AI-103 Practice Test 3 — 10 Questions

Question 1

You are deploying a model in Azure AI Foundry. You need to ensure that the model endpoint is protected by a virtual network. Which configuration should you use?

Accepted Answer

Configure a Private Endpoint for the AI resource. Configuring a Private Endpoint for the AI resource is correct because Azure Private Link creates a private IP address within your virtual network for the Azure AI Foundry resource, ensuring all traffic to the model endpoint stays on the Microsoft backbone and never traverses the public internet. This is the standard mechanism for VNet-protected PaaS endpoints in Azure.

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Enable public internet access with IP firewall rules

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Enable Managed Identity on the endpoint

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Deploy a dedicated Azure Application Gateway

Question 2

You are deploying a custom text classification model using Azure AI Language. You need to implement cost management controls and track exact expenditures by tagging the Azure AI resource with the internal project cost center 'CC-409'. Where should you configure these tags in the Azure Portal?

Accepted Answer

Under the Resource management > Tags section of the Azure AI Services resource blade. Tags in Azure are configured under the Resource management > Tags section of the Azure AI Services resource blade. This allows you to assign metadata such as cost center 'CC-409' to the resource, which is then used for cost tracking and management. Tags are key-value pairs that can be applied to resources, resource groups, and subscriptions for organizational and billing purposes.

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Within the Networking blade under Private endpoint connections

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In the Access control (IAM) settings as a custom role assignment

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In the Diagnostic settings by creating a new metric alert rule

Question 3

In Azure AI Foundry, which tool would you use to measure the quality of your RAG application against a golden dataset?

Accepted Answer

Evaluation. The Evaluation tool in Azure AI Foundry is designed to measure the quality of generative AI applications, including RAG systems, against a golden dataset using metrics such as groundedness, relevance, coherence, and fluency. It provides both built-in and custom evaluators, making it the correct choice for quality measurement. Model Catalog, Deployment, and Prompt Flow Tracing serve different purposes.

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Model Catalog

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Deployment

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Prompt Flow Tracing

Question 4

You are developing an application to extract specific fields from invoices using Azure AI Document Intelligence. Which prebuilt model should you use for this task?

Accepted Answer

prebuilt-invoice. The prebuilt-invoice model is purpose-built for invoice documents and returns structured fields such as VendorName, InvoiceDate, InvoiceTotal, DueDate, CustomerName, and line items without any training. Because the task is extracting specific fields from invoices, this prebuilt model directly matches the document type and required output. Custom models or generic layout models would require extra work and would not return invoice-specific fields out of the box.

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custom-template

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prebuilt-layout

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prebuilt-document

Question 5

You are troubleshooting a low confidence score on specific fields extracted by your Azure AI Document Intelligence custom template model. Which TWO actions can help improve the extraction confidence for those fields? (Choose TWO)

Accepted Answer

Ensure all training documents are of high visual quality and free from blur or heavy skew.. Option C is correct because custom template models learn field positions and visual patterns from the labeled samples, so blurry, skewed, or low-quality training documents degrade the model's ability to reliably locate and read those fields, producing low confidence scores; supplying sharp, straight, high-quality scans directly improves feature extraction and OCR accuracy. Option D is correct because low confidence on specific fields usually indicates the model has not seen enough layout variation for those fields, so adding diverse training documents that capture the different layouts, fonts, and positions where those fields appear lets the model generalize and raise its confidence. Option A is wrong because the confidence threshold in the analyzeDocument request only filters which results are returned or flagged; it does not change the model's actual extraction confidence. Option B is wrong because converting to grayscale and downsampling to 72 DPI reduces image fidelity and typically lowers OCR and extraction quality rather than improving confidence. Option E is wrong because a prebuilt business card model is a fixed, general-purpose model that cannot be trained on your specific fields or layouts, so it cannot improve confidence for your custom template fields.

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Increase the confidence threshold parameter in the analyzeDocument request body to 0.99.

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Convert all training PDFs to grayscale and reduce their resolution to 72 DPI to speed up processing.

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Switch the model type from custom template to a prebuilt business card model.

Question 6

You are developing a global news aggregation application using the Azure AI Language service. You need to automatically identify the primary language of incoming user-submitted articles across 120 different languages before routing them to translation pipelines. Which API endpoint should you call?

Accepted Answer

POST /language/:analyze-text?api-version=2023-04-01 with the TextLanguageDetection task. The TextLanguageDetection task in the /language/:analyze-text endpoint is specifically designed to identify the language of a document and return the detected language code and confidence score. It supports the full range of Azure AI Language languages (currently 120+), making it the correct choice for routing multilingual articles.

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POST /language/translate?api-version=3.0

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POST /language/:analyze-text?api-version=2023-04-01 with the CustomEntityRecognition task

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POST /language/analyze-text/jobs?api-version=2023-04-01 with the TextSentimentAnalysis task

Question 7

You are deploying an LLM in Azure AI Foundry and need to monitor for toxicity and jailbreak attempts in real-time. Which feature should you enable?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the service designed to detect harmful content, including toxicity and jailbreak attempts, in real time for LLM applications. It provides classifiers for hate, violence, sexual, and self-harm content, plus Prompt Shields for jailbreak and indirect prompt injection detection. Enabling it allows pre- and post-processing moderation of prompts and completions.

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Azure Policy

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Microsoft Purview Compliance Manager

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Azure Monitor Log Analytics

Question 8

You are configuring an Azure AI Document Intelligence custom extraction pipeline that processes thousands of diverse medical forms daily. You notice that extraction confidence scores for certain hand-written fields are low. What is the most effective remediation step within the Document Intelligence workflow?

Accepted Answer

Add additional labeled training documents that specifically feature diverse handwriting samples for those low-confidence fields.. The most effective remediation for low-confidence extraction of handwritten fields in a custom Document Intelligence model is to provide more labeled training data that represents the variability causing the low confidence. Handwriting is highly variable, so adding diverse handwriting samples for the specific fields helps the model learn the patterns and improve accuracy. This directly addresses the root cause—insufficient training examples for those field variations—within the Document Intelligence workflow.

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Disable the OCR engine parameter in the API call to bypass text recognition steps.

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Decrease the model training iterations configuration setting to prevent overfitting on handwriting.

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Switch from a Custom Neural model to a Custom Template model.

Question 9

Your custom extraction model is failing to identify fields on a new version of a document. You have labeled 50 documents. What is the most likely solution?

Accepted Answer

Add more training documents with the new layout. When a custom extraction model fails on a new document layout, the most likely fix is to add more training documents that represent the new layout so the model can learn the additional variations. Custom models (especially neural) improve with more diverse, labeled examples covering the layouts they must handle; 50 documents of the old layout are insufficient for the new version. Retraining with representative samples is the standard remedy.

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Change the API version to v2.1

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Switch to a template model

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Increase the confidence threshold

Question 10

Your company's security policy requires that all Azure AI Services accounts disable public network access completely. However, internal applications hosted inside a specific Azure Virtual Network must still communicate with the AI services. How should you configure network access?

Accepted Answer

Disable public network access and configure a Private Endpoint for the resource.. To disable public network access while allowing internal applications in a specific Azure Virtual Network to communicate with Azure AI Services, you should disable public network access and configure a Private Endpoint. This creates a private IP address in the VNet for the service, enabling secure private connectivity.

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Configure an inbound Network Security Group (NSG) rule on the AI resource subnet.

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Keep public network access enabled and rely on API keys for security.

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Deploy a VPN gateway inside the Azure AI resource group.