AI-103 Practice Test 17 — 15 Questions

Question 1

You are setting up diagnostic logging for an Azure OpenAI resource. Your security team mandates that all log streams must be exported in real-time to a security information and event management (SIEM) platform outside Azure, such as Splunk. Which destination should you configure in the Azure Monitor diagnostic setting?

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

Accepted Answer

Stream to an Azure Event Hub. Azure Event Hub is the supported real-time streaming destination for Azure Monitor diagnostic settings and integrates natively with external SIEM platforms like Splunk via the Splunk add-on for Event Hub. It provides low-latency, high-throughput egress of logs outside Azure.

Answer

Send to a Microsoft Sentinel workbook

Answer

Configure an Azure Automation runbook webhook

Answer

Archive to an Azure Storage account container

Question 2

What is the primary purpose of a 'Vector Store' in a RAG-based agentic application?

Accepted Answer

To store embeddings for efficient semantic similarity search.. A vector store's primary purpose in a RAG-based agentic application is to persist embeddings and enable efficient semantic similarity search. When a user query arrives, it is embedded and compared against stored vectors using approximate nearest neighbor algorithms to retrieve the most relevant chunks. This retrieval step grounds the LLM's response in external knowledge, which is the core of RAG.

Answer

To generate text responses.

Answer

To log user conversations for analytics.

Answer

To fine-tune the LLM.

Question 3

You are evaluating the performance of a custom text classification model in Language Studio. Which THREE evaluation metrics are provided to assess model quality? (Choose three.)

Accepted Answer

F1-score. In Azure AI Language custom text classification, the evaluation page in Language Studio reports standard classification quality metrics: F1-score (B), Precision (C), and Recall (E). Precision measures the proportion of predicted labels that are correct, Recall measures the proportion of actual labels that were correctly identified, and F1-score is the harmonic mean of Precision and Recall, giving a balanced single-number summary of model quality. These three metrics are computed per class and as overall/macro averages, which is exactly what Language Studio surfaces for assessing a custom text classification model. HTTP request latency (A) and CPU utilization percentage (D) are operational/infrastructure performance measures, not model-quality evaluation metrics, so they are not provided for assessing classification accuracy.

Answer

HTTP request latency in milliseconds

Answer

CPU utilization percentage

Question 4

Which THREE prebuilt document models are natively available out-of-the-box in Azure AI Document Intelligence? Each correct selection represents a valid prebuilt model.

Accepted Answer

Prebuilt W-2 tax form model. Azure AI Document Intelligence ships several prebuilt models for common business documents, and the prebuilt W-2 tax form model (A) is one of them, designed to extract US wage and tax statement fields such as wages, tips, and federal tax withheld. The prebuilt Invoice model (B) is also natively available and extracts key invoice fields like vendor name, invoice date, due date, line items, and amounts due. The prebuilt Receipt model (E) is likewise an out-of-the-box model that extracts merchant name, transaction date, line items, and totals from sales receipts. Options C and D are not real Document Intelligence prebuilt models: Video Sentiment analyzer is not a document analysis capability, and 3D CAD Model Analyzer does not exist in the service's prebuilt model catalog.

Answer

Prebuilt Video Sentiment analyzer

Answer

Prebuilt 3D CAD Model Analyzer

Question 5

Your company's compliance mandate requires that all data processed by Azure AI Speech and Document Intelligence services remain encrypted using keys managed entirely by your organization. Which THREE configuration steps are required? Each correct answer presents part of the solution.

Accepted Answer

Assign appropriate permissions for the AI service managed identity to access the Key Vault key.. Option A is correct because when using customer-managed keys (CMK) with Azure AI services, the AI service's managed identity must be granted access to the Key Vault key via an access policy or RBAC role (e.g., Key Vault Crypto Service Encryption User) so the service can wrap/unwrap the data encryption key. Option C is correct because you must provision an Azure Key Vault to hold the CMK, and enabling soft-delete and purge protection is mandatory for CMK scenarios to prevent accidental or malicious key deletion that would render encrypted data unrecoverable. Option D is correct because the CMK must be bound to the Azure AI services account by setting the customer-managed key identifier (the Key Vault key URI) in the account's encryption properties, which is what actually switches encryption from Microsoft-managed keys to your organization's keys. Option B is wrong because storing private keys in plain text in source code is a severe security anti-pattern and is never a valid configuration step. Option E is wrong because disabling diagnostic logging has no bearing on encryption key management and would actually undermine compliance and auditing requirements.

Answer

Store the private key as plain text in the application source code.

Answer

Disable all diagnostic logging across all Azure subscriptions.

Question 6

Your development team has created a custom model using Azure Machine Learning. You need to deploy this model as a real-time web service endpoint with auto-scaling capabilities. Which deployment target should you select?

Accepted Answer

Azure Managed Online Endpoint. Azure Managed Online Endpoints are designed for real-time inference with built-in autoscaling, authentication, and monitoring, making them the correct deployment target for a custom Azure Machine Learning model exposed as a web service. They support scale-to-zero or rule-based scaling based on metrics like CPU utilization or request rate.

Answer

Azure Functions consumption plan

Answer

Azure Container Registry webhook

Answer

Azure Batch cluster

Question 7

You are configuring tool integration for an agent. Which THREE aspects must be clearly defined in the tool definition for the agent to correctly use it?

Accepted Answer

The tool's description, explaining what it does and when to call it.. Option A is correct because the tool's description tells the agent what the tool does and, critically, when it should be invoked, which guides the model's decision to call it. Option D is correct because the tool's name is the identifier the agent uses to reference and invoke the specific function in its tool-call output. Option E is correct because the JSON schema for the function arguments defines the exact parameter names, types, and required fields the agent must supply, ensuring valid, parseable arguments. Option B is not required in the tool definition; rate limiting is an operational/policy concern handled outside the tool's schema. Option C is wrong because hardcoding authentication credentials in the tool definition is insecure and unnecessary—credentials should be managed via secure configuration, not embedded in the tool spec.

Answer

The total number of requests allowed per hour.

Answer

The authentication credentials (hardcoded).

Question 8

When labeling documents for a custom extraction model, what happens if you label the same field inconsistently across documents?

Accepted Answer

Model accuracy will decrease. Inconsistent labeling of the same field across training documents introduces conflicting ground truth, which degrades the model's ability to learn a stable pattern. The neural trainer will still complete, but precision and recall for that field drop because the model receives contradictory signals. Consistency is a core labeling best practice in Document Intelligence Studio.

Answer

The training will fail immediately

Answer

No impact

Answer

The model will automatically correct the labels

Question 9

You are configuring a custom named entity recognition project in Language Studio. You have tagged your training documents and want to split your dataset into training and testing sets. What are the recommended split options available in Language Studio?

Accepted Answer

Automatic split by percentage or manual assignment of documents to sets.. Language Studio for custom NER provides two dataset-splitting methods: automatic split by a percentage (e.g., 80/20) that randomly assigns documents to training and testing sets, or manual assignment where you explicitly tag each document as training or testing. Both are configured directly in the project's data labeling interface.

Answer

Splitting exclusively by alternating lines within each document.

Answer

External split configuration via an Azure DevOps pipeline YAML file.

Answer

Command-line split using Azure PowerShell scripts only.

Question 10

You are deploying an Azure AI Speech resource using an ARM template. Which parameter defines the name of the resource instance?

Accepted Answer

name. In an ARM template, the 'name' property within the resource block specifies the name of the resource instance being deployed, such as the Azure AI Speech account name. This is a required property for every ARM resource and determines the unique identifier of the deployed resource. The other properties configure SKU, region, and resource type but do not set the instance name.

Answer

sku

Answer

location

Answer

kind

Question 11

What is the purpose of 'Azure Advisor' in an AI solution?

Accepted Answer

To provide best practice recommendations for resources.. Azure Advisor is a personalized cloud consultant that analyzes your deployed resources and telemetry to surface best-practice recommendations across five categories: Cost, Security, Reliability, Operational Excellence, and Performance. In an AI solution, it reviews resources such as Azure OpenAI, Cognitive Services, and supporting compute/storage and flags misconfigurations like missing redundancy or underutilized capacity. It is advisory only — it never modifies resources on your behalf, so it cannot patch or manage anything.

Answer

To manage user identities.

Answer

To automatically patch the AI models.

Answer

To host the web frontend for the AI application.

Question 12

Which Azure feature allows you to control which users can modify or delete your Azure AI resources?

Accepted Answer

Azure RBAC.. Azure RBAC (Role-Based Access Control) is the mechanism that governs who can perform management-plane actions such as modifying or deleting Azure AI resources. You assign roles like Contributor, Owner, or Cognitive Services Contributor to users, groups, or service principals at a scope (management group, subscription, resource group, or resource), and those assignments determine whether modify/delete operations are permitted.

Answer

Conditional Access policies.

Answer

Azure Firewall.

Answer

Resource Locks.

Question 13

You are calling the Azure AI Language REST API for Named Entity Recognition (NER) and receive an HTTP 429 status code. How should your application handle this error?

Accepted Answer

Implement an exponential backoff retry policy and resend the request after a delay.. HTTP 429 indicates 'Too Many Requests', meaning you have exceeded the rate limit or quota for the Azure AI Language service. The correct handling is to implement an exponential backoff retry policy, which gradually increases the delay between retries to avoid overwhelming the service. This is a standard practice for transient throttling errors.

Answer

Switch the API endpoint to use the Free pricing tier.

Answer

Regenerate the subscription key and restart the client service.

Answer

Immediately re-submit the request with doubled payload size.

Question 14

You are monitoring the performance of a deployed Azure OpenAI model. You notice occasional HTTP 429 (Too Many Requests) errors occurring during peak traffic hours. Which monitoring and management action should you take to resolve this issue?

Accepted Answer

Configure Azure Monitor metric alerts for 'Token Generation Rate' and scale up your provisioned throughput deployment capacity.. HTTP 429 from Azure OpenAI indicates rate limiting — the deployment's tokens-per-minute (TPM) or requests-per-minute (RPM) quota is being exceeded. The correct response is to monitor token consumption metrics via Azure Monitor and either increase the provisioned throughput (PTU) capacity or raise the standard deployment's TPM quota, which directly addresses the throttling. Configuring metric alerts on token generation rate provides early warning before 429s spike during peak hours.

Answer

Disable content safety filters to reduce processing latency and throughput overhead.

Answer

Rotate the primary API key immediately to clear the server-side connection cache.

Answer

Update the Network Security Group rules to increase the maximum allowed packet size for the API endpoint.

Question 15

Which Azure AI service should you use if you need to train a model to recognize your company's proprietary product packaging using custom-labeled images with minimal machine learning expertise?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision is purpose-built for training custom image classification and object detection models using your own labeled images, and it abstracts away the ML pipeline (data splitting, hyperparameter tuning, model selection) behind a simple portal and SDK. It requires no ML expertise — you upload images, tag them, and click Train. This makes it the correct choice for recognizing proprietary product packaging with custom labels.

Answer

Azure AI Vision Prebuilt API

Answer

Azure Machine Learning Automated ML

Answer

Azure AI Document Intelligence