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

Question 1

Refer to the exhibit. You are configuring an Azure AI Language resource using an ARM template. The settings include PII recognition with domain set to 'phi'. What is the effect of this setting?

Accepted Answer

It will detect protected health information (PHI) such as medical record numbers.. When you set the domain to 'phi' in PII recognition for Azure AI Language, the service is configured to detect protected health information (PHI) as defined by HIPAA, such as medical record numbers, health plan numbers, and patient names. This is a specialized subset of PII that focuses on health-related sensitive data, not general health entities or standard PII categories. Option D correctly identifies this behavior.

Answer

It will detect all standard PII categories plus additional health-specific entities.

Answer

It will enable the entity recognition model to detect health entities.

Answer

It will ignore PII detection and only detect health-related entities.

Question 2

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. The conversational language understanding (CLU) API endpoint requires the `/language/:analyze-conversations` path with a colon prefix before the operation name, which is the correct syntax for the Azure AI Language REST API. The `api-version=2022-10-01-preview` parameter is required to specify the preview version that supports CLU. This URL structure targets the analyze-conversations operation specifically for CLU, not the general text analytics or root endpoint.

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 3

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.. It combines Azure AI Language's built-in opinion mining with a custom NER model to extract product features, then uses post-processing logic to correctly associate sentiments with those features. This approach addresses the core limitation of opinion mining, which can misalign sentiments when multiple features with contrasting sentiments appear in the same sentence. By first extracting features via custom NER, you can then map each sentiment phrase to the nearest or most relevant extracted entity, improving accuracy without sacrificing scalability or cost-effectiveness.

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 4

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. CLU prebuilt entity components provide ready-made entity extraction for common types like dates and locations without requiring manual labeling. This minimizes manual effort while still allowing the chatbot to understand user intents and extract entities, as the prebuilt components are domain-agnostic and cover a wide range of entity types out of the box.

Answer

Conversational Language Understanding (CLU) Orchestration Workflow

Answer

CLU prebuilt intents

Answer

Azure AI QnA Maker

Question 5

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. A custom speech model is required because the company needs to recognize specific domain terms like caller names and account numbers, which are not part of the default base speech model. By training a custom speech model with relevant audio and text data, Azure AI Speech can accurately transcribe these specialized terms, improving recognition accuracy for the call center scenario.

Answer

Speaker recognition

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Pronounciation assessment

Answer

Batch transcription

Question 6

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. Text Analytics for health is a specialized Azure AI Language feature designed to extract and label medical entities from unstructured clinical text, such as diagnoses, medications, and procedures. It also includes a built-in PHI detection and redaction capability that automatically identifies and masks protected health information, enabling HIPAA compliance without requiring custom model training.

Answer

Azure Purview Information Protection

Answer

Custom named entity recognition (NER)

Answer

Azure AI Document Intelligence

Question 7

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. Conversational Language Understanding (CLU) is the Azure AI Language service that enables intent recognition and entity extraction from user utterances. When building a bot with Microsoft Copilot Studio, you can integrate a CLU app to handle complex natural language understanding, allowing the bot to route user intents to appropriate topics or actions.

Answer

A QnA Maker knowledge base

Answer

An Azure AI Translator resource

Answer

An Azure AI Bot Service bot

Question 8

You are an AI engineer at a global e-commerce company. The company uses Azure AI Language to analyze product reviews in English, Spanish, and French. The current solution calls the sentiment analysis API for each review individually, resulting in high latency and cost. You need to design a new solution that processes reviews in batches, reduces the number of API calls, and still supports multiple languages. The solution must also extract key phrases and detect the language automatically. You have the following options:

Option A: Use the Azure AI Language synchronous API with the 'multi-language' parameter set to true. Send reviews one by one.

Option B: Use the Azure AI Language asynchronous batch API. Combine all reviews into a single batch request, but only for one language at a time.

Option C: Use the Azure AI Language asynchronous batch API. Send a single batch request with all reviews, setting the 'language' parameter to 'multi' to auto-detect language, and specify sentiment analysis, key phrase extraction, and language detection as tasks.

Option D: Use the Azure AI Translator service to translate all reviews to English, then use the Azure AI Language batch API for English-only sentiment and key phrase extraction.

Accepted Answer

Option C. The asynchronous batch API supports multiple tasks (sentiment, key phrases, language detection) in a single request, and setting language to 'multi' enables auto-detection. This reduces API calls and latency. Option A is wrong because synchronous one-by-one calls increase latency/cost. Option B is wrong because it processes only one language per batch, requiring multiple batches. Option D is wrong because it adds translation cost and latency, and may lose nuances.

Answer

Option B

Answer

Option A

Answer

Option D

Question 9

You are an AI developer at a legal firm. The firm uses Azure AI Language to extract entities from legal documents. The current custom NER model is trained on a small dataset and has low accuracy for certain entity types like 'Statute' and 'Case Citation'. You need to improve the model's performance without increasing the labeling effort significantly. You have the following options:

Option A: Add more labeled examples for the underperforming entity types by manually labeling additional documents.

Option B: Use the prebuilt entity recognition model from Azure AI Language and map its outputs to custom entities.

Option C: Enable active learning in the custom NER project and review the suggested labels from the model.

Option D: Train a new model using the Azure Machine Learning automated ML (AutoML) for text classification.

Accepted Answer

Option A. Active learning in Azure AI Language custom NER automatically identifies the most informative unlabeled documents and suggests labels for them, which directly targets the underperforming entity types ('Statute' and 'Case Citation') without requiring manual labeling of the entire dataset. This reduces labeling effort significantly while improving model accuracy by focusing on ambiguous or high-uncertainty examples.

Answer

Option D

Answer

Option B

Answer

Option C

Question 10

You are designing a solution to analyze customer call transcripts using Azure AI Language. The solution must extract key phrases, detect sentiment per utterance, and identify the customer's intent (e.g., 'cancel subscription', 'technical support'). The data is stored in Azure Blob Storage and processed in near real-time. Which combination of Azure AI Language features and processing pattern should you use?

Accepted Answer

Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection.. It combines the conversation summarization API (which provides utterance-level sentiment and key phrase extraction) with an orchestration workflow model that routes to a custom conversational language understanding (CLU) project for intent detection. This pattern supports near real-time processing of call transcripts from Azure Blob Storage, meeting the requirements for per-utterance sentiment, key phrase extraction, and intent identification.

Answer

Use custom text classification to classify each utterance into intent categories and use the sentiment analysis API on the entire transcript.

Answer

Use the prebuilt key phrase extraction API to identify important terms and the prebuilt sentiment analysis API for overall transcript sentiment, then map intents via a rules-based approach.

Answer

Use the prebuilt conversational language understanding model for intent detection and Azure AI Language sentiment analysis API for utterance-level sentiment, processing each utterance independently via Azure Functions.

Question 11

Your company uses Azure AI Language to analyze customer feedback from surveys. The current pipeline extracts key phrases and sentiment. The data science team wants to identify emerging topics over time, such as new product complaints or feature requests. You need to modify the pipeline to track topic evolution. Which Azure AI Language feature should you enable?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows you to define and train a model to categorize feedback into custom classes (e.g., 'new product complaint', 'feature request') and track their frequency over time, enabling topic evolution analysis. Unlike prebuilt features, custom classification adapts to your specific domain and can be retrained as new topics emerge, directly supporting the data science team's goal of identifying emerging topics.

Answer

Named entity recognition (NER)

Answer

Conversation summarization

Answer

Key phrase extraction

Question 12

You are building a solution to extract custom entities from legal contracts using Azure AI Language. You have a small set of labeled documents. Which two features should you use to build and improve the custom NER model? (Choose two.)

Accepted Answer

Use active learning to automatically suggest new labels from unlabeled documents.. Active learning in Azure AI Language automatically identifies unlabeled documents where the model has low confidence and suggests them for labeling, which improves the custom NER model iteratively with minimal manual effort. This feature is specifically designed to reduce the labeling burden while maximizing model accuracy by focusing on the most informative samples.

Answer

Use the prebuilt NER model as a base and extend it with custom entities.

Answer

Configure an orchestration workflow to route documents to the best model.

Answer

Extract key phrases from the documents and use them as features.

Question 13

You are a developer at a large financial institution. The compliance team needs to automatically analyze quarterly earnings call transcripts to extract forward-looking statements (e.g., 'we expect revenue to grow') and flag any that are overly optimistic or lack necessary disclaimers. The transcripts are stored as text files in Azure Blob Storage. You need to design a solution using Azure AI Language services that meets the following requirements: 1) Extract all forward-looking statements from each transcript. 2) For each statement, determine if it contains optimistic language (e.g., 'strong growth', 'excellent performance') and if it includes a disclaimer (e.g., 'this is a forward-looking statement'). 3) Output a structured JSON file per transcript with the statements, optimism score, and disclaimer presence. 4) Minimize development effort and avoid custom machine learning model training. Which approach should you take?

Accepted Answer

Use key phrase extraction to identify important phrases, then run sentiment analysis on each sentence to detect optimism.. It uses prebuilt Azure AI Language features (key phrase extraction and sentiment analysis) that require no custom ML model training, meeting the requirement to avoid custom training. Key phrase extraction can identify potential forward-looking phrases, and sentiment analysis on each sentence can provide a per-statement optimism score. Disclaimer presence can be inferred by checking for specific phrases like 'forward-looking statement' using simple text matching or by incorporating the sentiment analysis result for that sentence. This approach minimizes development effort while providing structured output per transcript.

Answer

Use the prebuilt named entity recognition (NER) to identify entities related to financial terms, then apply sentiment analysis to the entire transcript to determine overall optimism.

Answer

Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.

Answer

Use custom question answering to create a knowledge base of typical forward-looking statements and query the transcript for matches.

Question 14

Your company runs a global e-commerce platform. You are building a chatbot using Azure AI Language's conversational language understanding (CLU) to handle customer requests in multiple languages. The bot must support English, German, and Japanese. You have labeled training data in English only. The deadline is tight, and you want to minimize manual labeling. You also need to ensure that the bot can gracefully handle unsupported languages (e.g., French) by directing the user to a human agent. You have access to Azure AI Translator. Which approach should you take?

Accepted Answer

Use a single CLU project with multilingual option enabled, train on English data only. Configure the bot to detect the language of user input; if it is English, German, or Japanese, route to CLU; otherwise, route to a human agent.. Azure AI Language's CLU supports a multilingual option that allows a single project to handle multiple languages without requiring translated training data. By enabling this option and training on English data only, the model can generalize to German and Japanese due to shared multilingual embeddings. The bot can then detect the user's language and route unsupported languages like French to a human agent, minimizing manual labeling while meeting the deadline.

Answer

Use a single CLU project with English data only. Translate the English training data into German and Japanese using Azure AI Translator, then train a single multilingual model by including the translated data.

Answer

Use a single CLU project with English data only. Before calling CLU, translate non-English user input to English using Azure AI Translator. For unsupported languages, detect language and route to human agent.

Answer

Build separate CLU projects for English, German, and Japanese. Label training data in each language by translating the English data using Azure AI Translator.

Question 15

Your organization runs a popular news website. You want to use Azure AI Language to automatically generate summaries of news articles for the homepage. The summaries must be concise (under 100 words), extractive (selecting key sentences from the article), and available in both English and Spanish. You have a large corpus of articles in both languages. You need to implement a solution that requires minimal custom development and leverages Azure AI Language's prebuilt capabilities. Which approach should you take?

Accepted Answer

Use the prebuilt extractive summarization API in Azure AI Language for both languages, specifying the maximum summary length.. The prebuilt extractive summarization API in Azure AI Language natively supports both English and Spanish, can generate summaries under 100 words by specifying the maximum summary length, and requires no custom development. This directly meets the requirements for concise, extractive summaries with minimal effort.

Answer

Train a custom extractive summarization model using Azure AI Language's custom text summarization feature with labeled data in both languages.

Answer

Use the conversation summarization API to summarize each article.

Answer

Use the prebuilt abstractive summarization API to generate summaries.