AI-102 Implement natural language processing solutions Practice Test 9 — 15 Questions

Question 1

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. Azure OpenAI Service with a RAG (Retrieval-Augmented Generation) pattern is correct because it combines a large language model with a retrieval layer that fetches relevant passages from a document knowledge base (e.g., via Azure AI Search) and generates grounded, multilingual answers. Azure AI Language - Custom Question Answering is correct because it is purpose-built to create a knowledge base from documents and FAQs and return precise answers to natural-language questions, including multilingual support. Azure AI Language Understanding (LUIS) is not correct because it only performs intent and entity extraction, not document retrieval or answer generation. Azure AI Translator is not correct because it only translates text between languages and does not retrieve or answer from a knowledge base. Azure AI Document Intelligence is not correct because it only extracts structured data (text, tables, key-value pairs) from documents and does not provide question-answering retrieval.

Answer

Azure AI Language Understanding (LUIS)

Answer

Azure AI Translator

Answer

Azure AI Document Intelligence

Question 2

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.. Option A is correct because custom sentiment analysis in Azure AI Language is a fine-tuned model that requires you to provide a substantial set of labeled training data (typically hundreds of labeled utterances per class) so the model can learn domain-specific patterns. Option C is correct because the pre-built sentiment analysis model is trained on general-purpose text, so specialized jargon, acronyms, or industry-specific phrasing in a niche domain may be misclassified, which is a key reason to consider a custom model. Option E is correct because Azure AI Language's pre-built sentiment analysis supports multiple languages out-of-the-box, which is a significant advantage when your data spans several languages and you want to avoid building separate custom models per language. Option B is not correct because custom models are not inherently faster; latency depends on deployment, and custom models can add overhead compared to the pre-built service. Option D is not correct because pre-built Azure AI Language models can be deployed in containers (for example, via Docker with the Language container images) for on-premises or disconnected scenarios.

Answer

Custom models always have faster response times.

Answer

Pre-built models cannot be used in containers.

Question 3

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 NER (Named Entity Recognition) model extracts general-purpose entities from unstructured text, and its entity categories explicitly include Person, so option B (identifying names of people) is correct. The same pre-built model also includes an Organization category, so option D (identifying organization names) is correct. Both are part of the standard entity taxonomy returned by the NER endpoint without any custom training. Option A is wrong because domain-specific medical terms require the separate Text Analytics for Health feature, not the general pre-built NER model. Option C is wrong because key phrase extraction is a distinct Azure AI Language capability (the Key Phrase Extraction feature), not entity recognition. Option E is wrong because sentiment analysis is its own Azure AI Language feature, separate from NER.

Answer

Identifying domain-specific medical terms

Answer

Extracting key phrases from text

Answer

Determining overall sentiment of the text

Question 4

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.

Answer

Laptop is a part of Electronics.

Question 5

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.

Answer

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

Answer

Send another request to CLU to confirm the booking details.

Question 6

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 7

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

Accepted Answer

Extractive summarization.. Extractive summarization is the correct feature because it specifically identifies and extracts the most important sentences from a document to create a concise summary. Azure AI Language's extractive summarization uses a ranking model to score sentences based on relevance and informativeness, directly addressing the requirement to automatically summarize long documents.

Answer

Sentiment analysis.

Answer

Key phrase extraction.

Answer

Entity recognition.

Question 8

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?

Accepted Answer

Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.. 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.

Answer

Reduce the number of categories to simplify the classification.

Answer

Increase the number of training epochs to further train the model.

Answer

Use a different classification algorithm, such as a neural network.

Question 9

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?

Accepted Answer

Sentiment analysis API.. The Sentiment Analysis API is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether each customer review expresses positive, negative, or neutral sentiment.

Answer

Language detection API.

Answer

Entity recognition API.

Answer

Key phrase extraction API.

Question 10

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

Accepted Answer

To identify entities in the text and link them to a knowledge base.. The request is configured for entity linking, which is a specific capability of the Azure AI Language API that identifies named entities in the text and resolves them to a unique identifier in a knowledge base (such as Wikipedia or a custom knowledge graph). This goes beyond simple named entity recognition (NER) by providing a canonical link, enabling disambiguation of entities with the same name (e.g., 'Washington' as a state vs. a person). The response includes both the entity name and a URL to the knowledge base entry, confirming the primary purpose is linking to a knowledge base.

Answer

To extract named entities from the text without linking.

Answer

To extract key phrases from the text.

Answer

To analyze the sentiment of the text.

Question 11

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

Accepted Answer

Language Detection. Language Detection is the correct feature because it is specifically designed to identify the language of text input, returning a language name and a confidence score. In a multi-language chatbot, this detection step is essential to route the query to the appropriate language-specific model or handler. Azure AI Language Service provides a dedicated pre-built capability for language detection, which can be called via the REST API or SDK.

Answer

Sentiment Analysis

Answer

Key Phrase Extraction

Answer

Entity Recognition

Question 12

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

Accepted Answer

Azure AI Search. Azure AI Search is the correct data source because it provides a search index that can be queried by Copilot Studio using the 'Azure AI Search' connector. This allows the copilot to perform semantic or keyword-based retrieval over indexed technical documents, enabling accurate question-answering from a knowledge base. Copilot Studio natively supports Azure AI Search as a data source for generative answers, making it the optimal choice for this scenario.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Microsoft Lists

Question 13

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?

Accepted Answer

Add more labeled data from actual production tickets. Adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.

Answer

Switch to a prebuilt text classification model

Answer

Increase the number of training epochs

Answer

Reduce the confidence threshold for classification

Question 14

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

Accepted Answer

Azure AI Language Service. Azure AI Language Service (formerly Text Analytics) includes a pre-built PII detection feature that can identify, categorize, and redact personally identifiable information from unstructured text. This service is specifically designed for text-based extraction tasks, making it the correct choice for processing customer support transcripts stored in Azure Blob Storage.

Answer

Azure AI Speech

Answer

Azure AI Translator

Answer

Azure AI Vision

Question 15

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

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct feature because it is specifically designed to ingest semi-structured content like FAQ PDFs and automatically generate question-answer pairs. It uses a built-in extraction pipeline that parses the document structure (e.g., headings, bullet points) to identify likely questions and their corresponding answers, which can then be reviewed and refined in the Azure Language Studio portal.

Answer

Key Phrase Extraction

Answer

Extractive Summarization

Answer

Conversational Language Understanding