AI-102 Implement natural language processing solutions Practice Test 6 — 15 Questions

Question 1

You are deploying a custom named entity recognition (NER) model using Azure AI Language. The model must extract product codes that follow a specific pattern (e.g., 'PRD-12345'). You have 5,000 labeled examples. After training, the model extractor works well on development data but fails to extract product codes from new data. What is the most likely issue?

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

Accepted Answer

The model is overfitting to the training data.. The model performs well on development data but fails on new data, which is the classic symptom of overfitting. In Azure AI Language custom NER, overfitting occurs when the model memorizes the training examples—including noise or specific patterns—rather than generalizing to the underlying product code pattern. With 5,000 labeled examples, the dataset size is likely sufficient, but the model may have learned spurious correlations that do not hold in unseen data.

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The training data size is insufficient.

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The product code pattern is too complex for the model to learn.

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The labeling is inconsistent across the dataset.

Question 2

You need to analyze customer service call transcripts to identify common issues. The solution must extract key phrases, detect sentiment, and identify the language used. The transcripts are stored in Azure Blob Storage. Which Azure AI Language feature should you use to process them asynchronously?

Accepted Answer

Async Text Analytics API. The Async Text Analytics API (now part of Azure AI Language's asynchronous operations) is designed for batch processing of documents stored in Azure Blob Storage. It supports key phrase extraction, sentiment analysis, and language detection in a single asynchronous call, making it ideal for processing large volumes of customer service call transcripts without blocking the application.

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Conversational Summarization API

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Custom Text Classification API

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Opinion Mining API

Question 3

A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization selects the most important sentences directly from the source document to create a concise summary, preserving the original wording. This aligns with the requirement to extract key sentences without generating new text, making it the correct choice for this scenario.

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Abstractive summarization

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Key phrase extraction

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Entity recognition

Question 4

You are building a custom question answering solution using Azure AI Language. Which TWO actions are required to deploy the solution?

Accepted Answer

Create a Custom Question Answering project in Azure AI Language.. A Custom Question Answering project is the container for your question-answer pairs, synonyms, and active learning settings within Azure AI Language. You must create this project to define the knowledge base that the solution will query against.

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Train a custom model using the Azure AI Language training API.

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Create a QnA Maker service in the Azure portal.

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Publish the project to a web app bot.

Question 5

You are designing a solution to detect personally identifiable information (PII) in documents using Azure AI Language. The solution must also handle documents in multiple languages. Which THREE features should you use?

Accepted Answer

The language detection feature of Azure AI Language.. Azure AI Language's language detection feature is necessary to identify the language of each document before applying PII detection, ensuring the correct language-specific model is used for accurate entity extraction. This is a prerequisite step in a multi-language PII detection pipeline, as the PII detection API requires the language parameter to be set appropriately.

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The conversation summarization API.

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The Text Analytics for health feature.

Question 6

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

Accepted Answer

Sentiment analysis. Sentiment analysis is a core feature of the Azure AI Language Text Analytics API, allowing you to determine the overall positive, negative, or neutral sentiment of customer feedback. This capability is essential for analyzing customer opinions at scale, and it is explicitly listed as one of the three main analysis types (along with entity recognition and key phrase extraction) in the service documentation.

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Image captioning

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Speech-to-text

Question 7

You are debugging a CLU application. The JSON above shows a request to the Azure AI Language runtime API. The response returns an intent of "BookFlight" with a confidence of 0.95, but no entities are extracted. The training data includes entities like "Location" and "DateTime". What is the most likely cause?

Accepted Answer

The request is missing the 'verbose' parameter set to true.. The CLU runtime API, by default, returns only the top intent and does not include extracted entities unless the 'verbose' query parameter is set to true. Without this parameter, the response omits the entities array even if the model was trained to extract them, which explains why the intent is returned with high confidence but no entities appear.

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The deployment name is incorrect.

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The model was not trained on entity extraction.

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The utterance does not contain any entities.

Question 8

You run the PowerShell script above to call the Text Analytics API. The response shows a sentiment label of 'positive' with a score of 0.99. However, you expected 'negative' because the word 'excellent' was meant to be sarcastic. What is the most likely reason for this result?

Accepted Answer

The model does not detect sarcasm and interprets the text literally.. The Text Analytics API performs sentiment analysis using a machine learning model that evaluates the literal wording of the text. It does not have built-in capability to detect sarcasm, irony, or implied meaning. Therefore, the word 'excellent' is interpreted as positive regardless of the intended sarcastic tone, resulting in a high positive score.

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The score threshold for negative sentiment is too high.

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The language parameter is set incorrectly.

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The API version does not support sentiment analysis.

Question 9

You deploy the ARM template above to create an Azure AI Language resource. After deployment, you try to use the custom question answering feature but it is not available. What is the most likely reason?

Accepted Answer

The 'kind' property is set to 'TextAnalytics', which does not enable custom question answering.. The 'kind' property in the ARM template is set to 'TextAnalytics', which provisions a general-purpose Text Analytics resource. Custom question answering is a feature of the Azure AI Language service that requires the resource to be created with the 'kind' set to 'Language' or 'ConversationalLanguageUnderstanding' (depending on the API version). Because the resource is of the wrong kind, the custom question answering capability is not available, even though the deployment succeeds.

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The apiVersion is too old to support custom question answering.

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The SKU is set to S, but custom question answering requires a higher SKU.

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The location does not support custom question answering.

Question 10

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

Accepted Answer

Enable the content filter in Azure OpenAI Service.. Azure OpenAI Service includes a built-in content filter that actively scans both input prompts and generated completions to detect and block harmful content such as hate speech, violence, or self-harm. This filter operates at the service level, providing a robust safety layer that cannot be bypassed by model configuration alone. While system prompts can guide behavior, they are not a reliable safeguard against adversarial inputs or model misuse.

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Use a system prompt that instructs the model to be safe.

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Set the temperature parameter to 0.

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Set max_tokens to a low value.

Question 11

You are building a solution to extract key phrases from customer reviews using Azure AI Language. Which feature should you use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct feature because it is specifically designed to identify and return the main talking points or important terms from unstructured text, such as customer reviews. Azure AI Language's Key Phrase Extraction API analyzes the text structure and linguistic patterns to surface the most relevant phrases, which directly addresses the requirement to extract key phrases from reviews.

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Sentiment Analysis

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Language Detection

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Named Entity Recognition

Question 12

You are designing an NLP solution to analyze legal documents. The solution must identify specific clauses and parties involved. Which Azure AI service is most appropriate?

Accepted Answer

Custom Named Entity Recognition in Azure AI Language. Custom Named Entity Extraction (Custom NER) in Azure AI Language is the correct choice because it allows you to train a model to recognize domain-specific entities like legal clauses and party names from your own labeled data. Pre-built NER only recognizes generic entity types (e.g., person, organization, location) and cannot be customized for legal terminology. This makes Custom NER the only option that meets the requirement to identify specific clauses and parties unique to legal documents.

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Pre-built Named Entity Recognition in Azure AI Language

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Text Analytics for Health

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Immersive Reader

Question 13

You are deploying a question answering solution using Azure AI Language. The solution must be able to provide answers from a set of frequently asked questions (FAQs) in PDF format. What should you do?

Accepted Answer

Create a custom question answering project and add the PDF as a source.. Azure AI Language's custom question answering feature allows you to directly upload a PDF as a knowledge source. The service automatically extracts Q&A pairs from the document, enabling the solution to answer questions based on the FAQ content without needing additional search or cognitive skill pipelines.

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Use Azure AI Search with cognitive skills.

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Use Azure OpenAI with a system prompt containing the PDFs.

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Use the pre-built question answering in Azure AI Language.

Question 14

You are building a conversational AI solution that must handle multiple intents in a single user utterance. Which Azure AI feature should you use?

Accepted Answer

Use the orchestration workflow feature in Conversational Language Understanding.. The orchestration workflow feature in Conversational Language Understanding (CLU) is designed to handle multiple intents within a single user utterance by connecting to different projects (e.g., CLU, QnA Maker, or custom question answering) and routing the utterance to the appropriate service. This allows the solution to parse complex utterances that may trigger multiple intents across different domains, which is exactly what the scenario requires.

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Use QnA Maker with multiple QnA pairs.

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Use a single Conversational Language Understanding project with multiple intents.

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Use Azure Bot Service with multiple dialogs.

Question 15

Your NLP solution uses custom text classification in Azure AI Language. You need to improve the model's accuracy. Which action should you take?

Accepted Answer

Add more labeled training data with balanced classes.. Custom text classification in Azure AI Language relies on supervised learning, where model accuracy is directly proportional to the quantity and quality of labeled training data. Adding more labeled examples with balanced class distributions reduces bias, improves generalization, and helps the model learn distinguishing features more effectively, which is the most impactful action for improving accuracy.

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Increase the number of training epochs.

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Decrease the batch size.

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Use a different pretrained model.