AI-103 Practice Test 4 — 10 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.

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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.

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

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Deploy all tenants in the F0 free pricing tier.

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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.

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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.

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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.

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Increasing the embedding dimension size to 3072

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Enabling Azure AI Content Safety filters

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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.

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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.