AI-103 Practice Test 2 — 30 Questions

Question 1

Your organization has multiple Azure AI services deployed across different resource groups. Your security officer requests a centralized dashboard to monitor security recommendations, vulnerability assessments, and compliance posture for all AI resources. Which Azure service should you open?

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

Accepted Answer

Microsoft Defender for Cloud. Microsoft Defender for Cloud provides a centralized security posture management dashboard that aggregates security recommendations, vulnerability assessments, regulatory compliance, and threat protection across Azure resources, including Azure AI services. It is the correct service to open when a security officer requests a unified view of security recommendations and compliance posture across multiple resource groups.

Answer

Azure Advisor

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Microsoft Entra ID protection portal

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Azure Policy compliance blade

Question 2

You are designing a multi-tenant SaaS application on Azure. Each tenant requires isolation of their Azure OpenAI token usage and cost tracking. What is the recommended provisioning strategy to achieve strict cost and quota isolation per tenant?

Accepted Answer

Provision a separate Azure OpenAI resource for each tenant.. Provisioning a separate Azure OpenAI resource per tenant provides independent quota limits, separate keys, and distinct billing meters, which together deliver strict cost and quota isolation. This is the recommended multi-tenant pattern when isolation is a hard requirement.

Answer

Use a single Azure OpenAI resource for all tenants and track usage using custom HTTP headers.

Answer

Deploy all tenants in the F0 free pricing tier.

Answer

Rely on Azure Cost Management resource group tags alone without resource separation.

Question 3

You are analyzing product reviews using Azure AI Language. You receive sentiment analysis output where a sentence has a positive confidence score of 0.85, a neutral score of 0.10, and a negative score of 0.05. What is the sentiment label assigned to this sentence?

Accepted Answer

positive. Azure AI Language sentiment analysis assigns the label corresponding to the highest confidence score among positive, neutral, and negative. Here positive is 0.85, which is the maximum, so the sentence is labeled positive. The scores are a probability distribution that sums to approximately 1.0.

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neutral

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mixed

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negative

Question 4

You are building a custom named entity recognition model in Language Studio. You want to extract product serial numbers that follow a specific pattern (two uppercase letters followed by four digits, a hyphen, and three digits). Which entity type definition method should you use?

Accepted Answer

Regex entity type. A regex entity type is designed to extract entities that match a specific regular expression pattern, such as two uppercase letters followed by four digits, a hyphen, and three digits (e.g., '[A-Z]{2}\d{4}-\d{3}'). In Language Studio, you define the regex pattern and optionally provide examples, making it ideal for structured identifiers like serial numbers.

Answer

List entity type

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Learned entity type

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Prebuilt entity type

Question 5

You are deploying an Azure AI multi-service account in the West US region. The finance department requires that all resource deployment costs be tracked by department code using Azure Resource Manager tags. Which Azure feature should you use to enforce the mandatory application of the department tag across all future Azure AI resource deployments?

Accepted Answer

Azure Policy. Azure Policy is the governance service that enforces organizational standards by evaluating resources against rules and taking effects such as deny, audit, or append. A policy with an 'append' or 'deny' effect on the tag requirement enforces mandatory department tags across all future deployments.

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Azure Cost Management budgets

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Azure role-based access control (RBAC)

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Azure Blueprints

Question 6

You are implementing prompt engineering for a customer support agent. To minimize hallucinations when the model lacks information, which Prompt Flow technique is most effective?

Accepted Answer

Instructional grounding to only answer from context. Instructional grounding — explicitly instructing the model to answer only from the provided context and to say it doesn't know otherwise — is the most direct and effective prompt-engineering technique to minimize hallucinations when the model lacks information. It constrains the model's generation to the retrieved context rather than its parametric memory.

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Chain-of-thought prompting

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Increasing the Top_P parameter

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Few-shot prompting with varied examples

Question 7

You are using Azure AI Document Intelligence to extract information from identity documents, such as passports and driver licenses. Which prebuilt model should you select?

Accepted Answer

prebuilt-idDocument. The prebuilt-idDocument model is specifically designed to extract key information from identity documents such as passports, driver licenses, and U.S. state IDs. It returns structured fields like name, date of birth, document number, and expiration date. This is the only prebuilt model among the options that targets identity documents.

Answer

prebuilt-tax.us.w2

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prebuilt-W2

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prebuilt-businessCard

Question 8

You are developing a RAG solution using Azure AI Search. Users are searching using domain-specific acronyms that are not well-represented in standard embedding models, resulting in poor retrieval scores. What feature should you enable in Azure AI Search to improve keyword matching for exact acronyms?

Accepted Answer

Hybrid search combining vector search and full-text BM25 search. Hybrid search in Azure AI Search runs vector (k-NN) search and full-text BM25 keyword search in parallel, then fuses the results using Reciprocal Rank Fusion (RRF). BM25 excels at exact term matching, so domain-specific acronyms that embedding models underrepresent will still be matched lexically, dramatically improving recall for those queries. This is the standard remedy when semantic/vector retrieval alone misses rare tokens.

Answer

Increasing the embedding dimension size to 3072

Answer

Enabling Azure AI Content Safety filters

Answer

Setting the semantic ranker threshold to zero

Question 9

Which file format is NOT natively supported for training custom models in Azure AI Document Intelligence?

Accepted Answer

DOCX. Azure AI Document Intelligence custom model training accepts PDF, JPEG, PNG, BMP, TIFF, and HEIF image formats, but DOCX (Word) files are not natively supported as training inputs. To train on Word content, you must first convert DOCX to PDF or render pages as images before uploading to the labeled training set. This is why DOCX is the correct answer to 'which is NOT natively supported.'

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PDF

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JPEG

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BMP

Question 10

You are provisioning an Azure AI Language resource and want to ensure that all telemetry and activity logs are retained for compliance auditing over a 365-day period. Where should you configure this retention policy?

Accepted Answer

In the resource's Diagnostic settings by specifying a retention period on a storage account destination.. Diagnostic settings in Azure allow you to configure the collection and retention of telemetry and activity logs. By specifying a retention period (e.g., 365 days) on a storage account destination, you ensure logs are retained for compliance auditing. This is the correct place to set retention policies for Azure AI Language resource logs.

Answer

In the Microsoft Defender for Cloud security policy blade.

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In the Azure AI Language service Studio configuration panel.

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In the Azure Cost Management budget alert settings.

Question 11

You are designing an agentic workflow in Azure AI Foundry that requires the agent to call an external REST API. Which component should you use to define the tool interface?

Accepted Answer

Tool definition using a Python function. In Azure AI Foundry agentic workflows, tools are defined as callable functions with a name, description, and parameter schema; a Python function definition (often decorated or registered as a tool) is the standard way to expose an external REST API to the agent. The model then emits structured arguments that invoke that function. This is the correct component for defining the tool interface.

Answer

Azure AI Content Safety

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LLM output parser

Answer

Flow input schema

Question 12

You are troubleshooting an Azure AI Language asynchronous summarization job. You submitted a batch of documents using the `/analyze` endpoint with long-running operations (LRO). How should your application check the status and retrieve the results of the job?

Accepted Answer

Poll the URL provided in the 'operation-location' response header using a GET request until the status is 'succeeded'.. Azure AI Language asynchronous (LRO) jobs return an 'operation-location' header in the initial 202 Accepted response. The application must poll that URL with GET requests until the status field reads 'succeeded', then retrieve results from the same endpoint. This is the standard Azure long-running operation pattern.

Answer

Listen for incoming webhook notifications on an Azure Event Grid subscription attached to the text analytics resource.

Answer

Query the Azure Monitor metrics API for job completion status.

Answer

Repeatedly send synchronous POST requests to the same document endpoint until a 200 OK is returned.

Question 13

You have an Azure AI application that is hitting rate limits (429 errors). You need to determine which specific client application is causing the issue. How do you identify this?

Accepted Answer

Enable diagnostic logging to Log Analytics and run a KQL query.. Diagnostic logging to Log Analytics captures per-request telemetry including the caller identity, subscription, resource, operation name, and HTTP status code, so a KQL query filtering on ResponseCode == 429 and summarizing by CallerIpAddress or identity reveals which client is exhausting the quota. The Activity Log only records control-plane operations (resource create/update/delete), not data-plane inference calls, so it cannot show which app is making the requests.

Answer

Check the 'Activity Log'.

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Increase the resource quota in the 'Quotas' blade.

Answer

Restart the AI service.

Question 14

Which THREE of the following are components of a standard Prompt Flow DAG (Directed Acyclic Graph)?

Accepted Answer

Inputs. In a standard Prompt Flow DAG, Inputs (A) are the declared entry points that supply values (for example, a question string or a dataset column) to the flow, making them a required component. Outputs (B) are also required because they define the flow's returned values (such as the model's answer or a score) that downstream consumers or evaluations read. Tools (D) are the executable nodes of the DAG — LLM, Python, Prompt, and built-in tools — and the edges connecting them define the directed acyclic execution order. Model Training Jobs (C) are not part of a Prompt Flow DAG; training is handled by separate Azure Machine Learning job constructs, not flow nodes. Data Labeling Projects (E) are likewise a distinct Azure Machine Learning feature for annotating data and are not components of a Prompt Flow DAG.

Answer

Model Training Jobs

Answer

Data Labeling Projects

Question 15

You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?

Accepted Answer

Configure a maximum iteration step limit in the agent orchestration loop. Agentic workflows in Azure AI Foundry (and similar frameworks like Semantic Kernel or AutoGen) require a bounded orchestration loop; configuring a maximum iteration or step limit causes the orchestrator to terminate or escalate when the agent exceeds the threshold, preventing infinite tool-calling loops. This is the standard architectural safeguard for multi-step reasoning agents. It directly addresses the symptom of repeated identical tool calls.

Answer

Increase the vector store chunk overlap size

Answer

Enable Azure API Management caching for the tool endpoint

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Set the model frequency penalty to 2.0

Question 16

You are configuring a secure enterprise architecture where Azure AI Vision calls must not traverse the public internet. You create a private endpoint for your Azure AI Services resource in your Virtual Network. Which DNS configuration step is required for client applications within the VNet to resolve the private endpoint correctly?

Accepted Answer

Configure Azure Private DNS zones corresponding to the cognitive services DNS zone and link it to your VNet.. When you create a private endpoint for an Azure AI Services resource, the private IP is associated with the service's public DNS name (for example, <resource>.cognitiveservices.azure.com). For clients in the VNet to resolve that name to the private IP instead of the public IP, you must create an Azure Private DNS zone named privatelink.cognitiveservices.azure.com and link it to the VNet, with an A record mapping the resource to the private endpoint IP.

Answer

Add the endpoint IP to the local hosts file of every Azure AI Vision instance.

Answer

Configure public DNS records on your corporate DNS server to point to the public IP.

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Modify the routing table to drop all DNS traffic.

Question 17

Which THREE attributes are critical for a successful custom model training set?

Accepted Answer

High-quality images. Option A (High-quality images) is correct because custom model training relies on OCR and layout analysis, so clear, well-lit, high-resolution scans or photos with minimal skew, blur, and noise give the model reliable visual features to learn from. Option B (Consistent labeling across all documents) is correct because supervised training requires ground-truth labels to be applied with the same schema and conventions throughout the dataset; inconsistent labels introduce contradictory signals that degrade accuracy and confuse the model. Option C (Representative samples of all document layouts) is correct because the model must generalize to every form, template, and layout it will encounter in production, so the training set must include examples covering the full variety of structures, fonts, and field placements. Option D is not required because documents need not come from a single printer; in fact, variety in printers, scanners, and capture conditions improves robustness. Option E is not required because there is no fixed minimum of 1,000 pages per model; the needed volume depends on document complexity and diversity, and quality and representativeness matter more than a rigid page count.

Answer

All documents from the same printer

Answer

At least 1,000 pages per model

Question 18

Your company requires that all Azure AI service endpoints are accessible only from within a specific Azure Virtual Network (VNet) and that public internet access is completely disabled. How should you configure the Azure AI resource networking settings?

Accepted Answer

Disable public network access on the Networking blade and create a Private Endpoint associated with your VNet and subnet.. Disabling public network access and creating a Private Endpoint associated with the VNet and subnet ensures the Azure AI resource is reachable only through private IP addresses inside the virtual network. This fully removes public internet exposure, which matches the requirement.

Answer

Set the routing preference to Microsoft network routing and enable service endpoints on the default subnet.

Answer

Configure the firewall to allow only the corporate public IP address range and keep public network access enabled.

Answer

Deploy an Azure Application Gateway in front of the Azure AI multi-service account endpoint.

Question 19

You are designing an enterprise RAG solution using Azure AI Search and Azure OpenAI. You need to ensure data security and compliance across different user permission tiers. Which TWO actions should you implement? (Choose two.)

Accepted Answer

Ensure all data transfers between Azure AI Search and Azure OpenAI utilize TLS 1.2 or higher.. Option A is correct because Azure AI Search and Azure OpenAI communicate over HTTPS endpoints, and enforcing TLS 1.2 or higher protects data in transit against interception or downgrade attacks, which is a baseline requirement for enterprise security and compliance. Option B is correct because Azure AI Search supports document-level access control via security filters (for example, using the user's group IDs in a filterable field or the built-in ACL/rbacScope fields), ensuring each permission tier only retrieves documents its users are authorized to see, which is essential in a multi-tenant RAG solution. Option C is wrong because storing unencrypted authentication tokens in embedding metadata exposes credentials to anyone querying the index and violates token-handling best practices. Option D is wrong because granting admin privileges to all users removes least-privilege enforcement and does not address retrieval latency in a legitimate way. Option E is wrong because disabling managed identities and embedding plaintext connection strings in source code leaks secrets and discards Azure's keyless, identity-based authentication model.

Answer

Store unencrypted user authentication tokens directly inside the embedding vector metadata.

Answer

Grant every user application admin privileges to bypass retrieval latency checks.

Answer

Disable managed identities and store plaintext connection strings in the application source code.

Question 20

You are analyzing product reviews and need to identify specific product features mentioned by users along with whether users expressed positive or negative opinions about those specific features. Which advanced feature of sentiment analysis should you enable?

Accepted Answer

Opinion mining. Opinion mining, also called aspect-based sentiment analysis, is the advanced sentiment analysis feature that identifies specific aspects (product features) mentioned in text and determines the sentiment expressed toward each one. It returns aspect-sentiment pairs, which is exactly what is needed to know whether users felt positively or negatively about particular product features. This makes opinion mining the correct choice.

Answer

Extractive summarization

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Key phrase extraction

Answer

Named Entity Recognition