned in the order they appear in the original text, not by rank. Option B is wrong because sentences are not ranked by confidence when sortBy is Offset. Option C is wrong because the request requests 3 sentences.

Option D is wrong because the summary is not abstractive.

Practice this question →
51
MCQmedium

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.Use the prebuilt NER model and map the recognized entities to the required types.
B.Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
C.Use Azure Logic Apps to call the Text Analytics API and post-process the results.
D.Train a custom NER model for genes and use prebuilt NER for chemicals.
AnswerB

Custom NER allows training a model tailored to the specific entities using the labeled data.

Why this answer

Custom NER in Azure AI Language is designed for training custom entity extraction models with labeled data. It supports training from scratch using your own dataset. Option A is correct.

Option B is wrong because prebuilt models do not cover specialized entities. Option C is wrong because using a different service like Logic Apps adds complexity. Option D is wrong because two models would be redundant and more work.

Practice this question →
52
MCQeasy

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

A.The text does not contain any recognized entities
B.The API version is incorrect
C.The document language should be 'es' for Spanish
D.The endpoint URL is for the wrong region
AnswerA

The text is a common pangram without named entities, so the API correctly returns none.

Why this answer

The text 'The quick brown fox jumps over the lazy dog.' contains no named entities like people, places, or organizations. The API version and endpoint appear correct. The document format is fine.

Practice this question →
53
MCQmedium

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?

A.Use Azure AI Translator to translate all input to English before sending to CLU.
B.Add utterances in all three languages to the CLU project and enable multi-lingual detection.
C.Use the Translator service to detect language and route to language-specific CLU endpoints.
D.Create separate CLU projects for each language.
AnswerB

CLU can be trained with multiple languages and detect language automatically.

Why this answer

Option C is correct because you should add utterances in all target languages to the CLU project and enable multi-lingual detection. Option A is wrong because separate projects per language are not recommended; CLU supports multi-lingual models. Option B is wrong because translating on the fly increases complexity and may not capture nuances.

Option D is wrong because the Translator service does not understand intents.

Practice this question →
54
MCQhard

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?

A.The training data changes are not saved
B.The project needs to be rebuilt from scratch
C.The endpoint has a caching issue
D.The new model is not yet deployed; you must deploy it to the endpoint
AnswerD

Redeploying the model to the same endpoint updates the active model.

Why this answer

The endpoint might still be pointing to the old trained model. You need to update the endpoint assignment to the new model version. Rebuilding the project or deleting the endpoint would be disruptive.

Caching doesn't apply in this context.

Practice this question →
55
Multi-Selecthard

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?

Select 3 answers
A.Custom models require a large set of labeled training data.
B.Custom models always have faster response times.
C.The pre-built model may not accurately handle domain-specific jargon.
D.Pre-built models cannot be used in containers.
E.Pre-built models offer multilingual support out-of-the-box.
AnswersA, C, E

Custom models need labeled data for training.

Why this answer

Options A, C, and E are correct. Pre-built models may not capture domain-specific language (A). Custom models require labeled data (C).

Azure AI Language pre-built supports multiple languages but may have lower accuracy for specialized domains (E). Option B is wrong because response time is generally similar. Option D is wrong because both can be deployed to containers.

Practice this question →
56
MCQeasy

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.Entity Recognition
B.Extractive Summarization
C.Key Phrase Extraction
D.Sentiment Analysis
AnswerB

Extractive Summarization extracts important sentences to form a summary.

Why this answer

The correct answer is B because Extractive Summarization extracts key sentences from the text. A (Key Phrase Extraction) extracts phrases, not a summary. C (Entity Recognition) identifies entities.

D (Sentiment Analysis) determines sentiment.

Practice this question →
57
Multi-Selectmedium

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

Select 3 answers
A.Integrate LUIS for intent detection.
B.Set up a multi-turn extraction policy for follow-up questions.
C.Enable active learning to improve answer suggestions.
D.Add chit-chat to handle common conversational phrases.
E.Configure a single-turn extraction policy.
AnswersB, C, D

Multi-turn extraction is needed for conversation flow.

Why this answer

Option B is correct because multi-turn extraction is a core feature of custom question answering that allows the system to handle follow-up questions by maintaining context across turns. This is essential for conversational flows where a user's subsequent query depends on the previous answer, and it is configured via the project settings in Language Studio.

Exam trap

The trap here is that candidates often confuse the need for LUIS integration (Option A) with question answering, not realizing that custom question answering is a standalone service that does not require intent detection from LUIS.

Practice this question →
58
MCQhard

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?

A.az cognitiveservices account deployment list --resource-group myRG --name myLangService
B.az cognitiveservices account deployment delete --resource-group myRG --name myLangService --deployment-name myDeployment
C.az cognitiveservices account deployment show --resource-group myRG --name myLangService --deployment-name myDeployment
D.az cognitiveservices account deployment create --resource-group myRG --name myLangService --deployment-name myDeployment
AnswerC

This shows the current provisioning state.

Why this answer

The show command confirms provisioningState is Succeeded, indicating readiness. The create command only returns the initial status.

Practice this question →
59
MCQhard

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

A.Use QnA Maker with multiple QnA pairs.
B.Use a single Conversational Language Understanding project with multiple intents.
C.Use the orchestration workflow feature in Conversational Language Understanding.
D.Use Azure Bot Service with multiple dialogs.
AnswerC

Orchestration workflow routes to multiple intents.

Why this answer

Option B is correct because orchestration workflow in Conversational Language Understanding allows routing to multiple intents. Option A is incorrect because a single project with multiple intents cannot handle multiple intents in one utterance. Option C is incorrect because QnA Maker is for question answering, not multiple intents.

Option D is incorrect because Azure Bot Service is a framework, not an NLP feature.

Practice this question →
60
MCQeasy

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

A.Azure AI Language
B.Azure AI Translator (Document Translation)
C.Custom Translator in Azure AI Translator
D.Azure OpenAI Service with GPT-4
AnswerB

Document Translation preserves formatting.

Why this answer

Option A is correct because Azure AI Translator supports document translation with formatting preservation. Option B is incorrect because Azure OpenAI can translate but may not preserve formatting. Option C is incorrect because Custom Translator is for custom models, not general translation.

Option D is incorrect because Azure AI Language does not include translation.

Practice this question →
61
Multi-Selecthard

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

Select 3 answers
A.Domain-specific vocabulary coverage
B.Time to develop and deploy
C.Need for a trained endpoint
D.Availability of labeled training data
E.Model size and memory footprint
AnswersA, B, D

Pre-built may miss domain terms; custom can include them.

Why this answer

Pre-built models are faster to deploy and require no labeled data, but may not cover domain-specific terms. Custom models require labeled data and training time but offer higher accuracy for niche domains. Model size is not a typical consideration; both can be scaled.

Custom models require a trained endpoint. Pre-built models are often cheaper for generic use.

Practice this question →
62
MCQhard

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.

A.Leverage the Text Analytics for Health API to extract entities from the support tickets.
B.Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
C.Use the Prebuilt Entity Extraction skill in Azure AI Search to extract order numbers and categories from the text.
D.Use the Conversational Language Understanding (CLU) project type to train a model for both intent and entity extraction.
AnswerB

Custom NER with prebuilt components combines ease of use with flexibility for custom categories.

Why this answer

Option B is correct because the prebuilt entity components in the custom NER project can recognize common patterns like order numbers without needing to write custom regex or rules. Option A is wrong because the built-in Prebuilt Entity Extraction skill in Azure AI Search is for indexing, not for the Language service. Option C is wrong because the Conversational Language Understanding (CLU) project type is for intent classification and entity extraction, but it requires a lot of data and training.

Option D is wrong because the Text Analytics for Health is for medical entities and would not work for order numbers.

Practice this question →
63
Multi-Selecteasy

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

Select 3 answers
A.Image captioning
B.Speech-to-text
C.Sentiment analysis
D.Entity recognition
E.Key phrase extraction
AnswersC, D, E

Analyzes sentiment.

Why this answer

Options A, B, and C are correct because these are standard features. Option D is wrong because image analysis is part of Azure AI Vision. Option E is wrong because speech-to-text is part of Azure AI Speech.

Practice this question →
64
MCQmedium

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.Use actions to call external APIs for context.
B.Use topics with slots to collect information across turns.
C.Use global variables to store conversation state.
D.Use entities to capture user input.
AnswerB

Topics with slots manage multi-turn context.

Why this answer

Option B is correct because Copilot Studio uses topics with slots to manage context and gather information across turns. Option A is wrong because entities alone don't manage conversation flow. Option C is wrong because variables are used to store data but slots are for context.

Option D is wrong because actions are for API calls, not conversation management.

Practice this question →
65
MCQeasy

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?

A.Sentiment Analysis
B.Entity Recognition
C.Language Detection
D.Key Phrase Extraction
AnswerA

Sentiment Analysis directly provides sentiment labels and scores.

Why this answer

Option A is correct because Sentiment Analysis is the feature that returns sentiment labels and scores. Option B is wrong because Key Phrase Extraction identifies key phrases but not sentiment. Option C is wrong because Language Detection identifies the language.

Option D is wrong because Entity Recognition identifies entities.

Practice this question →
66
MCQmedium

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.One of the reviews exceeds the maximum character limit for a single document.
B.The reviews contain characters that are not valid UTF-8.
C.The request contains more than 5 documents.
D.The reviews are written in a language not supported by the service.
AnswerA

The service limits each document to 5,120 characters.

Why this answer

The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.

Exam trap

The trap here is that candidates often assume the 400 error is due to unsupported languages or encoding issues, but the actual constraint is the per-document character limit, which is explicitly documented in the service's input specifications.

How to eliminate wrong answers

Option B is wrong because the service automatically handles UTF-8 encoding validation and would return a different error (e.g., 400 with 'InvalidRequestContent') if characters were not valid UTF-8, but the question states the reviews are standard customer reviews, making invalid UTF-8 unlikely. Option C is wrong because the API supports up to 10 documents per request (not 5), so a request with more than 5 documents would still succeed unless it exceeds the 10-document limit. Option D is wrong because unsupported languages typically result in a successful response with empty key phrases or a warning, not a 400 error; the service supports over 40 languages for key phrase extraction.

Practice this question →
67
MCQmedium

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?

A.The model's confidence threshold is set too low.
B.The training data is not representative of the new tickets.
C.There is data leakage between the training and test sets.
D.The model is overfitting to the training data.
AnswerD

Overfitting leads to high training accuracy but poor generalization.

Why this answer

Option C is correct because high accuracy on the test set but low accuracy on new data indicates overfitting, where the model learns the training data too well and fails to generalize. Option A is wrong because accuracy on the test set is high, so insufficient training data is not indicated. Option B is wrong because low confidence scores are not mentioned; overfitting can still produce high confidence.

Option D is wrong because data leakage would cause inflated accuracy on the test set, but the test set accuracy is already high and the problem is with new data.

Practice this question →
68
MCQhard

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.Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.
B.Enable active learning in the project settings and periodically publish the updated knowledge base.
C.Increase the number of FAQ documents in the knowledge base.
D.Manually add alternate question phrases to the knowledge base for each QnA pair.
AnswerB

Active learning automatically suggests alternative phrasings based on user queries.

Why this answer

Option C is correct because enabling active learning allows the system to learn from user interactions and suggest alternative phrasings. Option A is wrong because increasing document count doesn't help with paraphrasing. Option B is wrong because additional training is not a built-in feature; active learning is.

Option D is wrong because Azure AI Bot Service is for bot hosting, not improving answer coverage.

Practice this question →
69
Multi-Selectmedium

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

Select 2 answers
A.Create a Custom Question Answering project in Azure AI Language.
B.Train a custom model using the Azure AI Language training API.
C.Create a QnA Maker service in the Azure portal.
D.Deploy the project to an Azure AI Language resource.
E.Publish the project to a web app bot.
AnswersA, D

This is the first step.

Why this answer

Options B and D are correct because you need to create a project and deploy the model for production use. Option A is wrong because the QnA Maker service is deprecated; Azure AI Language uses the Custom Question Answering feature. Option C is wrong because training data can be provided in multiple formats and the model is trained automatically.

Option E is wrong because publishing to a web app bot is optional.

Practice this question →
70
MCQmedium

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.projectName
B.multilingual
C.language
D.confidenceThreshold
AnswerB

Setting multilingual to true enables support for multiple languages.

Why this answer

The correct answer is D because the 'multilingual' parameter set to true enables support for multiple languages. A (confidenceThreshold) is for prediction confidence. B (projectName) is just the name.

C (language) sets the primary language but does not enable multilingual support.

Practice this question →
71
MCQhard

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

A.The API endpoint is incorrect.
B.The subscription key has expired.
C.The subscription key is from a different region.
D.The subscription key is not valid for this Cognitive Services resource.
AnswerD

Inner error explicitly states that.

Why this answer

The error clearly states invalid subscription key. The inner error confirms it's not valid for the resource.

Practice this question →
72
MCQeasy

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.Set the 'category' parameter to 'general' to use a standard translation model.
B.Set the 'scope' parameter to 'document' to ensure context-aware translation.
C.Set the 'formality' parameter to the desired level (e.g., 'formal' or 'informal').
D.Set the 'language' parameter to 'fr' and the 'from' parameter to 'en'.
AnswerC

The formality parameter controls the tone of the translation.

Why this answer

Option C is correct because Azure AI Translator provides a 'formality' parameter that allows you to specify the desired level of formality (e.g., 'formal' or 'informal') in the translated text. This parameter directly controls the tone and register of the output, ensuring that the translation preserves the original email's tone and formality, which is critical for customer communications.

Exam trap

The trap here is that candidates often confuse the 'formality' parameter with language or category settings, mistakenly thinking that simply specifying the target language (Option D) or using a general category (Option A) is sufficient to control tone, when in fact the formality parameter is the only dedicated mechanism for this purpose.

How to eliminate wrong answers

Option A is wrong because the 'category' parameter is used to select a custom translation model or domain (e.g., 'general' for standard translations), but it does not control tone or formality; it affects terminology and style based on the training domain. Option B is wrong because Azure AI Translator does not have a 'scope' parameter; context-aware translation for documents is handled by the Document Translation feature, not by a request parameter in the standard Translate operation. Option D is wrong because while setting 'language' to 'fr' and 'from' to 'en' is necessary for specifying the source and target languages, it does not address the requirement to preserve tone and formality; it only defines the language pair.

Practice this question →
73
MCQeasy

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

A.Document summarization
B.Key phrase extraction
C.Entity recognition
D.Sentiment analysis
AnswerA

Document summarization provides a concise summary of the document.

Why this answer

Option B is correct because Document summarization (extractive or abstractive) is designed to summarize documents. Option A is wrong because Key phrase extraction extracts phrases, not summaries. Option C is wrong because Entity recognition identifies entities.

Option D is wrong because Sentiment analysis gives sentiment scores.

Practice this question →
74
Drag & Dropmedium

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

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order

Why this order

First, have labeled data ready, then create the resource, train the model, evaluate, and deploy.

Practice this question →
75
MCQhard

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?

A.The SKU S0 does not support API calls from applications
B.The resource kind is set incorrectly for Language Service
C.The network ACLs block all traffic because no IP rules are defined
D.The custom subdomain name is invalid
AnswerC

Deny default with no allowed IPs blocks all calls.

Why this answer

The correct answer is A because the template sets 'defaultAction' to 'Deny' with no IP rules, blocking all network access. B (S0 SKU) supports API calls. C (custom subdomain) does not cause 403.

D (kind) is correct for Language Service.

Practice this question →
Page 1 of 3 · 194 questions totalNext →

Ready to test yourself?

Try a timed practice session using only Implement natural language processing solutions questions.

Start 20-question session