AI-102 Implement natural language processing solutions Practice Test 17 — 15 Questions

Question 1

You are building an application that must detect and redact personally identifiable information (PII) from free-text support tickets before storing them. The tickets are written in English, and you need to identify entities such as names, phone numbers, and email addresses, and replace them with asterisks. You plan to use the Azure AI Language service. Which API endpoint should you call?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

POST /language/:analyze-text with kind set to "PiiEntityRecognition" in the request body.. The PiiEntityRecognition kind in the analyze-text endpoint is purpose-built for identifying and redacting personal data. It returns both the detected entities and a redactedText field where entities are masked with asterisks. The other kinds either detect different entity types or serve different NLP tasks, and none provide the required redaction behavior.

Answer

POST /language/:analyze-text with kind set to "KeyPhraseExtraction" in the request body.

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POST /language/:analyze-text with kind set to "EntityRecognition" in the request body.

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POST /language/:analyze-conversations with kind set to "ConversationPII" in the request body.

Question 2

You are building a solution that uses Azure AI Language to summarize long customer support emails. The emails are in English, and each email can be up to 10,000 characters. You need to generate a concise summary that preserves the most important sentences and returns the summary along with the original sentences it selected. Which feature and approach should you use?

Accepted Answer

Use extractive summarization and split each email into chunks that respect the per-document character limit before sending requests.. Extractive summarization returns the most important original sentences, which satisfies the requirement to preserve sentences and identify which ones were selected. Since the per-document character limit is below 10,000 characters, each email must be split into compliant chunks before calling the API to avoid request failures.

Answer

Use key phrase extraction and concatenate the returned phrases into a summary.

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Use abstractive summarization and send the entire 10,000-character email in a single request.

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Use named entity recognition to extract the main subjects and build a summary from the entities.

Question 3

You are developing a conversational language understanding (CLU) project in Azure AI Language. The project must recognize user intents and extract entities from utterances. You have trained and deployed the model. You need to call the prediction API from a client application. The deployment name is 'prod' and the project name is 'HRBot'. Which URL should you use to send an utterance for prediction?

Accepted Answer

https://<resource-name>.cognitiveservices.azure.com/language/analyze-conversations/projects/HRBot/deployments/prod/:analyze-conversations?api-version=2023-04-01. The CLU prediction endpoint requires the project name and deployment name as path segments under the analyze-conversations operation, along with a supported API version. The correct URL includes /language/analyze-conversations/projects/{project}/deployments/{deployment}/:analyze-conversations?api-version=2023-04-01. Query-string routing, the wrong operation name, or an outdated API version will not work for CLU predictions.

Answer

https://<resource-name>.cognitiveservices.azure.com/language/:analyze-conversations?projectName=HRBot&deploymentName=prod&api-version=2023-04-01

Answer

https://<resource-name>.cognitiveservices.azure.com/language/analyze-text/projects/HRBot/deployments/prod?api-version=2023-04-01

Answer

https://<resource-name>.cognitiveservices.azure.com/language/analyze-conversations/projects/HRBot/deployments/prod?api-version=2022-05-01

Question 4

You are developing a solution that uses Azure AI Language to detect the language of incoming text messages. The messages may contain mixed languages within a single document. You need to ensure the API returns the detected language and a confidence score for each document. Which request should you make?

Accepted Answer

POST to the analyze-text endpoint with kind set to LanguageDetection.. Language detection is performed by setting the kind parameter to LanguageDetection in the analyze-text request. This returns the detected language and a confidence score for each document. Other tasks like EntityRecognition, KeyPhraseExtraction, and SentimentAnalysis serve different purposes and do not provide language detection.

Answer

POST to the analyze-text endpoint with kind set to EntityRecognition.

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POST to the analyze-text endpoint with kind set to KeyPhraseExtraction.

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POST to the analyze-text endpoint with kind set to SentimentAnalysis.

Question 5

Your Azure AI Language custom question answering project answers product questions from a knowledge base. Users report that the bot returns the same generic answer regardless of how they phrase a question, even when the knowledge base contains the correct information. You need to diagnose why the model is not matching semantically similar user phrasings to the right answer. What should you do first?

Accepted Answer

Review the project's default answer and confirm the deployed model is the one being queried by the bot. When every phrasing produces the same generic response, the runtime is almost certainly falling back to the project's default answer, which occurs when no question-answer pair scores above the threshold or when the bot queries a deployment that lacks the trained model. Inspecting the default answer and verifying the deployment target isolates configuration problems before any content changes are made. Content remedies such as alternate questions or more documents address different symptoms.

Answer

Enable active learning and wait for the model to retrain automatically on user queries

Answer

Add alternate questions to each question-answer pair in the project

Answer

Increase the number of documents in the knowledge base and re-index the project

Question 6

You are using Azure AI Language's custom question answering feature. Your knowledge base contains 500 FAQ pairs. Users report that the bot returns irrelevant answers when their questions are phrased differently from the stored FAQs. You want to improve the bot's ability to match paraphrased questions without retraining a full model. Which action should you take?

Accepted Answer

Add alternative question phrasings to each FAQ pair in the knowledge base.. Custom question answering learns from the question-answer pairs and their alternative phrasings. Adding alternative questions provides the model with more surface forms of the same intent, which improves semantic matching for paraphrased user queries without requiring a separate model training process.

Answer

Enable the 'chit-chat' personality in the project settings to make responses more conversational.

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Increase the confidence threshold in the project settings to filter low-scoring answers.

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Switch the project to use a custom text classification model instead of question answering.

Question 7

A company wants to build a conversational interface that can understand user intents and extract key information from utterances, such as booking a flight to a specific city on a specific date. They need to implement this using Azure AI Language. Which feature should they use?

Accepted Answer

Conversational language understanding (CLU). Conversational language understanding (CLU) is the Azure AI Language feature built for intent recognition and entity extraction in conversational input. It supports custom intents and entities, making it ideal for building chatbots and voice assistants that need to parse user requests like flight bookings. Other features lack intent understanding or custom entity extraction.

Answer

Custom text classification

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Named entity recognition (NER)

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Key phrase extraction

Question 8

A company wants to build a solution that summarizes long support call transcripts into concise paragraphs. The transcripts are stored as plain text in Azure Blob Storage. You plan to use the summarization feature in Azure AI Language. Which summarization type should you use to generate a short paragraph that captures the main points of each transcript?

Accepted Answer

Abstractive summarization. Abstractive summarization in Azure AI Language generates new, concise text that captures the essence of the input, making it suitable for creating short paragraphs from long transcripts. Extractive summarization only selects existing sentences, key phrase extraction returns terms, and named entity recognition identifies entities. Only abstractive summarization produces the rephrased, paragraph-style summary required.

Answer

Extractive summarization

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Key phrase extraction

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Named entity recognition

Question 9

You are using Azure AI Language's question answering feature to build a bot that answers employee questions from a set of HR policy documents. You need to ensure the bot provides accurate answers and can handle follow-up questions. Which two actions should you take? (Choose two.)

Accepted Answer

Enable multi-turn extraction to create follow-up prompts for related questions.. To improve accuracy and handle follow-ups in question answering, you should add alternate questions to capture different phrasings and enable multi-turn extraction to create follow-up prompts. These actions directly enhance the bot's ability to understand varied user input and maintain context. Other options either reduce flexibility or do not address the specific needs.

Answer

Increase the confidence threshold to 90% to ensure only high-confidence answers are returned.

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Use the exact match only setting to ensure answers are precise.

Answer

Add more source documents to the knowledge base to cover additional topics.

Question 10

A support organization uses Azure AI Language custom named entity recognition (custom NER) to extract product codes from warranty emails. The model performs well in production, but a new product line introduced codes formatted as two letters, a hyphen, and six digits, and the model misses them. You need the model to recognize the new format while keeping existing extraction quality. What should you do?

Accepted Answer

Add labeled utterances containing the new code format to the existing project, retrain, and review the evaluation metrics before redeploying.. The reliable way to extend a custom NER model to a new entity shape is to label representative utterances that contain that shape and retrain, then verify with evaluation metrics. This preserves the learned behavior on existing codes because the original labeled data remains in the project, while the new examples supply the boundary evidence the model needs for the two-letter, hyphen, six-digit pattern.

Answer

Increase the model's temperature setting so it generalizes to unseen code formats.

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Create a new custom NER project dedicated to the new product line and route emails to it based on keyword rules.

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Add the new product codes to a phrase list attached to the entity and redeploy the existing model.

Question 11

You are building an ASP.NET Core web app that must analyze the sentiment of user-submitted product reviews in real time. The app is deployed to an Azure App Service web app. You want to avoid managing API keys in code and prefer to use the app's managed identity for authentication to the Azure AI Language resource. What should you do to enable the app to call the sentiment analysis API securely?

Accepted Answer

Assign the Cognitive Services User role to the App Service's managed identity on the Language resource, and use DefaultAzureCredential in the app code.. The recommended secure approach is to use managed identity with Microsoft Entra ID authentication. Assigning the Cognitive Services User role to the App Service's managed identity grants the necessary permissions, and DefaultAzureCredential in the SDK automatically obtains a token. This eliminates the need to store or manage API keys in code, aligning with the scenario's requirement.

Answer

Configure the app to use the Language resource's endpoint and a SAS token generated from the resource's keys.

Answer

Store the Language resource's subscription key in Azure Key Vault and retrieve it at runtime using the app's managed identity.

Answer

Enable a system-assigned managed identity on the App Service and add the identity's object ID to the Language resource's access control list (ACL).

Question 12

A healthcare portal must summarize patient feedback stored in Azure Blob Storage. Documents are plain UTF-8 text and the summaries must be generated without sending content to a public endpoint. You are building the pipeline with Azure AI Language and a private network. Which configuration should you use to call extractive summarization while meeting the network requirement?

Accepted Answer

Create an Azure AI Language resource with a private endpoint and disable public network access, then call the REST API from a client inside the virtual network.. Meeting a no-public-endpoint requirement means the Azure AI Language resource must be reachable only through private networking. Combining a private endpoint with disabled public network access forces all calls, including extractive summarization, to travel inside the virtual network, which keeps document content off the public internet while preserving full API functionality.

Answer

Deploy the open-source summarization container for Azure AI Language on an Azure Kubernetes Service cluster and call its local endpoint.

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Create a standard Azure AI Language resource and authenticate with an account key, relying on TLS to protect the content in transit.

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Use the Azure AI Language resource with a service tag firewall rule that allows only the portal's app service outbound IP addresses.

Question 13

You are developing a conversational language understanding (CLU) project in Azure AI Language. You have defined intents and entities for a banking bot. During testing, you notice that utterances containing the phrase 'transfer funds' are sometimes predicted as the 'CheckBalance' intent instead of 'TransferMoney'. You have already added 20 labeled examples for 'TransferMoney' and 20 for 'CheckBalance'. What should you do to improve the model's ability to distinguish between these intents?

Accepted Answer

Add more labeled utterances for 'TransferMoney' that include variations of the phrase 'transfer funds' and similar expressions, ensuring diversity in phrasing.. The model misclassifies utterances because the training data for 'TransferMoney' may not include enough variations of the phrase 'transfer funds'. Adding more diverse labeled examples for that intent helps the model learn the distinguishing features. Increasing epochs or adding entities does not directly address intent confusion. Multilingual settings are irrelevant here.

Answer

Add a prebuilt entity for 'money' to the project and map it to the 'TransferMoney' intent.

Answer

Enable the 'Use multiple languages' option in the project settings to improve model understanding.

Answer

Increase the number of training epochs for the CLU model to allow it to learn more from the existing data.

Question 14

You are building a customer feedback dashboard with Azure AI Language. Analysts need to see, for each document, the overall sentiment and the sentiment expressed toward specific aspects such as shipping speed and product quality within the same request. The documents are short English reviews. Which feature and configuration should you use?

Accepted Answer

Call the sentiment analysis operation with opinion mining enabled and read the target and assessment pairs from the response.. Aspect-based sentiment analysis, delivered through opinion mining on the sentiment analysis operation, returns the document sentiment plus target and assessment pairs that pair each aspect with its expressed opinion. That single call supplies both the overall sentiment and the per-aspect sentiment the dashboard requires, without needing separate models or manual inference.

Answer

Call the custom text classification operation with a multi-label project that has one label per aspect.

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Call the key phrase extraction operation and infer aspect sentiment from the returned phrases.

Answer

Call the sentiment analysis operation with opinion mining enabled and specify the aspect targets in the request.

Question 15

You are building a solution that uses Azure AI Language's sentiment analysis to monitor customer feedback. The feedback includes text in multiple languages, and you need to obtain sentiment scores at both the document level and the sentence level. Which API endpoint should you call?

Accepted Answer

POST /language/:analyze-text with kind set to "SentimentAnalysis" and include opinion mining.. The unified Azure AI Language endpoint /language/:analyze-text supports sentiment analysis when the kind parameter is set to SentimentAnalysis. It returns both document-level and sentence-level sentiment, and can optionally include opinion mining. This is the current recommended approach, replacing the older Text Analytics API endpoints. The other options either use legacy endpoints or specify incorrect task kinds.

Answer

POST /language/:analyze-text with kind set to "KeyPhraseExtraction" and include sentiment scores.

Answer

POST /text/analytics/v3.1/sentiment with the showStats parameter set to true.

Answer

POST /language/:analyze-conversations with kind set to "SentimentAnalysis".