AI-102 Implement natural language processing solutions Practice Test 8 — 15 Questions

Question 1

A PowerShell script (exhibit) attempts to update the endpoint for an Azure AI Language resource. What is the outcome of running this script?

Accepted Answer

The script fails because the property does not exist. The PowerShell script attempts to set the 'endpoint' property on an existing Azure AI Language resource using the `Set-AzCognitiveServicesAccount` cmdlet. However, the 'endpoint' property is read-only and cannot be modified after resource creation; it is generated automatically by Azure based on the resource's name and region. Attempting to update it results in a failure because the property does not exist as a writable attribute in the resource's schema.

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The script creates a new resource

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The script changes the API version

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The script updates the endpoint successfully

Question 2

You are designing a conversational AI solution using Microsoft Copilot Studio. The exhibit shows part of a topic configuration. What is the purpose of the 'triggers' section?

Accepted Answer

To define the conditions that activate the topic. In Microsoft Copilot Studio, the 'triggers' section defines the conditions (such as specific phrases, intents, or keywords) that activate a topic. This is the entry point for the conversation flow, ensuring the bot responds appropriately when a user's input matches the defined trigger phrases. Option C correctly identifies this purpose.

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To define the response the bot sends

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To define the authentication method

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To define the entities to extract

Question 3

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.

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Read model

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Layout model

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General document model

Question 4

You are building a custom named entity recognition (NER) model using Azure AI Language. After labeling 200 documents, you train the model and achieve 85% precision but only 60% recall. Which action is most likely to improve recall?

Accepted Answer

Increase the number of labeled documents, especially those containing the target entities. Low recall in a custom NER model typically indicates that the model is failing to identify many instances of the target entities. Increasing the number of labeled documents, especially those containing the target entities, provides more positive examples for the model to learn from, directly improving its ability to recognize those entities and thus boosting recall.

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Lower the confidence threshold

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Increase the training hours

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Switch to a different Azure AI Language feature

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.

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A QnA Maker knowledge base

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A Language Understanding (LUIS) app

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An Azure Functions app

Question 6

Your team uses Azure AI Language in a multi-region architecture. You need to ensure that the solution is resilient to regional outages. Which THREE actions should you take?

Accepted Answer

Use a global endpoint that routes to the nearest region. Azure AI Language global endpoints automatically route requests to the nearest available region, providing built-in failover without manual intervention. This ensures that if one region experiences an outage, traffic is redirected to the next closest healthy region, maintaining service continuity.

Answer

Disable regional endpoints

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Use a single region with high availability enabled

Question 7

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

Accepted Answer

Call the sentiment analysis REST API for each post. The sentiment analysis REST API in Azure AI Language is designed for real-time, per-document analysis. By calling the API for each social media post as it arrives, you achieve the lowest latency and can process posts individually without batching or streaming overhead, which is essential for real-time sentiment analysis.

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Use Azure AI Search with cognitive skills

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Use the batch processing feature in Azure AI Language

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Send posts to an Event Hub and use Stream Analytics

Question 8

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.

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Entity Linking

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Custom Text Classification

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Language Detection

Question 9

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.

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Conversational Language Understanding

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Azure AI Translator

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Azure AI Text-to-Speech

Question 10

You are deploying a custom Named Entity Recognition (NER) model using Azure AI Language. You have 500 labeled documents. After training, the model shows high precision but low recall. Which action is most likely to improve recall?

Accepted Answer

Add more labeled examples covering the missed entities. Adding more labeled examples that cover the missed entities directly addresses the root cause of low recall: the model has not seen enough representative patterns for those entities during training. In Azure AI Language custom NER, the model learns to recognize entities based on the labeled data; insufficient or imbalanced examples for certain entity types cause the model to fail to identify them, lowering recall. Enriching the training set with diverse examples of the underperforming entities gives the model more opportunities to learn their variations, thereby improving recall without sacrificing precision.

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Switch to Conversational Language Understanding

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Reduce the confidence threshold for entity extraction

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Increase the number of training epochs

Question 11

Your application needs to extract key phrases from customer reviews to identify common topics. Which Azure AI Language feature should you use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct Azure AI Language feature because it is specifically designed to identify and return a list of key phrases from unstructured text, such as customer reviews, that capture the main topics and themes. This allows you to aggregate common topics across multiple reviews without manual analysis.

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Sentiment Analysis

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Language Detection

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Named Entity Recognition

Question 12

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

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Train a custom translation model that ignores those terms

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Post-process the output to revert translations of those terms

Question 13

You need to analyze customer feedback to determine whether the sentiment is positive, negative, or neutral. Which Azure AI service should you use?

Accepted Answer

Azure AI Language - Sentiment Analysis. Azure AI Language's Sentiment Analysis is the correct service because it is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether customer feedback sentiment is positive, negative, or neutral.

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Azure AI Language - Key Phrase Extraction

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Azure AI Language - Named Entity Recognition

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Azure AI Language - Language Detection

Question 14

You are building a multilingual chatbot using Azure AI Language. For a given user utterance, you need to first detect the language, then route to the appropriate language-specific intent model. Which combination of Azure AI Language features should you use?

Accepted Answer

Language Detection and Conversational Language Understanding. The scenario requires first detecting the language of the user utterance (using Language Detection) and then routing to a language-specific intent model (using Conversational Language Understanding, which supports multiple languages in separate projects or deployments). This combination directly fulfills the requirement of language-aware intent routing.

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Key Phrase Extraction and Conversational Language Understanding

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Translator and Conversational Language Understanding

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Language Detection and Custom Text Classification

Question 15

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

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Use Entity Linking

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Use Custom Text Classification