AI-102 Practice Test 19 — 15 Questions

Question 1

Refer to the exhibit. You send this request to the Conversational Language Understanding API. The response includes the intent 'BookFlight' with entities 'FromCity: Seattle' and 'ToCity: Boston', but the 'Date' entity is missing. What is the most likely cause?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The model was not trained to recognize date entities. The Conversational Language Understanding (CLU) API returns only intents and entities that the deployed model was explicitly trained to recognize. If the training data did not include labeled 'Date' entities, the model will not extract them regardless of the input text. The API itself supports entity extraction, and the endpoint and string index type settings do not affect whether a specific entity type is recognized.

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The stringIndexType should be 'Utf16CodeUnit'

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The API version does not support entity extraction

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The endpoint is pointing to the wrong deployment

Question 2

Refer to the exhibit. A developer received this response from an Azure OpenAI chat completion call. The prompt was "What is the capital of France?". The finish_reason is "stop". What does this indicate?

Accepted Answer

The model completed the response naturally.. The finish_reason 'stop' indicates that the model completed the response naturally, meaning it generated a complete answer to the prompt and reached a logical stopping point (e.g., the end of a sentence or the end of the generated text). This is the standard behavior for a successful completion where the model did not encounter any content filter, token limit, or other interruption.

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The response was truncated due to content filtering.

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The model stopped generating before the response was complete.

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The response reached the max_tokens limit.

Question 3

You are reviewing an ARM template for deploying Azure OpenAI Service. The template includes a deployment for gpt-35-turbo with a capacity of 100. You need to ensure that the deployment uses provisioned throughput instead of standard. What should you modify?

Accepted Answer

Change the sku name to 'ProvisionedManaged'.. To use provisioned throughput (PTU) with Azure OpenAI Service, you must set the SKU name to 'ProvisionedManaged' in the ARM template. The default SKU is 'Standard', which uses pay-per-token consumption. Changing the SKU name to 'ProvisionedManaged' tells the resource provider to allocate dedicated throughput capacity for the deployment, ensuring consistent latency and throughput regardless of other workloads.

Answer

Remove the raiPolicyName property.

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Increase the capacity to 200.

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Change the model format to 'GPT-4'.

Question 4

Your organization is using Azure OpenAI Service to generate content. You need to ensure that the content meets safety guidelines by filtering harmful outputs. What should you configure?

Accepted Answer

Configure the content filters in the Azure OpenAI Studio.. Content filters in Azure OpenAI Studio allow you to define severity levels (safe, low, medium, high) for categories like hate, sexual, violence, and self-harm, which are enforced at the inference API level to block or flag harmful outputs before they reach the user. This is the primary configuration for filtering model-generated content in Azure OpenAI Service.

Answer

Enable the Responsible AI dashboard.

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Use Azure AI Content Safety APIs to analyze outputs.

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Set the system message to instruct the model to avoid harmful content.

Question 5

You are developing a solution to detect defects on a manufacturing assembly line using computer vision. The solution must classify images as 'defective' or 'non-defective'. You have a limited set of labeled images (500 per class). Which approach should you recommend?

Accepted Answer

Use Azure AI Custom Vision with image classification. Azure AI Custom Vision with image classification is the best choice because it allows you to fine-tune a pre-trained deep learning model on your limited dataset (500 images per class) to classify images as 'defective' or 'non-defective'. This approach requires minimal data and expertise compared to training from scratch, and it is specifically designed for custom classification tasks with small datasets.

Answer

Use Azure AI Vision Image Analysis with a pre-built model

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Use Azure AI Custom Vision with object detection

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Train a deep learning model from scratch using Azure Machine Learning

Question 6

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 7

You need to restrict access to an Azure AI Language resource so that only a specific virtual network can call the endpoint. Which configuration should you use?

Accepted Answer

Enable a service endpoint or private endpoint for the resource. Azure AI Language resources can be isolated to a specific virtual network by enabling either a service endpoint (via the Microsoft.CognitiveServices service tag) or a private endpoint (using Azure Private Link). This configuration ensures that only traffic originating from the designated VNet can reach the resource's endpoint, effectively blocking all public internet access. Service endpoints provide a direct, optimized route from the VNet to the resource, while private endpoints assign a private IP from the VNet to the resource, making it accessible only within the VNet.

Answer

Rotate the shared access keys

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Assign a managed identity to the resource

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Configure an IP firewall rule with the VNet's public IP range

Question 8

You are designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?

Accepted Answer

Custom Entity Extraction. Custom Entity Extraction (D) is correct because it allows you to train a model with a small set of labeled examples to extract domain-specific entities like product names, order numbers, and dates from customer emails. This service supports iterative learning and improvement over time, making it ideal for scenarios where pre-built models lack the required specificity.

Answer

QnA Maker

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Pre-built Entity Extraction

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Text Analytics for health

Question 9

Your organization is building a knowledge base from technical manuals stored in multiple formats (PDF, Word, HTML). You need to extract text and images from these documents and create a searchable index. The solution must handle tables and preserve their structure. Which approach should you use?

Accepted Answer

Use Azure AI Document Intelligence layout model as a custom skill. The Azure AI Document Intelligence layout model is purpose-built to extract text, tables, and structure from documents in PDF, Word, and HTML formats, and it preserves table structure and reading order. When integrated as a custom skill in an Azure AI Search skillset, it enriches the pipeline with structured content that can be indexed and searched. This directly satisfies the requirement to handle tables while preserving their structure.

Answer

Upload documents directly to Azure AI Search

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Use Azure AI Language custom entity extraction

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Use Azure AI Vision OCR skill in the skillset

Question 10

You need the project to support English, Spanish, and French. What change should you make to the command?

Accepted Answer

Change --multilingual false to --multilingual true.. The command requires multilingual support for English, Spanish, and French. By default, the `--multilingual` flag is set to `false`, which restricts the project to a single language. Changing it to `true` enables the project to accept utterances in multiple languages, allowing the Conversational Language Understanding (CLU) model to process and train on intents and entities across all specified languages.

Answer

Change --language to "multi".

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Add --description "Multi-language support".

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Change --project-name to "SupportBotML".

Question 11

You are designing an agent that uses Azure AI Search as a knowledge store. The agent must handle multiple languages. Which feature should you configure in Azure AI Search to ensure the agent retrieves relevant results for queries in different languages?

Accepted Answer

Language analyzers. Language analyzers in Azure AI Search are specifically designed to handle linguistic variations across different languages, such as stemming, stop word removal, and tokenization rules. By configuring the appropriate language analyzer (e.g., 'en.microsoft' for English or 'fr.microsoft' for French) on a searchable field, the agent can retrieve relevant results for queries in multiple languages because the analyzer processes both the indexed content and the query string using the same language-specific rules.

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Scoring profiles

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Semantic search

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Synonym maps

Question 12

Which Azure AI service can be used to analyze sentiment in text data?

Accepted Answer

Azure AI Language Service. Azure AI Language Service (formerly Text Analytics) includes a built-in sentiment analysis feature that evaluates text and returns sentiment labels (positive, negative, neutral, mixed) along with confidence scores at the sentence and document level. This makes it the primary service for analyzing sentiment in text data. Azure AI Content Safety is not designed for sentiment analysis; it is intended for harmful content moderation.

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Azure AI Translator

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Azure AI Vision

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Azure AI Content Safety

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Azure AI Speech

Question 13

A multinational corporation uses Azure AI Language to analyze customer feedback in multiple languages. The solution must detect the language of incoming text and then perform sentiment analysis. Which approach minimizes latency and cost?

Accepted Answer

Call the language detection API followed by the sentiment analysis API. The requirement is to detect the language of incoming text and then perform sentiment analysis. The sentiment analysis API in Azure AI Language does not detect language; it requires the language to be specified. Therefore, you must first call the language detection API to identify the language, then pass that language code to the sentiment analysis API. While this involves two API calls, it is still more efficient than translating text (Option B) which adds significant latency and cost, or using cognitive skills with Azure AI Search (Option C) which adds indexing overhead. Option A is wrong because the sentiment analysis API cannot detect language on its own; it requires the language as an input parameter. Thus, Option D minimizes latency and cost by only performing the necessary steps without unnecessary transformations.

Answer

Use the sentiment analysis API with multilingual support

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Use the Translator service to translate text to English, then call sentiment analysis

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Store text in Azure AI Search and use cognitive skills for sentiment

Question 14

You are designing an Azure AI solution that uses Azure AI Language to analyze customer support transcripts. The solution must identify key phrases, detect sentiment, and extract custom entities specific to your product catalog. Which two Azure AI Language features should you enable?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction (Option B) is correct because it identifies the main points and important terms in customer support transcripts, such as 'refund request' or 'account issue,' which directly supports analyzing the content. Custom Entity Extraction (Option D) is correct because it allows you to define and extract domain-specific entities from your product catalog, such as product names or model numbers, using a trained custom entity extraction model. Together, these two features enable both general insight extraction and tailored, product-specific data extraction from the transcripts.

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PII Detection

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Summarization

Question 15

You are developing a bot using Microsoft Bot Framework and Azure AI Language. The bot must handle user intents that change mid-conversation. Which feature should you implement?

Accepted Answer

Adaptive dialogs. Adaptive dialogs are designed for dynamic, event-driven conversations where user intents can change mid-conversation. They use a trigger-based model (e.g., onIntent, onTurn) that allows the bot to react to new intents at any point, unlike linear dialog models. This makes them ideal for handling mid-conversation intent shifts without requiring predefined dialog flows.

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Prompt dialogs

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Waterfall dialogs

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