AI-102 Practice Test 35 — 15 Questions

Question 1

A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?

Accepted Answer

Language Detection. The correct answer is A because Language Detection is the feature specifically designed to identify the language of text. B (Key Phrase Extraction) extracts key phrases without language detection; C (Sentiment Analysis) determines sentiment; D (Entity Recognition) identifies entities like names and locations.

Answer

Sentiment Analysis

Answer

Key Phrase Extraction

Answer

Entity Recognition

Question 2

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 3

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 4

You are using Azure AI Translator to translate documents from English to French. Some technical terms must remain untranslated. How should you handle this?

Accepted Answer

Provide a dictionary with the terms and their translations set to the same word. Azure AI Translator supports a dictionary to specify words that should not be translated. The 'includeUntranslated' parameter does not exist. Post-editing is inefficient. Using a custom model requires training data.

Answer

Set the includeUntranslated parameter to true

Answer

Train a custom translation model that ignores those terms

Answer

Post-process the output to revert translations of those terms

Question 5

A healthcare organization deploys an Azure AI Language Service custom entity recognition model to extract medical conditions from clinical notes. During testing, the model fails to recognize rare diseases mentioned in the training data. What is the most likely cause?

Accepted Answer

The model requires more labeled examples of rare diseases. The correct answer is D because custom entity recognition models require sufficient examples per entity; rare diseases with few examples lead to poor recognition. A (overfitting) would cause high training accuracy but poor generalization, but the issue is specifically with rare diseases. B (imbalanced training data) could be a factor, but the primary cause is the low number of examples. C (entity length limit) is not relevant as rare diseases are not necessarily long.

Answer

The entity length exceeds the maximum allowed for the model

Answer

The model is overfitting to common diseases

Answer

The training data is imbalanced with too many common diseases

Question 6

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.. Option D is correct because it uses the multilingual CLU model for English, German, and Japanese (the model can predict in those languages without additional labeled data) and uses a language detection step to route unsupported languages to a human agent. Option A is wrong because building separate projects for each language requires labeling in each language, which is not minimal effort. Option B is wrong because translating user input introduces latency and potential translation errors. Option C is wrong because translating the English data and training separate models increases effort and may not work well due to translation quality.

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 7

A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?

Accepted Answer

Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.. Custom NER in Azure AI Language is designed for training custom entity extraction models with labeled data. It supports training from scratch using your own dataset. Option A is correct. Option B is wrong because prebuilt models do not cover specialized entities. Option C is wrong because using a different service like Logic Apps adds complexity. Option D is wrong because two models would be redundant and more work.

Answer

Use the prebuilt NER model and map the recognized entities to the required types.

Answer

Use Azure Logic Apps to call the Text Analytics API and post-process the results.

Answer

Train a custom NER model for genes and use prebuilt NER for chemicals.

Question 8

A company is developing a conversational AI solution using Microsoft Copilot Studio. They want the copilot to answer questions based on a knowledge base of technical documents. Which data source integration should they use?

Accepted Answer

Azure AI Search. The correct answer is B because Microsoft Copilot Studio can use Azure AI Search as a knowledge source for retrieval-augmented generation. A (Azure SQL Database) is not directly supported as a knowledge base for Copilot Studio. C (Azure Blob Storage) requires additional indexing. D (Microsoft Lists) is for simpler data.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Microsoft Lists

Question 9

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.

Question 10

You need to analyze customer call transcripts to identify positive and negative sentiment. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment Analysis. Option A is correct because Sentiment Analysis is designed for that purpose. Option B is incorrect because Key Phrase Extraction extracts key phrases. Option C is incorrect because Entity Recognition identifies entities. Option D is incorrect because Language Detection identifies language.

Answer

Language Detection

Answer

Named Entity Recognition

Answer

Key Phrase Extraction

Question 11

Your organization uses Azure AI Language for custom text classification. You have deployed a model to a dedicated endpoint. After updating the training data, you retrain and redeploy the model. Users report that the endpoint still returns predictions from the old model. What is the most likely cause?

Accepted Answer

The new model is not yet deployed; you must deploy it to the endpoint. The endpoint might still be pointing to the old trained model. You need to update the endpoint assignment to the new model version. Rebuilding the project or deleting the endpoint would be disruptive. Caching doesn't apply in this context.

Answer

The training data changes are not saved

Answer

The project needs to be rebuilt from scratch

Answer

The endpoint has a caching issue

Question 12

Your organization needs to analyze customer call transcripts to extract key insights, including sentiment, issues, and resolution. Which THREE Azure AI Language features should you use?

Accepted Answer

Custom named entity recognition. Conversation summarization summarizes call transcripts; custom NER can extract specific entities like issues and resolutions; sentiment analysis detects sentiment. Key phrase extraction and PII detection are not the primary needs.

Answer

Key phrase extraction

Answer

PII detection

Question 13

You are using Azure AI Language to analyze medical records. The built-in NER model does not recognize some medical terms. What should you do?

Accepted Answer

Train a custom NER model using Azure AI Language. Custom NER allows training a model on your specific terms. Text Analytics for Health is a specialized built-in model for healthcare but may not cover all custom terms. Custom Text Classification is for document classification. Entity Linking disambiguates to a knowledge base.

Answer

Use the Text Analytics for Health feature

Answer

Use Entity Linking

Answer

Use Custom Text Classification

Question 14

Your organization needs to analyze customer feedback from social media posts to determine the sentiment (positive, negative, neutral). The solution must process up to 10,000 posts per day and provide a confidence score for each sentiment. Which Azure AI service should you use?

Accepted Answer

Azure AI Language Service. Option A is correct because Azure AI Language provides sentiment analysis with confidence scores as a built-in feature. Option B is wrong because the Translator service translates text, it does not analyze sentiment. Option C is wrong because the Speech service processes audio, not text. Option D is wrong because the Language Understanding service (LUIS) is for intent and entity extraction, not sentiment analysis.

Answer

Azure AI Speech Service

Answer

Azure AI Translator

Answer

Azure AI Language Understanding (LUIS)

Question 15

You are using the Azure AI Language service to process customer reviews. You need to extract the following insights: overall sentiment, key phrases, and entity types (such as product names). Which THREE operations should you call?

Accepted Answer

Key Phrase Extraction. Options A, B, and C are correct because Sentiment Analysis, Key Phrase Extraction, and Entity Recognition are the operations that provide the required insights. Option D is wrong because Language Detection detects the language, not needed here. Option E is wrong because PII Detection detects personal information, not needed here.

Answer

Language Detection

Answer

PII Detection