AI-102 Practice Test 30 — 15 Questions

Question 1

Drag and drop the steps to troubleshoot a failed Azure AI Search indexer execution into the correct order.

Accepted Answer

Step 1: Check indexer execution history, Step 2: Review error messages, Step 3: Verify data source connectivity, Step 4: Check skillset execution, Step 5: Re-run the indexer. First check history, review errors, verify data source, check skillset, then re-run.

Answer

Step 1: Verify data source connectivity, Step 2: Check indexer execution history, Step 3: Review error messages, Step 4: Check skillset execution, Step 5: Re-run the indexer

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Step 1: Review error messages, Step 2: Check indexer execution history, Step 3: Check skillset execution, Step 4: Verify data source connectivity, Step 5: Re-run the indexer

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Step 1: Check skillset execution, Step 2: Verify data source connectivity, Step 3: Check indexer execution history, Step 4: Review error messages, Step 5: Re-run the indexer

Question 2

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.

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

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

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

Question 3

You have deployed a generative AI model using Azure Machine Learning. The model is used for generating financial reports. You need to monitor the model's performance and detect data drift in the input data. What should you use?

Accepted Answer

Azure Machine Learning data drift monitoring. Azure Machine Learning data drift monitoring is the correct choice because it is specifically designed to detect statistical changes in input data over time, which is critical for generative AI models used in financial reporting where data distributions can shift due to market changes or new regulations. It compares the current input data distribution against a baseline dataset using metrics like Wasserstein distance or Population Stability Index, and triggers alerts when drift exceeds a threshold. This ensures the model's outputs remain reliable and compliant, which is a core requirement for generative AI solutions in regulated industries.

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Azure Monitor

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Application Insights

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Azure AI Language

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.

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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

Your company uses Azure AI Content Safety to moderate user-generated content in a chat application. You need to detect and block sexual content in multiple languages. Which pre-built category should you configure?

Accepted Answer

Sexual. Azure AI Content Safety provides pre-built severity-based categories for content moderation. The 'Sexual' category is specifically designed to detect and block explicit sexual content, including text and images, across multiple languages. This makes it the correct choice for your requirement to moderate sexual content in a chat application.

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Self-harm

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Hate

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Violence

Question 6

You are building an agentic solution using Azure AI Agent Service. The agent needs to send an email via Microsoft Graph API. Which authentication method should you use for the action?

Accepted Answer

OAuth 2.0. Microsoft Graph API requires OAuth 2.0 for authentication because it uses delegated or application permissions to access user data securely. Azure AI Agent Service can use OAuth 2.0 with a managed identity or service principal to obtain an access token for the Graph API, ensuring proper authorization and compliance with Microsoft's security model.

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Client Certificate

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API Key

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Basic Authentication

Question 7

You executed the Azure CLI command shown to create an indexer. However, the indexer fails to run. The error indicates that the data source connection string is invalid. You have verified that the connection string is correct. What is the most likely issue?

Accepted Answer

The data source's connection string is not properly stored or the data source is not configured with the correct credentials. The error message explicitly states that the data source connection string is invalid, even though you have verified the string itself is correct. This typically occurs when the connection string is not stored correctly in the search service (e.g., it was truncated, escaped improperly, or contains special characters that were misinterpreted) or when the data source configuration lacks the correct credentials (e.g., the managed identity is not enabled, or the key/secret is missing). Option D directly addresses this mismatch between a verified string and the service's stored configuration.

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The --query parameter is incorrectly formatted

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The skillset name does not exist in the search service

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The parsingMode should be 'json' for PDF files

Question 8

You are using Azure AI Search to index a set of PDF documents. The index includes a 'content' field with the extracted text. Users report that when they search for 'budget forecast', documents containing only 'budget' or 'forecast' are ranked lower than expected. Which configuration change would improve the ranking for multi-word queries?

Accepted Answer

Enable semantic search on the index. Semantic search in Azure AI Search uses deep neural network models to understand the intent and context of multi-word queries like 'budget forecast', rather than relying solely on keyword matching. This improves ranking by capturing the semantic relationship between terms, so documents containing both words (or conceptually related content) are ranked higher even if the exact phrase is not present.

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Add a separate field for each word in the document

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Change the analyzer to a custom analyzer that splits on spaces only

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Set the 'content' field to a higher boosting value

Question 9

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 10

You are developing a chatbot using Azure AI Bot Service that uses Azure OpenAI Service for natural language understanding. The chatbot must be deployed in multiple regions for low latency. However, the customer requires that all customer data remain within the European Union. Which architecture should you recommend?

Accepted Answer

Deploy the bot and all supporting services in multiple EU regions, e.g., West Europe and North Europe, without cross-region data replication.. Deploying the bot and all supporting services in multiple EU regions (e.g., West Europe and North Europe) ensures low latency for users across Europe while keeping all customer data within the European Union. Azure OpenAI Service and Azure AI Bot Service can be deployed regionally without cross-region data replication, satisfying the data residency requirement. This architecture avoids any data leaving EU boundaries while providing geographic redundancy for performance.

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Deploy the bot in a single EU region and rely on CDN for static content.

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Use Azure Front Door to route traffic to a single bot hosted in West Europe.

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Deploy the bot in multiple global regions and use Cosmos DB with multi-region writes.

Question 11

Your Azure AI solution uses multiple AI services including Computer Vision and Language. To reduce costs, you want to share a single key and endpoint across services. Which Azure resource type should you deploy?

Accepted Answer

Azure AI services multi-service resource. D is correct because Azure AI services multi-service resource provides a single endpoint and key that can be used across multiple AI services (e.g., Computer Vision, Language, Face, etc.), reducing management overhead and cost by consolidating billing. This resource type is designed specifically for scenarios where you want to share credentials across services without deploying separate single-service instances.

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Separate single-service resources for each AI service

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Azure Key Vault for storing keys

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Azure API Management gateway

Question 12

Which TWO statements about Azure OpenAI Service content filters are true?

Accepted Answer

They can be configured with severity levels (low, medium, high). Azure OpenAI Service content filters can be configured with severity levels (low, medium, high) to control the strictness of filtering for each content category. This allows administrators to fine-tune the filter sensitivity based on their application's risk tolerance and compliance requirements.

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They only filter the output of the model

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They cannot be customized for specific use cases

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They are bypassed when using PTU deployments

Question 13

You need to create a solution that extracts key-value pairs from scanned invoices using Azure AI Document Intelligence. The invoices have varying layouts. Which model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model (Option D) is specifically designed to extract key-value pairs, line items, and other structured fields from invoices, even when layouts vary. It is trained on thousands of invoice samples and uses deep learning to handle diverse formats without requiring custom training, making it the correct choice for this scenario.

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Layout model

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Custom extraction model

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Read model

Question 14

A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?

Accepted Answer

Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part. The requirement is to extract key phrases and identify sentiment per speaker turn. The Azure AI Language key phrase extraction and sentiment analysis APIs operate on individual text inputs. By splitting the transcript by speaker turns, each segment can be analyzed independently, providing per-speaker insights. Processing the entire transcript as a single document would aggregate sentiment and key phrases, losing per-speaker granularity.

Answer

Call the prebuilt sentiment analysis API on the entire transcript

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Use QnA Maker to extract Q&A pairs per speaker

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Use Text Analytics for health to extract entities and sentiment

Question 15

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

Accepted Answer

Azure AI Services deployed in a private endpoint with no data leaving the network.. Deploying Azure AI Services within a private endpoint, combined with ensuring no data leaves the network, aligns with HIPAA's requirement for data privacy and compliance. This configuration uses Azure Private Link to keep all traffic within the Microsoft backbone network, preventing exposure to the public internet and meeting the strict data residency and encryption standards mandated by HIPAA.

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Microsoft Bot Framework SDK with a custom connector.

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Azure Cognitive Search with custom analyzers.

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Azure OpenAI Service with data processing enabled.