AI-102 Implement natural language processing solutions Practice Test 3 — 15 Questions

Question 1

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?

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

Accepted Answer

Split the data chronologically and ensure no overlapping data between train and test sets.. Data leakage occurs when information from outside the training set inadvertently influences the model, often due to overlapping or non-independent data splits. By splitting the data chronologically (e.g., training on older applications and testing on newer ones), the data scientist ensures that no future information leaks into the training process, which directly validates whether temporal leakage is causing poor accuracy. This approach is standard for time-series or sequential data like loan applications, where patterns may shift over time.

Answer

Increase the training dataset size and retrain the model.

Answer

Use k-fold cross-validation during training.

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Adjust the classification confidence threshold.

Question 2

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

Accepted Answer

Azure AI Language - Extractive Summarization. Azure AI Language's Extractive Summarization is specifically designed to generate concise summaries by extracting the most important sentences from a document while preserving the original meaning and key points. This service uses natural language processing to rank sentences based on relevance and coherence, making it ideal for summarizing long articles without altering the original content.

Answer

Azure AI Document Intelligence

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Azure AI Language - Key Phrase Extraction

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Azure AI Translator

Question 3

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?

Accepted Answer

Use topics with slots to collect information across turns.. In Microsoft Copilot Studio, topics with slots are specifically designed to handle multi-turn conversations by collecting and persisting information across user inputs. Slots allow the agent to prompt for missing details and maintain context within a topic, enabling complex, stateful interactions without external dependencies.

Answer

Use actions to call external APIs for context.

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Use global variables to store conversation state.

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Use entities to capture user input.

Question 4

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?

Accepted Answer

Use the multilingual option in Azure AI Language custom NER to extend the existing project to include French.. Azure AI Language's custom NER supports a multilingual option that allows you to extend an existing project to include additional languages without retraining from scratch. By enabling this option and adding French labeled data, the model learns to recognize entities in French while retaining its English performance, making it the most efficient approach.

Answer

Create a separate custom NER project for French and train from scratch using French contracts.

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Retrain the English model with a mix of English and French contracts.

Answer

Use Azure AI Translator to translate French contracts to English, then use the English model.

Question 5

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?

Accepted Answer

Azure AI Language - PII Detection. Azure AI Language's PII Detection feature is specifically designed to identify and redact personally identifiable information in text across multiple languages. It supports over 30 languages and can detect entities such as names, addresses, phone numbers, and credit card numbers, making it the correct choice for this multilingual PII redaction requirement.

Answer

Azure AI Content Safety

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Azure AI Document Intelligence

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Azure AI Translator

Question 6

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

Accepted Answer

Azure OpenAI Service with a RAG pattern. The Azure OpenAI Service combined with a Retrieval-Augmented Generation (RAG) pattern allows you to ingest documents into a vector store, retrieve relevant chunks based on the user's query, and then generate a natural language answer using a large language model. This architecture inherently supports multilingual input and output because the underlying model (e.g., GPT-4) can process and generate text in many languages, making it ideal for a multilingual question-answering bot.

Answer

Azure AI Language Understanding (LUIS)

Answer

Azure AI Translator

Answer

Azure AI Document Intelligence

Question 7

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?

Accepted Answer

Custom models require a large set of labeled training data.. Custom sentiment analysis in Azure AI Language requires a sufficiently large set of labeled training data to fine-tune a model for a specialized domain. Without this data, the custom model cannot learn domain-specific sentiment patterns, making it impractical for scenarios where labeled data is scarce.

Answer

Custom models always have faster response times.

Answer

Pre-built models cannot be used in containers.

Question 8

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

Accepted Answer

Identifying names of people. Azure AI Language's pre-built entity recognition includes a category for 'Person' that identifies names of people in text. This is a standard named entity recognition (NER) capability that extracts entities like individuals' names without requiring custom model training.

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Identifying domain-specific medical terms

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Extracting key phrases from text

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Determining overall sentiment of the text

Question 9

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

Accepted Answer

Both entity recognition and key phrase extraction results.. The request includes both the `entities` and `keyPhrases` tasks in the `tasks` array, which instructs the Azure AI Language service to perform both entity recognition and key phrase extraction on the same input document. The service processes each specified task independently and returns a combined response with results for both tasks, not just one.

Answer

An error because the request format is invalid.

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Only key phrase extraction results.

Answer

Only entity recognition results.

Question 10

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'?

Accepted Answer

Laptop is a type of Electronics.. In Azure AI Language custom entity recognition, you define entity types and subtypes using a hierarchical structure. The exhibit shows 'Laptop' as a child of 'Electronics', meaning Laptop is a subtype or specific type of the broader Electronics category. This allows the model to recognize that any Laptop entity is also an instance of Electronics, enabling more granular classification and downstream processing.

Answer

There is no defined relationship.

Answer

Electronics is a type of Laptop.

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Laptop is a part of Electronics.

Question 11

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?

Accepted Answer

Call a separate booking API with the extracted entities to complete the reservation.. After CLU extracts the intent and entities, the application must use those entities to call a separate booking API to complete the reservation. CLU itself does not perform bookings; it only provides language understanding. Option B is incorrect because the CLU response cannot directly book a flight. Option C is incorrect because the intent is already clearly identified as 'BookFlight' and entities are extracted, so no rephrasing is needed. Option D is incorrect because sending another request to CLU would not confirm booking; confirmation is handled by the booking API.

Answer

Use the CLU response to directly book the flight via the Azure AI Language service.

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Prompt the user to rephrase the request because the intent is ambiguous.

Answer

Send another request to CLU to confirm the booking details.

Question 12

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

Accepted Answer

Add alternate phrases to existing QnA pairs for common user queries.. Adding alternate phrases to existing QnA pairs directly improves the bot's ability to match user queries to the correct answer without retraining. In Azure AI Language's custom question answering, the model uses a ranker that compares the user's input against the questions and alternate phrases in the knowledge base. By providing more varied phrasings for common queries, you increase the likelihood of a high-confidence match, thereby reducing irrelevant answers.

Answer

Increase the confidence score threshold in the project settings.

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Reduce the number of QnA pairs to decrease ambiguity.

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Enable active learning to let the bot suggest new questions based on user queries.

Question 13

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?

Accepted Answer

Custom named entity recognition (NER) component.. Custom named entity recognition (NER) is the correct feature because it allows you to train a model to identify domain-specific entities, such as product names, that are not included in Azure AI Language's prebuilt entity catalog. Unlike prebuilt components, custom NER uses a labeled dataset to learn the exact spans of text that represent your custom entities, enabling the chatbot to extract them accurately from user utterances.

Answer

Prebuilt entity recognition component.

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Key phrase extraction.

Answer

List entity in a conversational language understanding (CLU) project.

Question 14

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?

Accepted Answer

Language detection API.. The Language Detection API is the correct choice because it is specifically designed to identify the language of input text automatically, returning a language code and confidence score. This enables the chatbot to route the query to the appropriate language-specific model without requiring any prior training or configuration. The other options either require explicit language specification or are designed for different tasks like translation or intent classification.

Answer

Translator API.

Answer

Conversational language understanding (CLU) with multilingual project.

Answer

Custom text classification model.

Question 15

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

Accepted Answer

Use a custom named entity recognition (NER) model for custom entities and call the prebuilt entity recognition API separately for standard entities.. Azure AI Language provides separate APIs for prebuilt entity recognition (covering standard entities like dates, numbers, and common types) and custom named entity recognition (NER) for domain-specific entities like product names and issue types. By using both services independently, you can extract standard entities from the prebuilt API and custom entities from a trained custom NER model, then combine the results. This approach avoids the limitations of a single model that cannot handle both predefined and custom entity types simultaneously.

Answer

Use the prebuilt entity recognition API only and map standard entities to custom categories.

Answer

Use a single conversational language understanding (CLU) project with entities defined for both standard and custom entities.

Answer

Use a custom text classification model to classify the transcript and then extract entities from the classified output.