AI-102 Implement natural language processing solutions Practice Test 12 — 15 Questions

Question 1

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. The endpoint URL and primary key are required to authenticate and route REST API requests to the deployed model. Language Studio provides these credentials after the model is deployed to a real-time endpoint, enabling low-latency inference via the Azure AI Language API.

Answer

Use the batch processing API

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Export the model as a Docker container

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Deploy to a test endpoint in the Azure portal

Question 2

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. To achieve real-time transcription with multi-language support and profanity filtering using Azure AI Speech, three configurations are required. First, set the `SpeechConfig.SpeechRecognitionLanguage` property (Option C) to specify the source language for accurate transcription. Second, use the Speech SDK with intermediate results (Option D) to provide real-time feedback during the call. Third, enable the ProfanityFilter option in the Speech SDK (Option E) to filter out profanity. Option A (Batch Transcription) is incorrect because it is not real-time. Option B (custom speech model per language) is unnecessary because the standard prebuilt models support multiple languages and profanity filtering without customization.

Answer

Use the Batch Transcription REST API

Answer

Deploy a custom speech model for each language

Question 3

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

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Option A

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Option D

Question 4

You are a solution architect at a media company. The company uses Azure AI Speech to generate subtitles for videos. The current solution uses the batch transcription API and takes several hours to process a 1-hour video. The business requires near-real-time subtitles for live streaming events. You need to design a new solution that provides low-latency transcription. You have the following options:

Option A: Use the batch transcription API with a higher priority queue.

Option B: Use the Speech-to-text REST API for real-time streaming with the Speech SDK.

Option C: Use the Azure AI Language API to transcribe audio from a file.

Option D: Use Azure AI Video Indexer to generate subtitles.

Accepted Answer

Option B. The Speech-to-text REST API with the Speech SDK supports real-time streaming transcription, which provides low-latency results suitable for live streaming events. Unlike the batch transcription API, which processes audio asynchronously and can take hours, the streaming API processes audio chunks in near-real-time, returning partial and final results with sub-second latency.

Answer

Option C

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Option D

Answer

Option A

Question 5

A company is building a chatbot using Azure OpenAI Service to handle customer inquiries. The bot sometimes responds with incorrect or fabricated information. The team wants to ground the model responses using their own product documentation stored in Azure Cognitive Search. Which configuration should they implement?

Accepted Answer

Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.. It directly integrates Azure Cognitive Search as a data source for Azure OpenAI, enabling the model to retrieve and ground its responses in the indexed product documentation. This approach uses a 'retrieve-then-read' pattern where the search results are injected into the prompt, reducing hallucinations by constraining the model's output to verified content.

Answer

Use Azure AI Document Intelligence to extract text and generate embeddings, then store them in a vector database for direct similarity search.

Answer

Fine-tune the GPT model on the product documentation dataset.

Answer

Enable the semantic ranker in Azure Cognitive Search to improve the relevance of search results.

Question 6

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 7

A healthcare organization needs to redact personally identifiable information (PII) from patient records before using them for research. They have large volumes of unstructured text in multiple languages. Which Azure AI service should they use?

Accepted Answer

Azure AI Language (PII detection feature). Azure AI Language's PII detection feature is specifically designed to identify and redact personally identifiable information from unstructured text. It supports multiple languages and can handle large volumes of text, making it the correct choice for redacting PII from patient records before research use.

Answer

Azure AI Content Safety

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Azure AI Document Intelligence (formerly Form Recognizer)

Answer

Azure AI Translator

Question 8

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)

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Conversation summarization

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Key phrase extraction

Question 9

You are building a multilingual customer support chatbot using Azure AI Language. The bot must understand user intents in English, Spanish, and French. You have pre-existing labeled data in English only. The solution should minimize manual labeling effort. Which approach should you recommend?

Accepted Answer

Use the multilingual option in conversational language understanding (CLU) and train on the English data only.. Azure AI Language's conversational language understanding (CLU) supports a multilingual project option that allows you to train a single model on labeled data in one language (e.g., English) and have it generalize to understand intents in other languages (e.g., Spanish and French) without needing additional labeled data. This directly minimizes manual labeling effort while still leveraging the pre-existing English data.

Answer

Use Azure AI Translator to detect the language and route to a rules-based intent handler for each language.

Answer

Build a separate CLU project for each language and use the English data to bootstrap labeling with active learning.

Answer

Translate the English labeled data into Spanish and French using Azure AI Translator, then train a separate CLU model per language.

Question 10

You are deploying an Azure AI Language custom text classification model. You need to ensure the model meets performance requirements before promoting it to production. Which two actions should you take? (Choose two.)

Accepted Answer

Evaluate the model on a held-out test set that was not used during training.. Evaluating the model on a held-out test set that was not used during training provides an unbiased estimate of its generalization performance. In Azure AI Language custom text classification, the training portal automatically splits your data into training and testing sets, but you can also upload your own test set. This ensures the model's accuracy reflects how it will perform on unseen production data, avoiding overfitting.

Answer

Ensure the model achieves at least 95% accuracy on a cross-validation split.

Answer

Use the training set to compute accuracy and ensure it is above 90%.

Answer

Compare the model's performance to a baseline model that always predicts the most common class.

Question 11

Your organization uses Azure AI Language to perform sentiment analysis and opinion mining on product reviews. You notice that the sentiment scores are often neutral even when the review text contains clearly positive or negative opinions. You suspect the model is not capturing the nuances. Which three actions could improve the sentiment analysis accuracy? (Choose three.)

Accepted Answer

Provide more labeled training examples that cover a wider variety of writing styles and sentiments.. Providing more labeled training examples that cover a wider variety of writing styles and sentiments directly improves the custom model's ability to learn nuanced patterns. Azure AI Language's custom sentiment analysis relies on supervised learning; more diverse, high-quality labeled data helps the model generalize better and reduces the tendency to default to neutral scores for ambiguous or complex reviews.

Answer

Pre-process the text with key phrase extraction to highlight important terms before sentiment analysis.

Answer

Use the basic sentiment analysis API without any customization.

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.

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

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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

You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?

Accepted Answer

Use custom text classification for category and custom named entity recognition for product name extraction.. Custom text classification can be trained on the labeled dataset to categorize tickets, and custom named entity recognition (NER) can be trained to extract product names from the text. Both services are part of the Azure AI Language resource, allowing a single resource to handle both tasks. This approach is cost-effective and easy to retrain as new categories emerge. Option A is incorrect because key phrase extraction is a prebuilt feature that returns general key phrases, not specifically trained to extract product names, and may miss them or include irrelevant phrases. Option B is incorrect because conversational language understanding (CLU) is optimized for multi-turn conversational flows and requires more complex configuration, making it less suitable for single-turn ticket classification and less cost-effective. Option D is incorrect because prebuilt NER only recognizes common entity types (e.g., person, organization) and cannot extract custom product names unless they match those predefined types.

Answer

Use custom text classification for category and key phrase extraction for product name.

Answer

Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.

Answer

Use custom text classification for category and prebuilt named entity recognition for product name extraction.