AI-103 Practice Test 16 — 15 Questions

Question 1

Your company processes business cards from international clients. Which prebuilt Azure AI Document Intelligence model should you use to extract names, company names, emails, and phone numbers from these cards?

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

Accepted Answer

prebuilt-businessCard. The prebuilt-businessCard model is specifically trained to extract common fields from business cards, including names, company names, emails, and phone numbers. It is optimized for the typical layout and content of business cards, making it the correct choice for this scenario.

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prebuilt-invoice

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prebuilt-layout

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prebuilt-receipt

Question 2

You are developing a multi-agent orchestration solution using Azure AI Agent Service and want to ensure that user inputs are safely evaluated before they are passed to the model. Which component should you configure to inspect and filter incoming prompts for malicious content and restricted topics?

Accepted Answer

Azure AI Content Safety integrated into the model gateway. Azure AI Content Safety integrated into the model gateway inspects incoming prompts for harmful content, jailbreak attempts, and restricted topics before they reach the model, providing a centralized safety layer for multi-agent orchestration. This is the correct component for pre-model prompt evaluation and filtering.

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Azure Key Vault access policies

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Azure Cognitive Search semantic ranker

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Azure API Management rate limiting policy

Question 3

When using Prompt Flow, you notice that your flow is failing at the 'aggregate' node. What is the most likely cause?

Accepted Answer

The input data from the previous node does not match the expected schema of the aggregate node.. The aggregate node in Prompt Flow is used to reduce or summarize outputs from a loop (e.g., computing an average or concatenating results). It expects a specific input schema—typically a list or a structured object produced by the preceding node. If the upstream node emits data that does not match that schema (wrong type, missing keys, or not a list), the aggregate node fails.

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The system prompt is too long.

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The search index is empty.

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The model is too busy.

Question 4

You are designing a generative AI solution in Azure AI Foundry. You need to ensure that user data sent to Azure OpenAI is not used to train Microsoft's foundational models. What is Microsoft's default data privacy policy for Azure OpenAI?

Accepted Answer

Customer data is not used to train foundation models by default. Microsoft's default policy for Azure OpenAI is that customer data (prompts and completions) is not used to train foundation models and is not shared with model providers. Data is processed only for the requested inference and retained per the service's data retention terms, not for training.

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Customer data is stored in public Azure storage accounts

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Customer data is automatically published to public hugging face repositories

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Customer data is used for model training unless you opt out by submitting a support ticket

Question 5

You are implementing Retrieval-Augmented Generation (RAG) using Azure AI Search as the retriever and Azure OpenAI as the generator. Users report that the generated responses frequently miss critical context located in the middle of long retrieved documents. Which grounding optimization technique should you apply?

Accepted Answer

Implement semantic ranking and reorder retrieved documents so the highest-scored chunks appear at the very beginning and end of the prompt context.. LLMs exhibit a 'lost in the middle' bias where content at the beginning and end of the context window is attended to more strongly than content in the middle. Applying semantic ranking to surface the most relevant chunks and then reordering them so the highest-scored chunks sit at the start and end of the prompt mitigates this positional bias and improves grounding.

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Increase the temperature parameter of the Azure OpenAI model to 1.2 to encourage creative synthesis of the middle content.

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Switch the Azure AI Search retriever from hybrid search to pure keyword BM25 search.

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Reduce the chunk size to 64 tokens to force the retriever to return hundreds of tiny fragments.

Question 6

You need to optimize the retrieval performance of your RAG agent. You find that the search engine returns too many irrelevant documents. What should you adjust?

Accepted Answer

Configure Hybrid Search settings in the search index.. Hybrid Search in Azure AI Search combines keyword (BM25) and vector similarity with Reciprocal Rank Fusion (RRF), and lets you tune parameters like k-nearest neighbors count, semantic ranker, and weighting to improve precision. When too many irrelevant documents are returned, adjusting hybrid search settings — such as enabling semantic reranking or reducing top-k — directly addresses retrieval relevance. The other options affect generation, not retrieval.

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Increase the model temperature.

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Change the model to a newer version.

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Reduce the system prompt length.

Question 7

You are building a RAG application where documents are updated frequently. You need to ensure that the Azure AI Search index reflects document deletions and updates in near real-time without performing full re-indexing. How should you design the ingestion pipeline?

Accepted Answer

Configure Azure AI Search indexers with change tracking and scheduled incremental synchronization. Azure AI Search indexers support change tracking via the data source's native mechanisms (e.g., SQL rowversion, Cosmos DB change feed, or blob soft-delete policies) and run on a schedule to detect and apply only changed or deleted documents. This incremental synchronization keeps the index current in near real-time without full re-indexing. It is the designed pattern for frequently updated content.

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Increase the Azure OpenAI model deployment concurrency quota

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Enable semantic caching on the OpenAI endpoint

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Delete the entire search service and recreate the index daily using a batch script

Question 8

You are training a custom template model in Azure AI Document Intelligence and notice that one of your key fields is consistently failing to extract on documents where the label text is vertically oriented. How should you address this issue?

Accepted Answer

Re-examine and ensure all instances of the field across training samples are accurately tagged with correct bounding regions in Document Intelligence Studio.. The issue is likely due to inaccurate labeling of the field in the training samples, especially for vertically oriented text. Ensuring all instances of the field are accurately tagged with correct bounding regions in Document Intelligence Studio will help the model learn to extract the field regardless of orientation.

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Switch the model type from custom template to prebuilt read model.

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Set the queryParameters property to 'orientation=vertical' in the training API request body.

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Rotate the source PDF documents to landscape orientation prior to uploading them for training.

Question 9

You are optimizing a RAG pipeline in Azure AI Foundry. Users complain that answers are missing key numerical details because important tables within PDF documents are being split across chunk boundaries during ingestion. What is the recommended remediation?

Accepted Answer

Use Azure AI Document Intelligence with layout extraction to parse tables and maintain table structure during chunking. Azure AI Document Intelligence with layout extraction preserves table structure by detecting rows, columns, and cells during parsing, so tables are chunked as coherent units rather than split arbitrarily. This keeps numerical relationships intact and prevents the retrieval step from returning partial tables that omit key figures. It is the recommended remediation for table-splitting problems in RAG ingestion.

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Increase the embedding model dimensions from 1536 to 3072

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Set the Azure OpenAI model max_tokens parameter to 4096

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Apply Azure AI Content Safety image analysis on all PDF pages

Question 10

When planning an Azure AI solution deployment strategy, what are THREE key considerations for model versioning and lifecycle management? (Choose three.)

Accepted Answer

Implementing automated regression testing in CI/CD pipelines when upgrading model versions.. Option A is correct because automated regression testing in CI/CD pipelines validates that a new model version meets quality, accuracy, and safety baselines before promotion, catching behavioral drift or breaking changes introduced by the upgrade. Option B is correct because Microsoft publishes deprecation and retirement schedules for specific model versions in Azure OpenAI and Azure AI services, and solutions must track these dates to migrate before a version is retired and API calls begin failing. Option E is correct because model upgrades can introduce unexpected behavior, so a documented rollback plan—such as pinning the previous version or using deployment slots to revert traffic—ensures continuity if the new version underperforms. Option C is not appropriate because deleting older versions immediately removes the ability to roll back and may break pinned deployments still serving production traffic. Option D is not appropriate because hardcoding a 'latest' model string without explicit version specifiers makes behavior non-deterministic and can silently break when Microsoft updates or retires that alias.

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Deleting older model versions immediately upon release of a new version to save cloud storage costs.

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Hardcoding the latest model version string into production code without version specifiers.

Question 11

Which TWO metrics are reported by Document Intelligence Studio to evaluate model performance?

Accepted Answer

Recall. Document Intelligence Studio reports Recall (A) and Precision (D) as the two core metrics for evaluating a custom model's extraction quality. Recall measures the proportion of actual ground-truth field values that the model correctly extracted, indicating how many true items were found. Precision measures the proportion of the model's extracted values that are actually correct, indicating how many of its predictions were accurate. These two metrics together characterize the model's accuracy on labeled test data in the Studio's evaluation view. Throughput (B) and Latency (C) are performance/operational characteristics rather than model accuracy metrics, and Confidence score (E) is a per-prediction value returned by the model, not an aggregate evaluation metric reported by the Studio.

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Throughput

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Latencey

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Confidence score

Question 12

Your organization is planning to deploy multiple Azure AI services and needs to implement robust cost management and governance practices. Which TWO actions should you take? (Choose two.)

Accepted Answer

Implement Azure Policy to enforce mandatory cost-tracking tags on all AI resource deployments.. Option A is correct because Azure Policy can enforce tagging requirements (such as cost center, owner, or environment tags) at deployment time, and consistent tags are the foundation for cost tracking, chargeback, and filtering in Azure Cost Management and Billing. Option D is correct because separating AI resources into distinct resource groups and subscriptions by business unit and environment provides clear cost boundaries, enables per-subscription budgets and access control, and prevents dev/test spend from being commingled with production. Option B is not appropriate because consolidating all environments into one resource group removes cost and access separation and makes it hard to attribute spend or apply environment-specific governance. Option C is not appropriate because disabling diagnostic logging sacrifices auditability, security monitoring, and troubleshooting, and cost reduction should not come at the expense of required telemetry. Option E is not appropriate because the F0 free tier has strict call and transaction limits and no production SLA, so it cannot support production AI workloads.

Answer

Deploy all development, test, and production AI resources into a single shared resource group to simplify administration.

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Disable diagnostic logging across all AI resources to reduce Azure storage and log ingestion costs.

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Use the Free (F0) pricing tier for all production workloads to eliminate infrastructure costs.

Question 13

You need to view the latency of requests made to an Azure AI services endpoint. Where do you find this information?

Accepted Answer

Azure Monitor metrics.. Azure Monitor metrics is the platform service that collects and displays numerical telemetry such as request latency, request count, and error rates for Azure AI services endpoints. You can view latency metrics in the Azure Portal under the resource's Metrics blade or via the Azure Monitor API. This is the standard place to observe performance data for Azure resources.

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The Subscription billing dashboard.

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The 'Deployment' logs in the machine learning workspace.

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Azure Advisor recommendations.

Question 14

You are managing an enterprise Azure OpenAI resource that experiences bursty traffic patterns. To optimize cost without sacrificing availability during sudden workload spikes, you decide to implement a caching layer for frequent prompts and responses. Which Azure service should you integrate in front of Azure OpenAI?

Accepted Answer

Azure Cache for Redis. Azure Cache for Redis provides a low-latency, in-memory caching layer that can store frequent prompt-response pairs, reducing the number of calls to Azure OpenAI during bursty traffic. This optimizes cost by serving cached responses and maintains availability by offloading repeated requests from the OpenAI resource. It is the standard Azure service for application-level caching in front of AI endpoints.

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Azure Traffic Manager routing profile

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Azure ExpressRoute local gateway

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Azure Content Delivery Network (CDN)

Question 15

Which THREE fields are typically returned by the prebuilt-receipt model?

Accepted Answer

MerchantName. The prebuilt-receipt model in Azure AI Document Intelligence is designed to extract common receipt fields, and MerchantName (A) is one of them because it identifies the vendor or business that issued the receipt. TransactionDate (C) is also returned since the model recognizes the date the purchase occurred, which is a standard receipt field. Total (E) is included as the model extracts the final amount paid, a core field on virtually every receipt. EmployeeID (B) is not a standard receipt field and is not returned by this model, as it relates to internal staff identifiers rather than receipt data. PatientName (D) is specific to healthcare or medical documents and is not part of the prebuilt-receipt schema.

Answer

EmployeeID

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PatientName