AI-102 Implement natural language processing solutions Practice Test 11 — 15 Questions

Question 1

Refer to the exhibit. You have an Azure AI Language resource named MyLangService. You need to call the conversational language understanding (CLU) API. Which URL should you use?

Accepted Answer

https://mylangservice.cognitiveservices.azure.com/language/:analyze-conversations?api-version=2022-10-01-preview. Option A is correct because the CLU endpoint is shown as the full URL with the path '/language/:analyze-conversations'. Option B is wrong because that URL is for Text Analytics. Option C is wrong because it uses the Text Analytics base URL incorrectly. Option D is wrong because it uses an invalid path.

Answer

https://mylangservice.cognitiveservices.azure.com/text/analytics/v3.0/sentiment

Answer

https://mylangservice.cognitiveservices.azure.com/language/analyze-conversations?api-version=2022-10-01-preview

Answer

https://mylangservice.cognitiveservices.azure.com/

Question 2

You are a developer at a large retail company. The company receives thousands of product reviews daily. You need to build a solution that automatically categorizes reviews into positive, negative, and neutral sentiments, and also extracts key product features mentioned (e.g., battery life, screen quality) along with their associated sentiments. The solution must be scalable and cost-effective. You have access to Azure AI Language. You decide to use the built-in sentiment analysis and opinion mining features. However, after initial testing, you find that the opinion mining feature does not always correctly associate sentiments with the correct product features. For example, in the review 'The battery life is great but the screen is terrible', opinion mining might incorrectly associate 'terrible' with 'battery life'. You need to improve the accuracy of feature-sentiment association. What should you do?

Accepted Answer

Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.. Option B is correct because by training a custom NER model to extract product features, you can then use the opinion mining results but map them to your custom entities more accurately. Option A is wrong because switching to CLU is for intent classification, not for fine-grained aspect sentiment. Option C is wrong because sentiment analysis without opinion mining loses aspect-level detail. Option D is wrong because PII detection is irrelevant.

Answer

Use the PII recognition feature to identify product features as entities.

Answer

Use Conversational Language Understanding (CLU) to define intents for each product feature and train a model with labeled utterances.

Answer

Use the standard sentiment analysis API without opinion mining, and then use key phrase extraction to identify features and assign overall sentiment.

Question 3

A company is building a chatbot using Azure AI Language. The chatbot must understand user intents and extract entities like dates and locations. The solution should minimize manual labeling effort. Which feature should the team use?

Accepted Answer

CLU prebuilt entity components. Option C is correct because the CLU prebuilt entity components can automatically recognize common entities like dates and locations without custom labeling. Option A is wrong because Orchestration Workflow routes to different projects but does not extract entities. Option B is wrong because prebuilt intents cover common intents, not entities. Option D is wrong because QnA Maker is for FAQ, not intent/entity extraction.

Answer

Conversational Language Understanding (CLU) Orchestration Workflow

Answer

CLU prebuilt intents

Answer

Azure AI QnA Maker

Question 4

A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?

Accepted Answer

Use active learning in the custom text classification project. Option B is correct because Azure AI Language supports active learning, which suggests labels and reduces manual effort. Option A is wrong because manual labeling of 10k docs is inefficient. Option C is wrong because Azure Machine Learning designer is not designed for text classification labeling. Option D is wrong because prebuilt models cannot handle custom categories.

Answer

Use a prebuilt model from the Azure AI Language service

Answer

Manually label all documents in the Language Studio

Answer

Use Azure Machine Learning designer to auto-label

Question 5

A multinational corporation uses Azure AI Language to analyze customer feedback in multiple languages. The solution must detect the language of incoming text and then perform sentiment analysis. Which approach minimizes latency and cost?

Accepted Answer

Use the sentiment analysis API with multilingual support. Option D is correct because Azure AI Language's multilingual sentiment analysis can detect language and sentiment in one call. Option A is wrong because calling two separate endpoints increases latency and cost. Option B is wrong because Translator is an extra service. Option C is wrong because Azure AI Search is for indexing, not sentiment.

Answer

Use the Translator service to translate text to English, then call sentiment analysis

Answer

Store text in Azure AI Search and use cognitive skills for sentiment

Answer

Call the language detection API followed by the sentiment analysis API

Question 6

A company uses Azure AI Speech to transcribe customer service calls. They need to identify callers by name and account number. Which feature should be enabled?

Accepted Answer

Custom speech model. Option A is correct because custom speech models improve recognition of domain-specific terms like names and account numbers. Option B is wrong because speaker recognition identifies who is speaking, not specific words. Option C is wrong because pronounciation assessment evaluates pronunciation, not recognition. Option D is wrong because batch transcription is for large volumes, not real-time.

Answer

Speaker recognition

Answer

Pronounciation assessment

Answer

Batch transcription

Question 7

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and redact protected health information (PHI). Which capability should the team configure?

Accepted Answer

Text Analytics for health. Option D is correct because the Azure AI Language health feature includes PHI detection and redaction. Option A is wrong because custom entity extraction does not automatically redact PHI. Option B is wrong because Azure Purview is for data governance, not real-time redaction. Option C is wrong because Azure AI Document Intelligence is for document processing, not language-specific PHI.

Answer

Azure Purview Information Protection

Answer

Custom named entity recognition (NER)

Answer

Azure AI Document Intelligence

Question 8

A developer is building a multilingual translation solution using Azure AI Translator. The solution must translate text to French, German, and Spanish. Which parameter should the developer set to specify the target language?

Accepted Answer

to. Option B is correct because the 'to' parameter specifies the target language. Option A is wrong because 'from' specifies source language. Option C is wrong because 'language' is not a REST API parameter. Option D is wrong because 'targetLanguage' is not the correct parameter name.

Answer

language

Answer

targetLanguage

Answer

from

Question 9

A company uses Azure AI Speech for real-time captioning during live events. They notice a delay of 5 seconds between speech and caption display. Which action should they take to reduce latency?

Accepted Answer

Use the Speech SDK with intermediate results enabled. Option A is correct because using the SDK with intermediate results reduces perceived latency by displaying partial captions. Option B is wrong because batch transcription is for offline processing, not real-time. Option C is wrong because increasing maxAlternatives increases processing. Option D is wrong because custom models improve accuracy, not necessarily latency.

Answer

Deploy a custom speech model

Answer

Switch to batch transcription API

Answer

Increase the maxAlternatives parameter

Question 10

A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?

Accepted Answer

Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part. Option C is correct because the CLU entity linking can link mentions to entities, but for speaker-level sentiment, they need to process each utterance separately. Option A is wrong because QnA Maker is for FAQ. Option B is wrong because prebuilt sentiment analysis does not differentiate speakers. Option D is wrong because Text Analytics for health is for healthcare, not financial.

Answer

Call the prebuilt sentiment analysis API on the entire transcript

Answer

Use QnA Maker to extract Q&A pairs per speaker

Answer

Use Text Analytics for health to extract entities and sentiment

Question 11

A developer is creating a custom question answering project in Azure AI Language. The knowledge base contains product manuals in PDF format. Which step is essential before importing the PDFs?

Accepted Answer

Ensure PDFs are in a supported format and accessible. Option D is correct because PDFs must be in a supported format and accessible via URL or uploaded. Option A is wrong because QnA Maker is deprecated in favor of custom question answering. Option B is wrong because Azure AI Search is not required. Option C is wrong because translation is not necessary.

Answer

Create an Azure AI Search index

Answer

Deploy a QnA Maker service

Answer

Translate PDFs to English

Question 12

A company is building a bot using Microsoft Copilot Studio (formerly Power Virtual Agents). They want to use Azure AI Language to understand user intents. Which TWO components are required?

Accepted Answer

A Conversational Language Understanding (CLU) app. Option A and Option D are correct. A CLU app is needed for intent recognition, and a Microsoft Copilot Studio bot is the interface. Option B is wrong because QnA Maker is deprecated. Option C is wrong because Azure AI Bot Service is not required if using Copilot Studio. Option E is wrong because Azure AI Translator is for translation, not intent recognition.

Answer

A QnA Maker knowledge base

Answer

An Azure AI Translator resource

Answer

An Azure AI Bot Service bot

Question 13

A company uses Azure AI Language to analyze customer reviews. They need to detect sentiment, extract key phrases, and identify named entities. Which THREE capabilities should they combine?

Accepted Answer

Named entity recognition (NER). Options A, B, and D are correct. Sentiment analysis, key phrase extraction, and named entity recognition are distinct capabilities in Azure AI Language. Option C is wrong because summarization is a separate skill. Option E is wrong because language detection is not needed if language is known.

Answer

Abstractive summarization

Answer

Language detection

Question 14

A developer is deploying a custom text classification model in Azure AI Language. The model must be accessible via a REST API with low latency. Which TWO actions should the developer take?

Accepted Answer

Obtain the endpoint URL and primary key from Language Studio. Options A and C are correct. Deploying the model as a real-time endpoint is required for low-latency REST API access. Using the correct endpoint and key is also necessary. Option B is wrong because batch processing is not low-latency. Option D is wrong because a 'test' endpoint does not exist. Option E is wrong because export/import is not needed for deployment.

Answer

Use the batch processing API

Answer

Export the model as a Docker container

Answer

Deploy to a test endpoint in the Azure portal

Question 15

A company uses Azure AI Speech to provide real-time transcription for customer support calls. The solution must handle multiple languages and filter out profanity. Which THREE configurations are needed?

Accepted Answer

Set the SpeechConfig.SpeechRecognitionLanguage property. Options A, B, and D are correct. SpeechConfig with source language configuration, profanity filter settings, and using the Speech SDK with intermediate results are necessary. Option C is wrong because batch transcription is not real-time. Option E is wrong because custom speech models are not required for profanity filtering.

Answer

Use the Batch Transcription REST API

Answer

Deploy a custom speech model for each language