AI-102 Implement natural language processing solutions Practice Test 13 — 15 Questions

Question 1

You want to use the Azure AI Language service to summarize long customer support conversations into a short summary. Which feature should you use?

Accepted Answer

Conversational Summarization. Conversational Summarization is the correct feature because it is specifically designed to condense multi-turn dialogues, such as customer support conversations, into concise summaries. Unlike generic text summarization, it understands the conversational flow, speaker turns, and context to produce a coherent summary of the interaction.

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Sentiment Analysis

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Entity Extraction

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Key Phrase Extraction

Question 2

You are building a solution that must translate customer chat messages from Spanish to English in real-time. The solution must also detect the language of incoming messages to handle cases where users write in other languages. Which TWO Azure AI service features should you use?

Accepted Answer

Azure AI Translator - Language Detection. Azure AI Translator's Language Detection feature (option A) is correct because it identifies the language of incoming chat text, which is exactly what is needed to handle messages written in languages other than Spanish. Azure AI Translator's Translation feature (option B) is correct because it performs the actual text-to-text translation from Spanish to English in real time. Together, these two features satisfy both requirements: detecting the source language and translating the chat messages. Option C (Azure AI Speech - Speech Translation) is not appropriate because the scenario involves chat text, not spoken audio. Option D (LUIS) is a natural language understanding service for intent and entity extraction, not translation or language identification. Option E (Custom Question Answering) builds FAQ-style knowledge bases and does not provide translation or language detection.

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Azure AI Speech - Speech Translation

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Azure AI Language Understanding (LUIS)

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Azure AI Custom Question Answering

Question 3

You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?

Accepted Answer

Recall. Recall is correct because it measures the proportion of actual positive instances correctly identified by the model, which is critical in custom text classification to ensure that relevant documents are not missed. In Azure AI Language, recall helps assess how well the model captures all instances of each category, especially when class distribution is imbalanced across the 10 categories.

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Word Error Rate

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BLEU Score

Question 4

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?

Accepted Answer

Key Phrase Extraction. Sentiment Analysis (C) is correct because it returns the overall sentiment of the review (positive, negative, neutral, or mixed) along with confidence scores, which is exactly the first insight required. Key Phrase Extraction (A) is correct because it identifies the main talking points in the review text, satisfying the key phrases requirement. Entity Recognition (E) is correct because it extracts and classifies named entities such as product names, locations, and organizations, which covers the entity types requirement. Language Detection (B) is not needed because the scenario does not ask for identifying the review's language, and PII Detection (D) is not needed because the scenario does not require detecting or redacting personal information.

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Language Detection

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PII Detection

Question 5

You need the project to support English, Spanish, and French. What change should you make to the command?

Accepted Answer

Change --multilingual false to --multilingual true.. The command requires multilingual support for English, Spanish, and French. By default, the `--multilingual` flag is set to `false`, which restricts the project to a single language. Changing it to `true` enables the project to accept utterances in multiple languages, allowing the Conversational Language Understanding (CLU) model to process and train on intents and entities across all specified languages.

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Change --language to "multi".

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Add --description "Multi-language support".

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Change --project-name to "SupportBotML".

Question 6

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

Accepted Answer

ProductName. The exhibit shows that ProductName has a recall of 0.65, which is lower than the recall for Date (0.98) and OrderNumber (0.99). Low recall indicates that the model is missing many true instances of ProductName. Adding more labeled examples specifically for ProductName will help the model learn its patterns better, improving recall and overall performance. This aligns with the practice of iterative model improvement in custom entity extraction within Azure AI Language.

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Date

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OrderNumber

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All entities need improvement.

Question 7

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

Accepted Answer

The number of calls exceeded the rate limit for the service tier.. HTTP 429 (Too Many Requests) is a rate-limiting response that occurs when the number of API calls exceeds the allowed threshold for the service tier. In Azure AI services, each pricing tier has a specific requests-per-second (RPS) or requests-per-minute (RPM) limit, and exceeding this limit triggers a 429 error to protect backend resources.

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Network connectivity issues.

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Authentication tokens expired.

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Invalid API keys were used.

Question 8

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?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. This feature uses machine learning classifiers trained on large datasets to assign a sentiment label and confidence scores at the sentence and document level, directly matching the requirement to analyze customer feedback for sentiment polarity.

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Entity Recognition

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Language Detection

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Key Phrase Extraction

Question 9

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

Accepted Answer

Create a separate LUIS app for each language and use the same intents and entities structure.. LUIS does not support a single multilingual model; each LUIS app is designed for a single primary language. To handle multiple languages efficiently, you must create a separate LUIS app per language, each with the same intent and entity schema, and route user queries to the appropriate app based on the detected language. This ensures optimal language-specific model training and accuracy.

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Use the prebuilt multilingual LUIS model and fine-tune it with your intents.

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Create a single LUIS app and add utterances in all three languages.

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Create one LUIS app with language set to 'Multilingual' and add utterances in all languages.

Question 10

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

Accepted Answer

Set the content type to 'text/html' in the translation request.. Azure AI Translator's Translate API accepts a contentType parameter, and setting it to 'text/html' tells the service to treat the input as HTML, preserving tags and only translating the human-readable text nodes. This is the documented mechanism for maintaining markup structure during translation. Without it, the service treats input as plain text and may mangle or translate tag names.

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Use the Translator glossary to preserve formatting.

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Use the Translator parallel corpus to train a custom model.

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Use the Transliterate method to preserve formatting.

Question 11

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?

Accepted Answer

Custom text classification (single or multi-label). Custom text classification (single or multi-label) is the correct project type because you have a labeled dataset with 10 categories and need to train a model to classify text into those specific categories. Azure AI Language provides a custom text classification feature that allows you to train a model using your own labeled data, supporting both single-label and multi-label classification scenarios. This is the only option that enables you to build a bespoke classifier tailored to your 10-category dataset.

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Conversational Language Understanding (CLU)

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Key Phrase Extraction

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Prebuilt Text Classification API

Question 12

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

Accepted Answer

Document summarization. Document summarization is the correct feature because it is specifically designed to generate concise summaries of large documents, extracting the most important information. Azure AI Language's document summarization uses extractive or abstractive techniques to produce a summary, directly addressing the requirement to summarize a large document.

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Key phrase extraction

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Entity recognition

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Sentiment analysis

Question 13

You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?

Accepted Answer

Create a custom NER project in Azure AI Language and train it with your annotated data.. Azure AI Language's custom NER (Named Entity Recognition) feature allows you to train a model using your own annotated dataset. With 50 documents, you have enough labeled data to fine-tune a custom entity extraction model that learns the specific entity types and patterns in your domain, enabling accurate extraction from new documents.

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Use the prebuilt entity recognition API to extract entities.

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Use the Conversational PII entity extraction feature.

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Use the built-in entity extraction skill in Azure AI Search.

Question 14

You are deploying a custom question answering solution in Azure AI Language. You need to ensure that the knowledge base can handle synonyms and alternative phrasings for questions. Which THREE strategies should you implement? (Select THREE.)

Accepted Answer

Add alternative question phrases to each QnA pair.. Adding alternative question phrases to each QnA pair directly expands the surface area of possible user queries that will match a given answer. This is a core feature of custom question answering in Azure AI Language, allowing you to predefine synonyms and rephrasings for each QnA pair without relying on external logic.

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Use a custom question answering project type instead of the prebuilt one.

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Increase the confidence threshold to 0.9 to reduce false positives.

Question 15

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.)

Accepted Answer

Language detection. 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.

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Sentiment analysis

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Summarization

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Entity recognition