AI-102 Implement natural language processing solutions Practice Test 5 — 15 Questions

Question 1

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.

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Azure AI Speech

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Azure AI Translator

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Azure AI Vision

Question 2

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.

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Key Phrase Extraction

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Extractive Summarization

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Conversational Language Understanding

Question 3

A company uses Azure AI Language Service with Custom Entity Recognition to extract invoice fields. The model correctly extracts invoice numbers but fails to extract dates in the format 'dd/mm/yyyy'. The training data includes dates in 'mm/dd/yyyy' format. What is the most likely issue?

Accepted Answer

The training data does not contain examples with the 'dd/mm/yyyy' format. Custom Entity Recognition in Azure AI Language Service learns patterns from labeled training data. Since the training data only contains dates in 'mm/dd/yyyy' format, the model has not seen any examples of 'dd/mm/yyyy' and therefore cannot generalize to that format. The model relies on the exact token sequences and date structures present in the training set, so missing format variations directly cause extraction failures.

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The dates exceed the maximum entity length

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The language detection is incorrectly identifying the locale

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The model is overfitting to invoice numbers

Question 4

Which TWO actions should you take to ensure that an Azure AI Language Service custom entity recognition model complies with data privacy regulations?

Accepted Answer

Configure data retention policies to delete data after processing. Configuring data retention policies to delete data after processing ensures that personally identifiable information (PII) is not stored longer than necessary, which is a key requirement for compliance with regulations like GDPR and CCPA. Option E is correct because anonymizing or removing PII from training data prevents sensitive information from being embedded in the custom entity recognition model, reducing the risk of data leakage during inference.

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Use prebuilt entity recognition models instead of custom

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Increase the number of training epochs

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Enable diagnostic logging for audit trails

Question 5

Which THREE components are required to build a custom question-answering solution using Azure AI Language Service?

Accepted Answer

A bot channel registration or web app to interact with users. A bot channel registration or web app is the front-end interface that allows users to submit questions and receive answers from the custom QnA solution. Without this interaction layer, the knowledge base cannot be queried by end users, making it an essential component for a complete solution.

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An Azure AI Search index

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An Azure Functions app for serverless compute

Question 6

Which Azure AI service can be used to analyze sentiment in text data?

Accepted Answer

Azure AI Language Service. Azure AI Language Service (formerly Text Analytics) includes a built-in sentiment analysis feature that evaluates text and returns sentiment labels (positive, negative, neutral, mixed) along with confidence scores at the sentence and document level. This makes it the primary service for analyzing sentiment in text data. Azure AI Content Safety is not designed for sentiment analysis; it is intended for harmful content moderation.

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Azure AI Translator

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Azure AI Vision

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Azure AI Content Safety

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Azure AI Speech

Question 7

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Accepted Answer

The projectName and deploymentName are missing from the body parameters. The Azure AI Language Service for custom entity recognition requires both `projectName` and `deploymentName` in the request body to identify which trained custom model to invoke. Without these parameters, the service cannot route the request to the correct custom model, so it returns no entities. The standard pre-built entity recognition does not require these fields, but custom entity recognition mandates them.

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The text input is too short for entity recognition

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The language parameter is set incorrectly

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The model version is not specified

Question 8

Refer to the exhibit. You deploy this ARM template to create an Azure AI Language Service resource. After deployment, you try to call the Language Service API from your application but receive a 403 Forbidden error. What is the most likely cause?

Accepted Answer

The network ACLs block all traffic because no IP rules are defined. The ARM template in the exhibit does not include any IP rules in the network ACLs section, which means the default behavior for Azure AI Language Service is to deny all traffic when network ACLs are explicitly configured. Even though the resource is deployed successfully, the absence of allowed IP ranges or a virtual network rule causes the service to block all API calls, resulting in a 403 Forbidden error.

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The SKU S0 does not support API calls from applications

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The resource kind is set incorrectly for Language Service

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The custom subdomain name is invalid

Question 9

Refer to the exhibit. You are creating a Conversational Language Understanding (CLU) project using the Azure AI Language Service REST API. You want the project to support both English and Spanish utterances. Which parameter in the request body enables this?

Accepted Answer

multilingual. The `multilingual` parameter, when set to `true` in the request body of the Azure AI Language Service REST API for a Conversational Language Understanding (CLU) project, enables the project to support multiple languages, such as English and Spanish, within a single project. This allows the model to learn from utterances in different languages and generalize across them, rather than requiring separate projects per language.

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projectName

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language

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confidenceThreshold

Question 10

A company is building a chatbot using Azure AI Language. The chatbot must detect user intent from utterances and also extract key entities like dates and product names. The solution must minimize latency for real-time conversation. Which approach should the team use?

Accepted Answer

Use the Conversational Language Understanding (CLU) feature of Azure AI Language, which supports both intent and entity extraction in a single API call.. The Conversational Language Understanding (CLU) feature of Azure AI Language is specifically designed to handle both intent recognition and entity extraction in a single API call, which minimizes latency for real-time conversations. CLU is the modern replacement for LUIS and is optimized for conversational scenarios, supporting orchestration and prebuilt entities like dates and product names.

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Use QnA Maker with a custom question-answer pair for each intent and entity.

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Use the Language Understanding (LUIS) service with a single call for both intent and entity extraction.

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Use two separate calls to the Azure AI Language API: one for intent recognition and one for entity extraction.

Question 11

You are developing a custom text classification model using Azure AI Language. The model must classify customer support tickets into 15 categories. You have 10,000 labeled examples. After training, the model shows 95% accuracy on the test set but only 60% on a small sample of new tickets. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. The model's 95% accuracy on the test set versus 60% on new tickets is a classic symptom of overfitting. In Azure AI Language custom text classification, overfitting occurs when the model learns noise and idiosyncrasies of the training data rather than generalizable patterns, causing poor performance on unseen data. The high accuracy on the test set but sharp drop on new tickets indicates the model memorized the training distribution and fails to generalize.

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The model's confidence threshold is set too low.

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The training data is not representative of the new tickets.

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There is data leakage between the training and test sets.

Question 12

You need to extract key phrases from a large collection of customer reviews using Azure AI Language. The solution should be cost-effective and process up to 1,000 documents per day. Which pricing tier should you choose?

Accepted Answer

Standard (S) tier. The Standard (S) tier is the correct choice because it supports up to 1,000 documents per day for key phrase extraction, which matches the stated requirement. The Free (F0) tier is limited to 5,000 text records per month (roughly 167 per day), making it insufficient for 1,000 documents daily. The Premium (P) tier is designed for high-throughput scenarios (millions of documents per day) and is overkill for this volume, while the Basic (B) tier does not exist for Azure AI Language's key phrase extraction API.

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Free (F0) tier

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Premium (P) tier

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Basic (B) tier

Question 13

A healthcare organization uses Azure AI Language to analyze clinical notes. They need to detect protected health information (PHI) such as patient names and dates of birth, and also identify medical conditions. Which Azure AI Language feature should they use?

Accepted Answer

Health Text Analytics (preview) in Azure AI Language. Health Text Analytics (preview) in Azure AI Language is the correct feature because it is specifically designed to extract and structure medical information from unstructured clinical text, including protected health information (PHI) such as patient names and dates of birth, as well as medical conditions. This preview API extends the Text Analytics for health capabilities with enhanced PHI detection and entity linking, making it the appropriate choice for healthcare compliance and clinical analysis.

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Conversation Analysis

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Custom Text Classification

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Text Analytics for health (general availability)

Question 14

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

Accepted Answer

Use a single CLU multilingual project that supports all three languages.. Azure AI Language's CLU (Conversational Language Understanding) supports multilingual projects, allowing you to train a single model that handles English, Spanish, and French. This minimizes development effort by avoiding separate projects for each language. Option A is wrong because translating all utterances to English adds latency and complexity, and may lose nuances. Option B is wrong because creating separate CLU projects for each language increases maintenance and deployment overhead. Option D is wrong because LUIS does not offer the same multilingual capabilities as CLU and is being deprecated.

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Use the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.

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Create a separate CLU project for each language and combine them with a routing mechanism.

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Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.

Question 15

You are using Azure AI Language to analyze sentiment in customer feedback. The analysis returns a sentiment label of 'mixed' for a review that contains both positive and negative statements. The overall sentiment score is 0.75 (positive). What does this indicate?

Accepted Answer

The text contains both positive and negative sentiments.. The 'mixed' sentiment label indicates that the text contains both positive and negative statements. The overall sentiment score of 0.75 (positive) reflects the aggregate confidence that the text is positive, but the label 'mixed' is assigned when the model detects significant conflicting sentiments, meaning the text is not uniformly positive or negative. This is a key behavior of Azure AI Language's sentiment analysis, which provides both a label and a score for the overall document.

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The text is negative overall.

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The text is neutral overall.

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The analysis is inconclusive.