AI-102 Practice Test 19 — 15 Questions

Question 1

Refer to the exhibit. You are defining a custom entity recognition model in Azure AI Language. The exhibit shows a partial configuration. What is the relationship between 'Laptop' and 'Electronics'?

Accepted Answer

Laptop is a type of Electronics.. In Azure AI Language custom entity recognition, you define entity types and subtypes using a hierarchical structure. The exhibit shows 'Laptop' as a child of 'Electronics', meaning Laptop is a subtype or specific type of the broader Electronics category. This allows the model to recognize that any Laptop entity is also an instance of Electronics, enabling more granular classification and downstream processing.

Answer

There is no defined relationship.

Answer

Electronics is a type of Laptop.

Answer

Laptop is a part of Electronics.

Question 2

Your company uses Azure AI Document Intelligence to process invoices. You need to extract the invoice date and total amount. Which model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model is specifically trained on thousands of invoice documents to extract key fields like invoice date, total amount, vendor details, and line items. It uses deep learning models optimized for invoice layouts, providing higher accuracy and structured output for these fields compared to general models.

Answer

Read model

Answer

Layout model

Answer

General document model

Question 3

You need to transcribe customer service calls into text for analysis. Which Azure service should you use?

Accepted Answer

Azure AI Speech-to-Text. Azure AI Speech-to-Text (B) is the correct service for transcribing audio recordings of customer service calls into text. It provides real-time or batch transcription of spoken language, which is exactly what the scenario requires for subsequent analysis. The other services handle different tasks: understanding intent, translating text, or generating speech.

Answer

Conversational Language Understanding

Answer

Azure AI Translator

Answer

Azure AI Text-to-Speech

Question 4

You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?

Accepted Answer

Create a custom NER project in Azure AI Language and train it with your annotated data.. Azure AI Language's custom NER (Named Entity Recognition) feature allows you to train a model using your own annotated dataset. With 50 documents, you have enough labeled data to fine-tune a custom entity extraction model that learns the specific entity types and patterns in your domain, enabling accurate extraction from new documents.

Answer

Use the prebuilt entity recognition API to extract entities.

Answer

Use the Conversational PII entity extraction feature.

Answer

Use the built-in entity extraction skill in Azure AI Search.

Question 5

Which TWO components are required to create a custom text classification model in Azure AI Language?

Accepted Answer

A set of labeled documents. A set of labeled documents is required because custom text classification in Azure AI Language uses supervised learning, where each document must be tagged with the correct class to train the model. Without labeled data, the model cannot learn the patterns that distinguish one category from another.

Answer

A QnA Maker knowledge base

Answer

A Language Understanding (LUIS) app

Answer

An Azure Functions app

Question 6

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 allows you to provide a custom dictionary where you can map a source term to a target term. By setting the translation to the same word (e.g., 'API' → 'API'), the service will leave that term untranslated while still translating the rest of the document. This is the native, supported mechanism for preserving specific terms without post-processing or custom model training.

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 7

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. In Azure AI Language, retraining a custom text classification model does not automatically update the deployed endpoint. After training, you must explicitly deploy the new model to the endpoint using the 'Deploy model' action. Until that step is completed, the endpoint continues to serve predictions from the previously deployed model.

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 8

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 `multilingual` parameter, when set to `true` in the request body of the Azure AI Language Service REST API for a Conversational Language Understanding (CLU) project, enables the project to support multiple languages, such as English and Spanish, within a single project. This allows the model to learn from utterances in different languages and generalize across them, rather than requiring separate projects per language.

Answer

projectName

Answer

language

Answer

confidenceThreshold

Question 9

You need to build a solution that can answer questions based on a set of PDF documents, such as product manuals. The solution should allow users to ask questions in natural language and receive answers with citations. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Question Answering. Azure AI Custom Question Answering (part of Azure AI Language) is specifically designed to extract question-answer pairs from semi-structured content like PDFs and provide answers with citations. It uses a deep learning-based extractive QA model that can locate answer spans within documents and return the source text as a citation, directly meeting the requirement for natural language questions and cited answers from product manuals.

Answer

Azure AI Text Analytics

Answer

Azure Cognitive Search

Answer

Azure AI Document Intelligence (formerly Form Recognizer)

Question 10

Your NLP solution uses custom text classification in Azure AI Language. You need to improve the model's accuracy. Which action should you take?

Accepted Answer

Add more labeled training data with balanced classes.. Custom text classification in Azure AI Language relies on supervised learning, where model accuracy is directly proportional to the quantity and quality of labeled training data. Adding more labeled examples with balanced class distributions reduces bias, improves generalization, and helps the model learn distinguishing features more effectively, which is the most impactful action for improving accuracy.

Answer

Increase the number of training epochs.

Answer

Decrease the batch size.

Answer

Use a different pretrained model.

Question 11

You are deploying an Azure AI solution that uses Azure OpenAI Service. The solution must be deployed in a way that minimizes latency for users in Asia. However, the company's data residency policy requires data to stay in the United States. What should you do?

Accepted Answer

Deploy the service in a US region and use Azure Front Door with caching to reduce latency.. It satisfies both requirements: data residency (deploying in a US region keeps data within the United States) and latency reduction for Asian users. Azure Front Door with caching stores frequently accessed model responses at edge locations closer to users in Asia, minimizing round-trip time without moving the origin data.

Answer

Use Azure CDN to cache the model responses in Asia.

Answer

Deploy the Azure OpenAI Service in an Asian region and use Azure Front Door to route traffic.

Answer

Deploy the service in multiple regions globally and use Traffic Manager for routing.

Question 12

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 custom entity recognition model in Azure AI Language Service requires sufficient labeled examples for each entity type to learn patterns effectively. Rare diseases with few training examples will have lower recall because the model lacks the statistical evidence to generalize those entities. This is a classic data sparsity issue in supervised NLP models.

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 13

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 14

You are building a chatbot that must understand user intents from free-text input. You have a small set of labeled examples. Which Azure AI Language feature should you use to classify intents with minimal effort?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to extract intents and entities from free-text input in a conversational context, and it can be trained with a small set of labeled examples to classify user intents with minimal effort. CLU provides a pre-built pipeline for intent recognition and entity extraction, making it the most efficient option for building a chatbot that understands user intents.

Answer

Entity Linking

Answer

Custom Text Classification

Answer

Language Detection

Question 15

You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language's PII detection feature and a custom Azure Function to redact.. Azure AI Language's PII detection feature is specifically designed to identify and categorize PII entities in text, and combining it with a custom Azure Function allows you to programmatically redact those entities before storing the results in Blob Storage. This provides a serverless, scalable pipeline that meets the requirement of extracting and redacting PII from medical records without manual intervention.

Answer

Use Azure AI Search with cognitive skills for PII detection.

Answer

Use Azure OpenAI to detect and redact PII.

Answer

Use Text Analytics for Health and then manually redact.