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

Question 1

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 correct answer is A because the template sets 'defaultAction' to 'Deny' with no IP rules, blocking all network access. B (S0 SKU) supports API calls. C (custom subdomain) does not cause 403. D (kind) is correct for Language Service.

Answer

The SKU S0 does not support API calls from applications

Answer

The resource kind is set incorrectly for Language Service

Answer

The custom subdomain name is invalid

Question 2

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 correct answer is D because the 'multilingual' parameter set to true enables support for multiple languages. A (confidenceThreshold) is for prediction confidence. B (projectName) is just the name. C (language) sets the primary language but does not enable multilingual support.

Answer

projectName

Answer

language

Answer

confidenceThreshold

Question 3

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.. Option B is correct because using the Conversational Language Understanding (CLU) feature of Azure AI Language with a single API call to trigger both intent detection and entity extraction minimizes latency by combining both tasks in one request. Option A is wrong because using separate calls for intent and entity increases latency. Option C is wrong because LUIS is being replaced by CLU and does not support the latest features. Option D is wrong because QnA Maker is for FAQ-style Q&A, not for intent recognition.

Answer

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

Answer

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

Answer

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

Question 4

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.. Option C is correct because high accuracy on the test set but low accuracy on new data indicates overfitting, where the model learns the training data too well and fails to generalize. Option A is wrong because accuracy on the test set is high, so insufficient training data is not indicated. Option B is wrong because low confidence scores are not mentioned; overfitting can still produce high confidence. Option D is wrong because data leakage would cause inflated accuracy on the test set, but the test set accuracy is already high and the problem is with new data.

Answer

The model's confidence threshold is set too low.

Answer

The training data is not representative of the new tickets.

Answer

There is data leakage between the training and test sets.

Question 5

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. Option B is correct because the Standard (S) tier offers pay-as-you-go pricing for the key phrase extraction feature, which is cost-effective for moderate volumes. Option A is wrong because Free (F0) tier has a limit of 5,000 transactions per month, which may be exceeded. Option C is wrong because the Premium tier is for higher throughput and SLA, which may be overkill. Option D is wrong because the Basic tier does not exist for Azure AI Language.

Answer

Free (F0) tier

Answer

Premium (P) tier

Answer

Basic (B) tier

Question 6

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. Option A is correct because the Azure AI Language Health Text Analytics (preview) includes both PHI detection and medical entity recognition. Option B is wrong because the standard Text Analytics for health does not include PHI detection. Option C is wrong because the Conversation Analysis feature is for conversational data, not clinical notes. Option D is wrong because Custom Text Classification does not detect PHI or medical conditions out of the box.

Answer

Conversation Analysis

Answer

Custom Text Classification

Answer

Text Analytics for health (general availability)

Question 7

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.. Option B is correct because Azure AI Language's multilingual project allows you to train a single model that works across multiple languages, leveraging cross-lingual transfer. Option A is wrong because creating separate projects for each language increases maintenance effort. Option C is wrong because the Translate API adds complexity and latency. Option D is wrong because LUIS does not support multilingual projects as effectively as CLU.

Answer

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

Answer

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

Answer

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

Question 8

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.. Option C is correct because a 'mixed' label indicates that the text contains both positive and negative sentiments, even if the overall score leans positive. Option A is wrong because 'mixed' does not indicate a neutral sentiment. Option B is wrong because the overall score is positive, not negative. Option D is wrong because a 'mixed' label is a valid output.

Answer

The text is negative overall.

Answer

The text is neutral overall.

Answer

The analysis is inconclusive.

Question 9

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?

Accepted Answer

The model is overfitting to the training data.. Option D is correct because the model likely overfits to the training data, failing to generalize to variations. Option A is wrong because insufficient training data would cause poor performance on development data too. Option B is wrong because labeling consistency issues would affect training and development performance. Option C is wrong because the model is designed to extract patterns, but overfitting is the issue.

Answer

The training data size is insufficient.

Answer

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

Answer

The labeling is inconsistent across the dataset.

Question 10

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. Option A is correct because the Async Text Analytics API supports asynchronous processing of documents from Azure Blob Storage, performing key phrase extraction, sentiment analysis, and language detection. Option B is wrong because the conversational summarization API is for summarizing conversations, not for multiple analytics tasks. Option C is wrong because the opinion mining API only extracts opinions, not key phrases or language. Option D is wrong because the custom text classification API requires a trained model and does not perform key phrase extraction or sentiment out of the box.

Answer

Conversational Summarization API

Answer

Custom Text Classification API

Answer

Opinion Mining API

Question 11

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. Option B is correct because extractive summarization selects key sentences from the document. Option A is wrong because abstractive summarization generates new sentences, not extracts. Option C is wrong because key phrase extraction extracts phrases, not sentences. Option D is wrong because entity recognition extracts named entities.

Answer

Abstractive summarization

Answer

Key phrase extraction

Answer

Entity recognition

Question 12

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.. Options B and D are correct because you need to create a project and deploy the model for production use. Option A is wrong because the QnA Maker service is deprecated; Azure AI Language uses the Custom Question Answering feature. Option C is wrong because training data can be provided in multiple formats and the model is trained automatically. Option E is wrong because publishing to a web app bot is optional.

Answer

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

Answer

Create a QnA Maker service in the Azure portal.

Answer

Publish the project to a web app bot.

Question 13

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.. Options A, B, and E are correct because PII detection supports multiple languages, language detection can identify the document language, and the multilingual model can handle mixed-language documents. Option C is wrong because the Text Analytics for health is for medical data, not general PII. Option D is wrong because the conversation summarization API is for summarizing conversations, not detecting PII.

Answer

The conversation summarization API.

Answer

The Text Analytics for health feature.

Question 14

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. Options A, B, and C are correct because these are standard features. Option D is wrong because image analysis is part of Azure AI Vision. Option E is wrong because speech-to-text is part of Azure AI Speech.

Answer

Image captioning

Answer

Speech-to-text

Question 15

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.. Option B is correct because the CLU request must include the verbose parameter to return entity extractions. Without verbose, only the top intent is returned. Option A is wrong because the text clearly contains entities. Option C is wrong because the deployment is specified. Option D is wrong because the training data includes entities.

Answer

The deployment name is incorrect.

Answer

The model was not trained on entity extraction.

Answer

The utterance does not contain any entities.

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

Exhibit

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

Exhibit