AI-102 Hard Questions — 25 Difficult Questions

Question 1

Which THREE components are required to build a custom copilot using Microsoft Copilot Studio that can answer questions from a SharePoint document library?

Accepted Answer

A Microsoft Copilot Studio copilot. A Microsoft Copilot Studio copilot is the core conversational interface that orchestrates the interaction. Without the copilot itself, there is no runtime environment to process user queries, manage conversation state, or invoke configured knowledge sources. It is the mandatory host for all custom copilot functionality.

Answer

A Power Automate flow to trigger the copilot

Answer

An Azure OpenAI Service deployment

Question 2

You are reviewing a skillset definition for an Azure AI Search indexer. The indexer is configured to index 1000 PDF documents. After running the indexer, you notice that only 500 documents have sentiment scores. What is the most likely cause?

Accepted Answer

The SplitSkill outputs are not correctly mapped to the SentimentSkill input; the skill runs only on the first page of each document. The SplitSkill divides each PDF into pages, and by default the SentimentSkill runs in the context of each page (e.g., /document/pages/*). If the SplitSkill outputs are not correctly mapped to the SentimentSkill input, the sentiment skill may only process the first page or fail to receive the split content, resulting in sentiment scores for only a subset of documents. Correct mapping ensures that each page's content is passed to the SentimentSkill for analysis.

Answer

The skills are defined in the wrong order; sentiment should run before split

Answer

The SentimentSkill is not supported in this region

Answer

The context of the SentimentSkill should be "/document" instead of "/document/pages/*"

Question 3

Refer to the exhibit. You receive this error when calling an Azure Cognitive Services API. What is the most likely cause?

Accepted Answer

The subscription key is not valid for this Cognitive Services resource.. The error indicates that the subscription key provided is not associated with the Cognitive Services resource being accessed. In Azure Cognitive Services, each resource has a unique set of keys, and using a key from a different resource (even in the same region) will result in a 401 Unauthorized error with this specific message. Option D is correct because the key must match the resource's endpoint exactly.

Answer

The API endpoint is incorrect.

Answer

The subscription key has expired.

Answer

The subscription key is from a different region.

Question 4

Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?

Accepted Answer

The 'Add your data' feature configured in Azure OpenAI Studio.. The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.

Answer

A fine-tuned custom model.

Answer

An Azure OpenAI embeddings model deployment.

Question 5

A healthcare organization uses Azure AI Health Insights to analyze patient data. They must ensure that PHI is not logged by the AI services. Which configuration should you apply?

Accepted Answer

Enable diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule. Azure AI Health Insights processes protected health information (PHI), and enabling diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule allows you to redact sensitive PHI from logs before they are stored. Data masking rules in Log Analytics can be configured to hide patterns like patient IDs or names, ensuring compliance with HIPAA while still retaining operational logs.

Answer

Disable all logging for the Azure AI resource

Answer

Configure the resource to use a system-assigned managed identity

Answer

Use Azure Policy to restrict logging to metadata only

Question 6

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle high throughput and process documents in batch. Which TWO configuration options should you use?

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. The standard (S0) tier is required for production-grade throughput because it offers higher transactions per second (TPS) and better scalability compared to the free (F0) tier. For batch processing of invoices, the async document analysis API is the correct choice because it supports submitting multiple documents in a single call and returns results via a polling mechanism, which is designed for high-volume scenarios.

Answer

Use the synchronous API for each invoice

Answer

Train a custom model for invoice extraction

Answer

Store output directly in Azure Blob Storage without API calls

Question 7

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

Answer

Upgrade to a higher pricing tier

Answer

Increase the number of partitions

Answer

Reduce the number of searchable fields

Question 8

Refer to the exhibit. You are deploying an AI project in Microsoft Foundry using an ARM template. The deployment fails with an error indicating that the hub resource is not in the same region. What is the most likely cause?

Accepted Answer

The hub and project are in different Azure regions. In Azure AI Foundry, the hub and project resources must reside in the same Azure region because the project is a child resource of the hub and relies on the hub's regional endpoint for metadata storage and compute orchestration. The ARM template deployment fails with a region mismatch error when the location property of the project specifies a different region than the hub's location, as Azure enforces regional affinity for child resources to ensure low-latency communication and data residency compliance.

Answer

The resource group for the hub is different from the project's resource group

Answer

The identity type is SystemAssigned, but the hub requires UserAssigned

Answer

The location property is misspelled in the template

Question 9

A healthcare company is using Azure AI Document Intelligence to extract patient data from forms. They need to ensure that all extracted data is encrypted at rest using a customer-managed key (CMK) and that the service endpoint is restricted to a specific virtual network. Which combination of steps should they take?

Accepted Answer

Create a private endpoint and associate a customer-managed key in the resource encryption settings. It combines a private endpoint (which restricts the service endpoint to a specific virtual network by providing a private IP address within that VNet, eliminating public internet exposure) with a customer-managed key (CMK) in the resource encryption settings, which ensures data at rest is encrypted using a key stored in Azure Key Vault that the customer controls. This directly meets both requirements: network isolation via private endpoint and CMK-based encryption at rest.

Answer

Use a service endpoint and configure a managed identity

Answer

Disable public network access and enable CMK via Azure Key Vault

Answer

Configure IP firewall rules and enable CMK via Azure Key Vault

Question 10

Your company is deploying an Azure AI solution that uses multiple AI services. You need to ensure that all API calls are authenticated securely using managed identities. Which of the following steps is required to enable managed identity authentication for an Azure AI service?

Accepted Answer

Assign a managed identity to the Azure AI service and grant it the required RBAC role. Managed identity authentication for Azure AI services requires assigning either a system-assigned or user-assigned managed identity to the resource, and then granting that identity the appropriate RBAC role (e.g., Cognitive Services User) on the target AI service. This eliminates the need for keys or secrets in the code and leverages Azure AD tokens for secure, passwordless authentication.

Answer

Enable system-assigned managed identity at the subscription level

Answer

Store the authentication key in Azure Key Vault and reference it in the application

Answer

Configure a shared access key in the Azure AI service

Question 11

Your Azure AI Language custom entity recognition model incorrectly extracts 'Microsoft' as an organization when it refers to the company, but fails to extract 'Microsoft' as a product when it refers to the software. How should you improve the model?

Accepted Answer

Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context. Custom entity recognition models in Azure AI Language learn to distinguish entity types based on context. By labeling 'Microsoft' as 'Organization' in sentences where it refers to the company and as 'Product' in sentences where it refers to the software, you provide the model with the contextual clues needed to disambiguate the same token across different uses. This supervised learning approach directly addresses the model's failure to recognize the product entity.

Answer

Reduce the amount of training data to avoid confusion

Answer

Remove the 'Organization' entity type from the model

Answer

Add more training sentences without labeling the entity type

Question 12

You are designing a conversational AI solution using Microsoft Copilot Studio. The solution must: - Allow users to ask questions about company policies stored in SharePoint Online. - Use generative answers with grounding data. - Ensure that responses are based only on approved documents. - Support authentication via Microsoft Entra ID. Which THREE components must be configured as part of the solution?

Accepted Answer

Create a new data source in Copilot Studio pointing to SharePoint Online.. Copilot Studio allows you to create a new data source that connects directly to SharePoint Online, enabling the generative answers feature to retrieve and ground responses on approved policy documents stored there. This ensures that only content from the specified SharePoint site is used, meeting the requirement for grounding data from approved documents.

Answer

Create a Power Automate flow to retrieve documents from SharePoint.

Answer

Train an AI Builder model to classify policy documents.

Question 13

You are a solution architect at a financial services company. You need to implement a knowledge mining solution that extracts information from annual reports (PDF) of publicly traded companies. The reports contain financial tables, executive summaries, and legal disclaimers. The solution must: (1) extract the company name, fiscal year, revenue, net income, and CEO name; (2) redact any personally identifiable information (PII) like email addresses and phone numbers before indexing; (3) index the extracted data in Azure AI Search; (4) allow users to query using natural language (e.g., 'Which company had the highest revenue in 2023?'). The reports are uploaded to an Azure Blob Storage container. You have access to Azure AI Services and Azure OpenAI. Which combination of services and configurations should you use?

Accepted Answer

Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.. Azure AI Document Intelligence can be trained with a custom extraction model to accurately extract specific financial fields like company name, fiscal year, revenue, net income, and CEO name from annual report PDFs. The PII detection skill in the Azure AI Search enrichment pipeline redacts sensitive information such as email addresses and phone numbers before indexing. Enabling semantic search allows users to query using natural language. Option B relies on Azure AI Vision OCR and Azure AI Language entity extraction, which are less precise for structured table extraction and cannot guarantee the specific fields needed. Option C uses generic skills like Document Layout and Entity Recognition, which are not tailored for financial data extraction and may miss critical fields. Option D uses GPT-4, which can be inconsistent for structured data extraction from tables and does not include built-in PII redaction within the search pipeline.

Answer

Use Azure AI Vision OCR to extract text from PDFs, then use Azure AI Language to extract entities and key phrases. Index in Azure AI Search with semantic search.

Answer

Use Azure AI Search with blob indexer, include a skillset with Document Layout skill, Entity Recognition skill (for financial entities), and Key Phrase Extraction. Enable semantic search.

Answer

Use Azure OpenAI GPT-4 to process each report via a custom extraction prompt, then send extracted JSON to Azure AI Search. Enable semantic search.

Question 14

Your Azure AI Search index contains millions of documents. Users report that search results are slow for complex queries. You need to improve query performance without reducing result quality. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas in Azure AI Search distributes query load across multiple copies of the index, enabling parallel processing of complex queries. This directly improves query throughput and latency without altering the index schema or reducing result quality, as replicas provide dedicated resources for query execution.

Answer

Reduce the maximum number of results returned per query

Answer

Remove all facet fields from the index

Answer

Disable complex query types such as fuzzy and regex

Question 15

Refer to the exhibit. You are troubleshooting an Azure OpenAI API call that is returning incomplete responses. The response stops mid-sentence. Which parameter should you adjust?

Accepted Answer

Increase max_tokens to 1000.. The `max_tokens` parameter controls the maximum number of tokens the model can generate in a single response. When a response stops mid-sentence, it typically means the token limit was reached before the model could complete its output. Increasing `max_tokens` to 1000 provides more room for the model to finish its generation, resolving the truncation issue.

Answer

Remove the stop parameter.

Answer

Increase temperature to 1.0.

Answer

Increase top_p to 1.0.

Question 16

Which THREE considerations are important when planning to use Azure OpenAI Service in a production environment?

Accepted Answer

Configure content filtering to block harmful outputs. Azure OpenAI Service includes built-in content filtering to detect and block harmful outputs such as hate, violence, or self-harm. This is a critical safety and compliance requirement for production deployments, as it helps meet responsible AI principles and regulatory obligations. Without configuring content filters, the service could generate inappropriate responses that violate usage policies or legal standards.

Answer

Train a custom model from scratch using your own data

Answer

Deploy the service within a virtual network (VNet)

Question 17

You are using Azure AI Foundry to fine-tune a GPT-3.5 model on a dataset of customer service conversations. The fine-tuning job fails with an error indicating that the training data format is invalid. What is the most likely issue?

Accepted Answer

The training data is not in JSONL format with the correct structure.. Azure AI Foundry requires fine-tuning data to be in JSONL format with a specific structure: each line must be a JSON object containing a 'messages' array with 'role' and 'content' fields for system, user, and assistant turns. The error indicates the training data format is invalid, and the most likely cause is that the data is not in this required JSONL structure, as JSONL is the only accepted format for GPT-3.5 fine-tuning in Azure OpenAI Service.

Answer

The training data is in CSV format instead of JSON.

Answer

The training data contains only one conversation example.

Answer

The training data does not include the assistant's responses.

Question 18

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

Accepted Answer

Enable active learning in the project settings and periodically publish the updated knowledge base.. Enabling active learning in Azure AI Language's custom question answering allows the system to learn from user interactions. It suggests alternative phrasings for existing QnA pairs, which helps answer paraphrased queries without manual effort. Option A is wrong because Direct Line Speech channel is for voice interactions, not improving answer coverage. Option C is wrong because simply adding more documents does not address the paraphrasing issue; it may increase redundancy but not handle different wording of the same question. Option D is wrong because manually adding alternate phrases is time-consuming and not scalable; active learning automates this process by identifying and suggesting variations based on user queries.

Answer

Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.

Answer

Increase the number of FAQ documents in the knowledge base.

Answer

Manually add alternate question phrases to the knowledge base for each QnA pair.

Question 19

You are building a generative AI solution using Azure AI Foundry. The solution must meet compliance requirements that require all model inputs and outputs to be auditable for a minimum of one year. What should you enable?

Accepted Answer

Diagnostic settings to capture request and response logs and store them in a storage account.. Enabling diagnostic settings for the Azure AI Foundry resource allows you to capture detailed request and response logs for all model interactions. By routing these logs to a storage account, you retain the data for the required one-year audit period, meeting compliance needs for full traceability of inputs and outputs.

Answer

Azure Monitor alerts for unusual activity.

Answer

Azure Monitor metrics for the Azure AI Foundry resource.

Answer

Azure Monitor workbooks to visualize usage.

Question 20

You are reviewing the ARM template snippet for an Azure AI Foundry hub. After deployment, you notice that the hub cannot connect to the storage account. What is the most likely cause?

Accepted Answer

The hub uses a managed network that blocks outbound traffic to the storage account. The most likely cause is that the hub uses a managed network which, by default, blocks outbound traffic to the storage account unless explicitly allowed via outbound rules or private endpoints. Azure AI Foundry hubs enforce network isolation for security, and if the storage account is not configured as an approved destination, the hub cannot establish the required connections for data access.

Answer

The location 'eastus' does not support AI Foundry hubs

Answer

The storage account name contains invalid characters

Answer

The key vault is not configured with a firewall rule