CCNA Nlp Solutions Questions — Page 3 of 3

151

Multi-Selecteasy

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

Select 2 answers

A.Sentiment analysis

B.Summarization

C.Language detection

D.Entity recognition

E.Key phrase extraction

AnswersC, E

Language detection returns the detected language name and ISO code for each comment, which is needed before any language-specific processing. It directly satisfies the first requirement of identifying the language of each social media comment.

Why this answer

Language detection is the correct feature because it identifies the language of each comment, which is a prerequisite for further analysis. Key phrase extraction is the second correct feature because it extracts important terms from the text, directly addressing the requirement to 'extract key phrases' after language identification.

Exam trap

Microsoft Azure AI Language often tests the distinction between features that analyze content (sentiment, entities, key phrases) versus those that identify metadata (language), and the trap here is that candidates might confuse 'key phrase extraction' with 'entity recognition' because both extract terms, but key phrases are broader and not limited to named entities.

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152

Multi-Selectmedium

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

Select 2 answers

A.Azure AI Language Understanding (LUIS)

B.Azure OpenAI Service with a RAG pattern

C.Azure AI Translator

D.Azure AI Document Intelligence

E.Azure AI Language - Custom Question Answering

AnswersB, E

Azure OpenAI Service with retrieval-augmented generation embeds documents in a search index, retrieves relevant chunks per query, and prompts the model to answer in the user's language. This satisfies the multilingual requirement because the underlying model handles translation and generation natively.

Why this answer

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.

Exam trap

The AI-102 exam often tests the distinction between services that process language (like LUIS or Translator) versus services that combine retrieval with generation (like Azure OpenAI with RAG) to answer questions from documents, leading candidates to mistakenly choose LUIS or Translator for a task that requires document-based Q&A.

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153

MCQeasy

You are building a solution that must summarize long documents in real time as they are uploaded to Azure Blob Storage. The summaries must be concise and capture the main points. You want to use Azure AI Language. Which feature should you use?

A.Extractive summarization

B.Key phrase extraction

C.Named entity recognition (NER)

D.Abstractive summarization

AnswerD

Abstractive summarization generates new, concise sentences that capture the main ideas, making it ideal for producing brief summaries of long documents. It is available in Azure AI Language and can process documents in real time via the API. This meets the requirement for concise, main-point summaries.

Why this answer

Abstractive summarization in Azure AI Language generates new, concise sentences that capture the main ideas of a document. It is designed for producing brief summaries and is available as a real-time API. Extractive summarization selects existing sentences, which may be less concise.

Key phrase extraction and NER provide different types of analysis and do not generate summaries.

Exam trap

The trap here is confusing summarization with other text analytics features like key phrase extraction or NER, which do not produce a summary.

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154

MCQmedium

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?

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

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

C.Add labeled utterances containing the new code format to the existing project, retrain, and review the evaluation metrics before redeploying.

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

AnswerC

Custom NER learns entity boundaries from labeled examples, so adding utterances that contain the new two-letter, hyphen, six-digit pattern teaches the model the new shape while the existing labeled data preserves prior behavior. Retraining and reviewing evaluation metrics confirms that recall on the new format improved without regressing the original product codes before you redeploy.

Why this answer

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.

Exam trap

The trap here is reaching for a runtime hint such as a phrase list or a sampling parameter instead of adding labeled training data for the new entity format.

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155

MCQeasy

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

A.Call the sentiment analysis REST API for each post

B.Use Azure AI Search with cognitive skills

C.Use the batch processing feature in Azure AI Language

D.Send posts to an Event Hub and use Stream Analytics

AnswerA

The sentiment analysis REST API processes each post synchronously, returning polarity scores immediately, which satisfies the real-time constraint. Batch or asynchronous pipelines would introduce latency, so per-post API calls are the appropriate mechanism for streaming social media sentiment.

Why this answer

The sentiment analysis REST API in Azure AI Language is designed for real-time, per-document analysis. By calling the API for each social media post as it arrives, you achieve the lowest latency and can process posts individually without batching or streaming overhead, which is essential for real-time sentiment analysis.

Exam trap

The trap here is that candidates often confuse real-time processing with streaming architectures (like Event Hubs and Stream Analytics) or batch processing, but the simplest and most direct real-time approach for per-document sentiment analysis is the REST API.

How to eliminate wrong answers

Option B is wrong because Azure AI Search with cognitive skills is designed for indexing and enriching documents at rest, not for real-time processing of individual streaming posts. Option C is wrong because the batch processing feature in Azure AI Language is intended for asynchronous, high-throughput processing of large volumes of documents, not for real-time, per-post analysis. Option D is wrong because sending posts to an Event Hub and using Stream Analytics is a streaming architecture that adds unnecessary complexity and latency for simple per-post sentiment analysis; the REST API is more direct and efficient for real-time needs.

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156

MCQhard

You are developing a solution that uses Azure AI Language to perform sentiment analysis on multilingual product reviews. The reviews are in English, German, and Japanese. You need to ensure that the sentiment score is accurate for each language. What should you do?

A.Specify the correct language code for each review in the request, such as 'en', 'de', or 'ja'.

B.Translate all reviews to English using Azure AI Translator, then perform sentiment analysis with language set to 'en'.

C.Set the language parameter to 'en' for all requests to force English sentiment analysis.

D.Omit the language parameter and let the service auto-detect the language for each review.

AnswerA

Azure AI Language sentiment analysis supports multiple languages, and providing the correct language code ensures the appropriate model is used. This yields the most accurate sentiment scores. Since the languages are known, explicitly setting the code avoids detection errors and improves reliability for English, German, and Japanese reviews.

Why this answer

Azure AI Language sentiment analysis supports multiple languages, and specifying the correct language code for each review ensures the model uses the appropriate linguistic rules. This yields the most accurate sentiment scores. Auto-detection or forcing a single language can lead to misinterpretation, while translation adds unnecessary complexity and potential loss of sentiment nuance.

Exam trap

The trap here is thinking that translating to English or forcing a single language is needed for multilingual sentiment, when Azure AI Language natively supports multiple languages with explicit language codes.

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