AI-103 Practice Test 15 — 15 Questions

Question 1

Your application processes receipt images using Azure AI Vision Read API. You need to extract text specifically from tables within the receipts. Which service is better suited for advanced table structure and key-value pair extraction compared to base Azure AI Vision Read?

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for structured document extraction, including tables, key-value pairs, and layout analysis. Its prebuilt receipt model and general document/layout models return structured JSON with table cells, row/column spans, and semantic key-value associations that the base Read API does not provide. The Read API only returns raw text lines and words with bounding boxes, without table structure or field semantics.

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Azure AI Face Service

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Custom Vision Service

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Azure AI Content Safety

Question 2

You are deploying an Azure OpenAI model in Azure AI Foundry and want to evaluate its performance and safety against a validation dataset before moving to production. Which tool should you use?

Accepted Answer

Model evaluation in Azure AI Foundry. Model evaluation in Azure AI Foundry provides a built-in evaluation framework where you can run your deployed model against a validation dataset and compute metrics like groundedness, relevance, fluency, coherence, and safety metrics (hate, violence, sexual, self-harm). It is the native tool for pre-production performance and safety assessment. Azure Monitor and Advisor serve different purposes (observability and cost), and API Management is for API governance.

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Azure Monitor Log Analytics query packs

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Azure Advisor cost recommendations

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Azure API Management developer portal

Question 3

You are designing a solution that processes user reviews. You want to extract key terms such as 'battery life', 'screen quality', and 'customer service' from the text. Which Azure AI Language feature is best suited for this?

Accepted Answer

Key phrase extraction. Key phrase extraction is designed to identify the main talking points or salient terms in text, which is exactly what is needed to surface terms like 'battery life' and 'screen quality' from reviews. It returns a ranked list of key phrases without requiring training. This makes it the best fit for extracting review topics.

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Sentiment analysis

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PII detection

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Language detection

Question 4

You are testing an agent in the Azure AI Foundry playground. You observe that the agent answers questions about its internal prompt. What is the most effective way to prevent this?

Accepted Answer

Update the system prompt to explicitly refuse requests about its instructions.. The most effective and direct mitigation for an agent revealing its system prompt is to harden the system prompt itself with explicit instructions to refuse any request about its instructions, configuration, or internal prompt. This is a prompt-level guardrail that addresses the observed behavior without disabling functionality or removing knowledge sources.

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Delete the search index.

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Use a higher-parameter model.

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Disable the playground.

Question 5

You are integrating Azure AI Vision Read API into a high-throughput workflow. You receive an HTTP 202 Accepted response. What action should your application take next?

Accepted Answer

Poll the URL specified in the Operation-Location response header until the status is succeeded.. An HTTP 202 Accepted response from the Azure AI Vision Read API indicates that the request has been accepted for asynchronous processing, and the response includes an Operation-Location header containing a URL to poll for the result. The application must poll that URL (typically with a GET request) until the status field returns 'succeeded', at which point the extracted text is available in the response body. This is the documented asynchronous pattern for the Read API.

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Switch to synchronous Read API endpoint.

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Parse the response body directly for the extracted text results.

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Immediately retry the POST request with an increased timeout value.

Question 6

You are using Azure AI Language custom text classification. You have uploaded your training dataset and trained a model. You notice that your evaluation metrics show low precision. What does low precision indicate about your model?

Accepted Answer

Many of the items predicted as a specific class actually belonged to a different class.. Low precision indicates that when the model predicts a class, it is often incorrect—meaning many predicted positives are false positives. In other words, items predicted as a specific class actually belong to a different class. This is the definition of low precision.

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The model failed to find many actual positive instances in the dataset.

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The latency of the REST API calls is too high.

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The training dataset contained fewer than 5 documents per class.

Question 7

You need to determine the primary dominant colors and whether an image is predominantly black-and-white using Azure AI Vision. Which visual feature parameter provides this information?

Accepted Answer

features=color. The features=color parameter in Azure AI Vision returns color-related analysis, including the dominant foreground and background colors and whether the image is black-and-white (isBWImg). This directly provides the dominant colors and monochrome determination requested. Other features return captions, people detection, or tags, not color metadata.

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features=denseCaptions

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features=people

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features=tags

Question 8

You are configuring a Custom Vision project to classify images of plant diseases. You notice that some image files uploaded via the API are rejected because they exceed the maximum allowed file size. What is the maximum image file size supported by Custom Vision for training?

Accepted Answer

6 MB. Custom Vision enforces a maximum training image file size of 6 MB per image. Images larger than this are rejected by the API during upload, regardless of their dimensions or format. This limit applies to training images, not prediction images, which have their own constraints.

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2 GB

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500 KB

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50 MB

Question 9

Your enterprise architecture team is reviewing the security posture of an Azure AI deployment that handles sensitive financial documents. Which TWO actions should you implement to secure data in transit and at rest? (Choose two.)

Accepted Answer

Enforce a minimum TLS version of 1.2 or higher for all API requests to secure data in transit.. Option A is correct because enforcing a minimum TLS version of 1.2 (or higher) on all API requests protects sensitive financial documents while they travel over the network, preventing downgrade attacks and use of deprecated protocols such as TLS 1.0/1.1. Option B is correct because configuring customer-managed keys (CMK) through Azure Key Vault gives the organization control over the encryption keys used for data at rest, satisfying compliance and key-ownership requirements for sensitive financial data. Option C is not appropriate because granting 'Contributor' to all developer accounts violates least privilege and does not itself secure data in transit or at rest; key rotation should be handled through Key Vault access policies or RBAC scoped narrowly. Option D is wrong because disabling diagnostic logging reduces auditability and incident response rather than protecting data, and logging can be configured to avoid storing sensitive content. Option E is wrong because relying exclusively on Microsoft-managed keys removes customer control over key lifecycle and does not meet the requirement to integrate Azure Key Vault for encryption at rest.

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Grant 'Contributor' permissions to all developer accounts to simplify key rotation.

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Disable diagnostic logging to prevent sensitive document text from being stored in Log Analytics.

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Use Microsoft-managed keys exclusively without integrating Azure Key Vault.

Question 10

You are building an Azure AI Document Intelligence solution to process complex financial statements. Due to varying layouts across different financial institutions, you decide to use a custom neural model instead of a custom template model. What is a key architectural advantage of the custom neural model in this scenario?

Accepted Answer

It supports cross-page tables and varying visual layouts without requiring strict coordinate alignment.. The custom neural model in Azure AI Document Intelligence is designed to handle complex, variable layouts, including cross-page tables and differing visual structures, without requiring strict coordinate alignment. This makes it ideal for financial statements from different institutions where layouts vary significantly. It leverages deep learning to understand document structure and extract fields accurately even when the position of data changes.

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It requires significantly fewer training documents than a template model to achieve high accuracy.

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It can be trained entirely unsupervised without requiring any human-labeled training documents.

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It can extract data from scanned documents without running Optical Character Recognition (OCR) first.

Question 11

You are designing a high-throughput, low-latency architecture for an Azure OpenAI application. Clients are distributed globally across North America, Europe, and Asia. To ensure minimal latency and high availability, you decide to deploy multiple Azure OpenAI instances in different regions. How should you route incoming client requests to the nearest optimal region?

Accepted Answer

Azure Front Door with latency-based routing. Azure Front Door is a global, scalable entry point that uses Microsoft's global edge network to route client requests based on latency. It continuously measures latency to the origin and directs traffic to the lowest-latency healthy backend, which is ideal for globally distributed Azure OpenAI instances. This ensures minimal latency and high availability.

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Azure ExpressRoute global reach

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Azure Bastion global routing profile

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Azure Local Traffic Manager (LTM)

Question 12

You are optimizing performance for a real-time video analytics pipeline that sends video frames to Azure AI Vision. Due to network bandwidth constraints, you need to reduce payload sizes while maintaining high OCR accuracy. What is the recommended approach?

Accepted Answer

Encode frames as optimized JPEG images with appropriate resolution scaling, ensuring text remains legible.. Optimized JPEG encoding with resolution scaling reduces payload size while preserving text legibility, which is the key requirement for OCR accuracy. JPEG's lossy compression is well-suited for photographic frames, and scaling to the minimum resolution that keeps text readable balances bandwidth and accuracy. This is the recommended approach for bandwidth-constrained real-time OCR pipelines.

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Convert frames to uncompressed 32-bit BMP format.

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Extract audio tracks instead of video frames.

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Send raw YUV byte arrays directly without image containerization.

Question 13

Your application analyzes high-resolution engineering blueprints using Azure AI Vision Read API. Users report that small text annotations are occasionally missed. How can you optimize the input image or API configuration to improve recognition of small text?

Accepted Answer

Ensure the image is submitted at full resolution without lossy compression, and verify contrast.. The Read API's OCR accuracy for small text depends heavily on the effective pixel density of the characters. Submitting the blueprint at full resolution without lossy compression preserves the fine strokes of small annotations, and verifying sufficient contrast ensures the OCR engine can distinguish text from the background. Lossy compression (JPEG) introduces artifacts that blur small glyphs, directly causing missed detections.

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Convert the blueprint to grayscale and reduce color depth to 1-bit monochrome.

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Switch from Read API to Tagging API.

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Downsample the image resolution to under 1000x1000 pixels before submission.

Question 14

You are designing a RAG solution using Azure AI Search and Azure OpenAI. Users report that when querying technical documentation, the system misses highly relevant paragraphs because the exact keywords do not match the user's conversational query. How should you configure Azure AI Search to improve retrieval accuracy?

Accepted Answer

Enable semantic ranking on the Azure AI Search index and use hybrid search combining vector embeddings and keyword BM25.. Enabling semantic ranking and using hybrid search (combining vector embeddings and BM25 keyword search) improves retrieval accuracy by leveraging both semantic understanding and keyword matching. This addresses the issue of conversational queries missing exact keywords.

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Switch the index analyzer to a standard whitespace analyzer to prevent stemming issues.

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Increase the number of retrieved documents (top_k) from 3 to 20 without changing the query type.

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Convert all documents to uppercase before indexing to ensure case-insensitive matching.

Question 15

You are building an agent that needs to execute Python code safely to calculate financial projections requested by users. Which tool in Azure AI Agent Service should you enable to allow the agent to write and run code in an isolated environment?

Accepted Answer

Code Interpreter tool. The Code Interpreter tool in Azure AI Agent Service provides a managed, sandboxed Python execution environment where the agent can write and run code to perform calculations such as financial projections. It handles file uploads, execution, and output retrieval without exposing your infrastructure. This is the native tool designed for safe code execution within an agent.

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Azure Container Instances dynamic scaling group

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Azure Functions webhook integration

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Azure AI Search vector embedding tool