AI-103 Practice Test 26 — 15 Questions

Question 1

You are configuring an Azure AI Language service resource in the Azure portal. Which THREE settings or blades are available for managing the resource? (Choose three.)

Accepted Answer

Keys and Endpoint. Option C, Keys and Endpoint, is correct because every Azure AI Language resource exposes a Keys and Endpoint blade in the portal where you retrieve the subscription key(s) and the REST endpoint URL used to authenticate and call the service. Option D, Pricing tier, is correct because the Azure AI Language resource is created on a pricing tier (for example Free F0 or Standard S) that determines quotas, throughput, and billing, and it can be viewed or changed in the portal. Option E, Identity, is correct because the resource supports managed identities, and the Identity blade lets you enable a system-assigned identity or assign user-assigned identities for keyless authentication to other Azure services. Option A, Virtual machine size selector, does not belong because Azure AI Language is a fully managed PaaS cognitive service and does not provision or size VMs. Option B, Container registry integration blade, does not belong because, although Azure AI Language offers Docker containers, the resource itself does not include a container registry integration blade in the portal.

Answer

Virtual machine size selector

Answer

Container registry integration blade

Question 2

When planning the deployment of an Azure AI Search service, which TWO sizing and capacity parameters must you configure during creation? Each correct answer presents part of the solution.

Accepted Answer

Replicas. During creation of an Azure AI Search service, you must choose the number of replicas (Option B) and partitions (Option C) as part of the pricing tier and capacity configuration. Replicas determine high availability and query throughput by creating multiple copies of the index that also enable read replicas for query load balancing. Partitions determine storage capacity and write throughput by splitting the index across multiple shards, and together replicas and partitions define the service's scaling units (SUs). Options A, D, and E are not Azure AI Search creation parameters: virtual CPU core count per hypervisor, Linux kernel version, and Active Directory domain controller IP are infrastructure or OS-level settings that Azure manages and are not exposed when provisioning an Azure AI Search service.

Answer

Virtual CPU core count per hypervisor

Answer

Linux kernel version

Answer

Active Directory domain controller IP

Question 3

You are monitoring an Azure OpenAI deployment in Azure AI Foundry and notice an increase in HTTP 429 (Too Many Requests) errors. Your application needs to handle these rate limit spikes gracefully. What is the recommended architectural solution?

Accepted Answer

Implement exponential backoff and jitter retry logic in the application SDK. HTTP 429 responses from Azure OpenAI indicate the deployment has exceeded its tokens-per-minute or requests-per-minute quota. The documented, recommended client-side mitigation is exponential backoff with jitter, which spaces retries out and randomizes them to avoid synchronized retry storms. The Azure OpenAI SDKs and Polly-style libraries implement this pattern natively.

Answer

Restart the Azure OpenAI resource from the Azure Portal

Answer

Set the model temperature parameter to 0.0

Answer

Switch the authentication method from API keys to Managed Identity

Question 4

You are configuring an Azure AI Document Intelligence container for deployment in an on-premises disconnected environment. Which environment variable is mandatory for the container to start successfully and report billing usage?

Accepted Answer

EULA=accept, billing=, apikey=. Document Intelligence containers require three environment variables to start and report billing: EULA=accept (to acknowledge the license), billing= (the Azure endpoint that receives usage telemetry), and apikey= (the key for that billing endpoint). Without all three, the container will not start or will not report usage, which is mandatory even in disconnected environments because billing telemetry is sent periodically when connectivity is available.

Answer

AZURE_COGNITIVE_SERVICES_TENANT_ID

Answer

AZURE_AI_CONNECTOR_CONNECTION_STRING

Answer

DOCUMENT_INTELLIGENCE_MODEL_CACHE_DIR

Question 5

You are migrating an Azure AI service from one region to another. What is the most efficient way to migrate the configuration?

Accepted Answer

Export the resource as an ARM template and deploy it to the new region.. Exporting the resource as an ARM template captures its full configuration declaratively, and deploying that template to the new region recreates the resource with identical settings efficiently. This is the recommended infrastructure-as-code approach for regional migration of Azure AI services, avoiding manual error and preserving configuration fidelity. It is faster and more reliable than rebuilding resources by hand.

Answer

Restore a backup from a Recovery Services vault.

Answer

Use Azure Data Factory to copy the resource.

Answer

Manually recreate all resources in the new region.

Question 6

You are optimizing a RAG pipeline in Azure AI Foundry. The model is hallucinating when retrieving documents from a large corpus. Which strategy should you implement to improve grounding accuracy?

Accepted Answer

Implement a Cross-Encoder re-ranking step. Cross-Encoder re-ranking is correct because it jointly encodes the query and each candidate document, producing a far more accurate relevance score than the bi-encoder cosine similarity used in initial vector retrieval. This precision filtering removes semantically similar but contextually irrelevant chunks before they reach the LLM, directly reducing hallucination caused by noisy or off-topic grounding context. In Azure AI Foundry, this is typically implemented via the semantic ranker or a custom re-ranking stage in the RAG orchestration pipeline.

Answer

Increase the temperature of the model to 1.0

Answer

Use a larger embedding model

Answer

Increase the chunk size to include the entire document

Question 7

You are monitoring your Azure OpenAI deployment in Azure AI Foundry. You notice an increase in HTTP 429 status codes. What does this error indicate?

Accepted Answer

Rate limit or token quota exceeded (Too Many Requests). HTTP 429 'Too Many Requests' indicates the client has exceeded the rate limit or token quota assigned to the Azure OpenAI deployment. This occurs when requests per minute (RPM) or tokens per minute (TPM) surpass the deployment's quota, triggering throttling until the window resets.

Answer

Internal server error at Microsoft

Answer

Model deployment not found

Answer

Invalid API key authentication

Question 8

Which THREE options are available for configuring network security in Azure?

Accepted Answer

Azure Private Link / Private Endpoints.. Azure Private Link / Private Endpoints (A) is correct because it provides private connectivity to Azure PaaS services over a private IP address within your virtual network, removing exposure to the public internet and thus directly configuring network-level security. Azure Firewall (D) is correct because it is a managed, cloud-based network security service that provides stateful firewall capabilities, FQDN filtering, and threat intelligence-based filtering at the network and application layers. Network Security Groups (E) are correct because they filter network traffic to and from Azure resources in a virtual network using inbound and outbound security rules based on source/destination IP, port, and protocol. Azure Storage Account Keys (B) are access credentials for authenticating to storage, not a network security configuration mechanism. Microsoft Entra ID Groups (C) are used for identity and access management (authorization), not for configuring network security controls.

Answer

Azure Storage Account Keys.

Answer

Azure Active Directory Groups.

Question 9

You are building an agentic workflow in Azure AI Foundry using the Semantic Kernel framework. The agent needs to call external APIs dynamically based on user intent. Which component should you register with the kernel to enable this capability?

Accepted Answer

A plugin containing native or semantic functions. In Semantic Kernel, plugins encapsulate native functions (code) or semantic functions (prompts) that the kernel can invoke. Registering a plugin with the kernel enables the agent to call external APIs dynamically based on user intent, as the kernel orchestrates function calls.

Answer

A system prompt template

Answer

A sequential planner instance

Answer

A custom memory store

Question 10

How do you handle sensitive user data when using an Azure AI Agent?

Accepted Answer

Use Data Masking/PII filtering before the data reaches the agent.. Responsible handling of sensitive data in Azure AI Agents requires filtering or masking PII before it reaches the agent, so that personal data is not sent to the model, logged in prompts, or persisted in agent threads. Data masking/PII filtering (for example via Azure AI Language PII detection or a pre-processing step in Prompt Flow) enforces this at the boundary. This aligns with privacy-by-design and data-minimization principles required for Responsible AI compliance.

Answer

Send all data to the model; the model will ignore it.

Answer

Increase the model parameter size.

Answer

Store user PII in the vector index.

Question 11

You are managing an Azure OpenAI model deployment in an active-passive multi-region disaster recovery architecture. A regional outage occurs, and you failover traffic to the secondary region. However, clients report receiving authentication errors. What is the most likely cause?

Accepted Answer

The client application's managed identity has not been granted the required RBAC roles on the secondary Azure OpenAI resource.. When using managed identity for authentication, the identity must be granted the appropriate RBAC role (such as Cognitive Services OpenAI User) on each Azure OpenAI resource it accesses. During failover to a secondary resource in another region, that resource is a distinct Azure resource with its own scope, so role assignments from the primary do not automatically carry over. The authentication error is therefore caused by missing RBAC role assignments on the secondary resource.

Answer

Azure automatically deletes API keys during a regional failover.

Answer

Azure OpenAI models require re-training after any regional failover event.

Answer

The primary region's DNS zone must be manually deleted for failover to work.

Question 12

Which authentication method is recommended to prevent 'key leakage' in Azure AI Services?

Accepted Answer

Using Microsoft Entra ID authentication.. Microsoft Entra ID authentication replaces static API keys with short-lived OAuth 2.0 tokens issued to an identity, so there is no long-lived secret to leak. Access is governed by RBAC roles on the AI resource, and tokens expire automatically. This is the recommended passwordless pattern for preventing key leakage in Azure AI Services.

Answer

Storing keys in a plaintext file.

Answer

Creating custom headers for API requests.

Answer

Regenerating keys every 30 days.

Question 13

You are configuring an Azure AI Document Intelligence prebuilt model to extract key-value pairs from standard invoices. Which API operation should your application call to process the document asynchronously?

Accepted Answer

POST /documentintelligence/documentModels/prebuilt-invoice:analyze?api-version=2024-02-29-preview. The correct async analyze operation for a prebuilt model in the 2024-02-29-preview API version is POST /documentintelligence/documentModels/{modelId}:analyze, where {modelId} is prebuilt-invoice. This returns a 202 Accepted with an Operation-Location header that your app polls until the analysis completes. The :analyze suffix is the long-running-operation (LRO) pattern used by Document Intelligence.

Answer

POST /documentintelligence/documentModels/prebuilt-invoice:train?api-version=2024-02-29-preview

Answer

POST /documentintelligence/models/prebuilt-invoice/analyze?api-version=2024-02-29-preview

Answer

POST /formrecognizer/v2.1/prebuilt/invoice/analyze

Question 14

You are writing a system prompt for an Azure OpenAI model deployed in Azure AI Foundry. You want to ensure the model responds only using the provided context and refuses to answer if the context is insufficient. What technique should you use?

Accepted Answer

Include explicit grounding instructions and a negative constraint in the system prompt. Explicit grounding instructions in the system prompt — such as 'Answer only using the provided context; if the context does not contain the answer, respond that you do not know' — are the standard prompt-engineering technique to constrain the model to retrieved context and refuse out-of-scope questions. This is a negative constraint that directly shapes model behavior at inference time. It is the correct, low-cost, deterministic approach for RAG grounding.

Answer

Configure Azure AI Content Safety severity levels to high

Answer

Set the model temperature to 1.0 and top_p to 0.0

Answer

Enable semantic caching on the Azure OpenAI deployment

Question 15

You are managing prompt assets in Azure AI Foundry. Your team collaborates on developing, testing, and versioning system prompts. Which feature in Azure AI Foundry should you use to store and manage these prompt templates?

Accepted Answer

Prompt flow / Prompt management workspace assets. Prompt flow / Prompt management workspace assets is correct because Azure AI Foundry provides a dedicated prompt asset store where teams can author, version, test, and share system prompts and prompt templates as first-class workspace artifacts. This enables collaboration, change tracking, and reproducibility across the development lifecycle, which is exactly what the scenario requires. Prompt flow integrates these assets into orchestrated LLM workflows with evaluation and deployment support.

Answer

Azure Storage Blob lifecycle management policies

Answer

Azure App Service deployment slots

Answer

Azure Key Vault secret versions