AI-103 Practice Test 22 — 15 Questions

Question 1

An agent is performing poorly on multi-hop reasoning tasks. What architectural pattern should you implement to improve performance?

Accepted Answer

Use a Chain-of-Thought prompting strategy in the agent's system prompt.. Chain-of-Thought (CoT) prompting instructs the model to reason step by step before producing a final answer, which is the standard architectural pattern for improving multi-hop reasoning in agents. Embedding CoT guidance in the agent's system prompt encourages intermediate reasoning steps that connect multiple facts or tool calls.

Answer

Increase the temperature to 1.0.

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Switch to a smaller model to increase speed.

Answer

Decrease the max_tokens parameter.

Question 2

What is the purpose of a 'Resource Group' in the context of Azure AI solutions?

Accepted Answer

To act as a logical container for managing related resources.. A Resource Group in Azure is a logical container that holds related resources for an Azure solution, allowing you to manage, deploy, monitor, and track billing for resources as a group. It provides a way to organize resources by lifecycle, department, or project, and acts as the scope for RBAC and Azure Policy assignments. It does not inherently provide high availability, network security, or storage for model artifacts.

Answer

To provide high availability for the service.

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To enforce network firewall rules.

Answer

To store the model weights.

Question 3

You are optimizing an application that calls Azure AI Vision Read API. Which THREE techniques help reduce latency and improve overall throughput? (Choose three.)

Accepted Answer

Resize extremely large images to optimal dimensions while maintaining readability.. Option C is correct because the Read API's OCR accuracy and speed depend on image resolution; downscaling extremely large images to dimensions that keep text legible reduces upload size and server-side processing time, lowering latency. Option D is correct because the Read API is asynchronous: you submit an image, receive an Operation-Location URL, and must poll the GET result endpoint; using a sensible interval such as 1 second avoids throttling (HTTP 429) and wasted requests that aggressive busy-waiting causes, improving effective throughput. Option E is correct because placing the calling application and the Azure AI Vision resource in the same Azure region minimizes network round-trip time for both the initial POST and the subsequent polling GETs, directly reducing latency. Option A is not appropriate because the Read API accepts JPEG, PNG, BMP, PDF, and TIFF, and sending uncompressed RAW bitmap data would inflate payload size and transfer time rather than bypass meaningful decoding overhead. Option B is not appropriate because disabling SSL/TLS verification weakens security, does not meaningfully speed up TCP handshakes, and is not a supported optimization for the Azure AI Vision Read API.

Answer

Send uncompressed RAW bitmap data to bypass image decoding overhead.

Answer

Disable SSL/TLS verification to speed up TCP handshakes.

Question 4

Which of the following is a key advantage of using a 'prebuilt' model over a 'custom' model?

Accepted Answer

No training required. A prebuilt model is trained by the provider on a broad corpus of common document types, so it can be used immediately without any labeled training data or training cycle. This 'no training required' characteristic is its defining advantage — you get fast time-to-value and predictable behavior out of the box. Custom models, by contrast, require you to label documents and train, which costs time and effort.

Answer

Higher accuracy for niche documents

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Lower cost per page

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Ability to define custom fields

Question 5

Which TWO strategies are recommended for handling high-volume traffic to an Azure AI endpoint?

Accepted Answer

Enabling autoscaling on the endpoint.. Option A is correct because enabling autoscaling on the Azure AI endpoint dynamically adjusts the number of compute instances based on metrics such as CPU utilization, request rate, or token throughput, allowing the deployment to absorb high-volume traffic without manual intervention. Option E is correct because implementing a global load balancer such as Azure Front Door distributes incoming requests across multiple regional endpoints, provides layer-7 routing, TLS offload, caching, and failover, which collectively improve throughput and resilience under heavy load. Option B is incorrect because limiting access to a single user artificially restricts concurrency and does nothing to increase capacity for high-volume traffic. Option C is incorrect because hardcoding the IP address of a compute node bypasses DNS-based load balancing and creates a single point of failure, preventing traffic from being spread across instances. Option D is incorrect because the Free pricing tier enforces strict rate and quota limits, making it unsuitable for high-volume production traffic.

Answer

Limiting access to a single user.

Answer

Hardcoding the IP address of the compute node.

Answer

Using only the 'Free' pricing tier.

Question 6

You are configuring text summarization using the Azure AI Language REST API. Which THREE parameters or configuration options are associated with summarization requests? (Choose three.)

Accepted Answer

maxSentenceCount parameter. Option A (maxSentenceCount parameter) is correct because the extractive summarization task in the Azure AI Language REST API accepts a maxSentenceCount value in its parameters object to limit how many sentences the summary returns. Option C (Abstractive summarization task definition) is correct because the API supports an abstractiveSummarization task kind, which generates new natural-language summary text rather than selecting existing sentences. Option E (Extractive summarization task definition) is correct because the API also supports an extractiveSummarization task kind, which ranks and returns the most salient sentences from the input document. Option B (Audio sample rate frequency) is incorrect because sample rate is a speech/audio processing setting, not a text summarization parameter. Option D (Image resolution DPI setting) is incorrect because DPI relates to image processing and has no role in the text summarization REST API.

Answer

Audio sample rate frequency

Answer

Image resolution DPI setting

Question 7

You need to extract data from a document that includes handwritten signatures and printed text. Which capability is required?

Accepted Answer

Handwritten OCR (Read). Handwritten OCR (Read) is the Azure AI Vision capability that recognizes both printed and handwritten text in documents and images, making it the required feature when signatures and handwriting are present. The Read API returns extracted text with bounding boxes and confidence, supporting mixed print and handwriting. This directly satisfies the requirement to extract data from documents containing handwritten signatures and printed text.

Answer

Object Detection

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Custom Template model

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Form Recognizer 2.0 API

Question 8

Which tool provides a graphical user interface for labeling documents for custom models?

Accepted Answer

Document Intelligence Studio. Document Intelligence Studio is the web-based graphical interface provided by Azure that lets you visually label documents, define fields, train custom models, and test them against sample files. It is specifically designed for the labeling and project-management workflow of custom extraction and custom classification models, so it is the correct answer for a GUI-based labeling tool.

Answer

Azure CLI

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Azure Storage Explorer

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Visual Studio Code

Question 9

You are building a RAG solution and observe that the model retrieves the correct documents but fails to use the information accurately. What is the most effective approach to improve performance?

Accepted Answer

Refine the system prompt instructions. When retrieval is already returning the correct documents but the model misuses the information, the problem is in generation, not retrieval. Refining the system prompt to explicitly instruct the model how to use the retrieved context — cite it, extract the relevant passage, and avoid extrapolation — is the most direct and effective fix. This targets the actual failure mode.

Answer

Increase the vector index size

Answer

Use a smaller base model

Answer

Disable streaming response

Question 10

Your organization has strict compliance policies requiring all Azure AI Services endpoints to reject connections that do not use Transport Layer Security (TLS) version 1.3. Where can you enforce this minimum TLS version setting?

Accepted Answer

In the Configuration blade of the Azure AI resource in the Azure Portal. The minimum TLS version for an Azure AI Services resource is configured in the Configuration blade of the resource in the Azure Portal, where you can set the minimum inbound TLS version (e.g., 1.2 or 1.3). This setting enforces that connections using older TLS versions are rejected. It is a resource-level network security control, not an IAM or monitoring setting.

Answer

Within the Data residency tab of the Overview blade

Answer

In the Access control (IAM) settings under role assignments

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In Application Insights under the Availability blade

Question 11

Which THREE of the following are best practices for Responsible AI when using Generative AI solutions?

Accepted Answer

Implementing content safety filters. Option B is correct because implementing content safety filters (such as Azure AI Content Safety categories for hate, violence, sexual, and self-harm content) helps prevent generative models from producing or surfacing harmful, offensive, or disallowed output, which is a core Responsible AI safeguard. Option D is correct because maintaining human-in-the-loop oversight ensures that consequential AI-generated decisions or outputs are reviewed and validated by a person before action, reducing risk of unchecked errors or misuse. Option E is correct because providing transparency to users that they are interacting with an AI (for example, disclosing that content is AI-generated or that they are chatting with a bot) upholds the Responsible AI principles of transparency and accountability, helping users calibrate trust. Option A does not belong because removing all system instructions degrades safety guardrails and controllability rather than improving responsible use, and Option C does not belong because collecting all user PII for model training violates data minimization, privacy, and consent principles central to Responsible AI.

Answer

Removing all system instructions to maximize creativity

Answer

Collecting all user PII for model training

Question 12

Which TWO actions should you take to ensure your agentic solution is 'Responsible'?

Accepted Answer

Enable Content Safety filters for all inputs and outputs.. Option A is correct because enabling Content Safety filters on all inputs and outputs is a core responsible-AI control that detects and blocks harmful content categories (hate, violence, sexual, self-harm) before they reach or leave the agent, directly mitigating safety risks. Option C is correct because transparent disclosure that users are interacting with an AI agent upholds the responsibility principles of transparency and accountability, ensuring users are informed and can calibrate their trust appropriately. Option B is not correct because disabling all logging removes auditability and traceability, which are essential for monitoring misuse, debugging, and accountability, and privacy is better handled through data minimization and redaction rather than eliminating logs. Option D is not correct because raising temperature to 1.0 increases randomness and unpredictability, which undermines reliability and safety rather than improving responsibility. Option E is not correct because unrestricted public internet access expands the attack surface and risks unsafe or unverified content, so access should be governed by allowlists, grounding, and policy controls.

Answer

Disable all logging to protect user privacy.

Answer

Set the model temperature to 1.0 to increase diversity.

Answer

Allow the agent to access the public internet without restrictions.

Question 13

Which TWO of the following are valid methods to improve the quality of retrieved data in a RAG system?

Accepted Answer

Implementing a hybrid search strategy. Option B is correct because a hybrid search strategy combines dense vector similarity with sparse keyword retrieval (e.g., BM25), which improves recall and precision by matching both semantic meaning and exact terms, directly enhancing the quality of retrieved documents in a RAG pipeline. Option C is correct because optimizing document chunking strategies—such as choosing appropriate chunk sizes and overlaps or semantic chunking—ensures that retrieved passages are coherent and contextually complete, which raises the relevance and usefulness of the context passed to the LLM. Option A is not correct because reducing vector dimensions typically trades off retrieval accuracy for storage and speed, degrading rather than improving retrieval quality. Option D is not correct because chat history session length affects conversational context, not the retrieval mechanism or the quality of retrieved documents. Option E is not correct because increasing prompt tokens only changes how much context is fed to the model and does not improve the retrieval step itself.

Answer

Reducing the number of vector dimensions

Answer

Changing the chat history session length

Answer

Increasing the number of tokens in the prompt

Question 14

When designing a RAG-based agent, which THREE factors directly influence the quality of the retrieved information?

Accepted Answer

The system prompt's instructions on retrieval.. Option A is correct because the system prompt's retrieval instructions directly steer when and how the agent invokes the retriever, what queries it formulates, and how it filters or re-ranks results, all of which shape the relevance of retrieved context. Option B is correct because hybrid search combines lexical keyword matching (e.g., BM25) with dense vector similarity, improving recall for exact terms and semantic matches alike, which raises retrieval quality. Option C is correct because chunk granularity and document quality determine what units are embedded and indexed; poorly sized or noisy chunks degrade embedding fidelity and produce irrelevant or incomplete context. Option D does not belong because training-update frequency affects the base model's parametric knowledge, not the retrieval pipeline that supplies external context in RAG. Option E does not belong because temperature only affects sampling randomness during generation and has no direct effect on which documents are retrieved.

Answer

The frequency of the model's training updates.

Answer

The model's temperature setting.

Question 15

You are deploying an enterprise RAG application in Azure AI Foundry. You need to ensure data security and compliance across all components. Which THREE security practices should you implement? (Choose THREE)

Accepted Answer

Use Managed Identities with Azure RBAC for service-to-service authentication. Option A is correct because Managed Identities combined with Azure RBAC eliminate the need to embed credentials in code or configuration, allowing the RAG application's components (e.g., Azure OpenAI, Azure AI Search, Storage) to authenticate to each other through Microsoft Entra ID tokens with least-privilege role assignments. Option C is correct because Azure Private Endpoints place Azure OpenAI and Azure AI Search on a private IP within your virtual network, removing exposure to the public internet and satisfying network isolation and data-exfiltration compliance requirements. Option E is correct because Customer-Managed Keys stored in Azure Key Vault give you control over the encryption keys protecting data at rest in Azure OpenAI, Azure AI Search, and storage accounts, which is a common regulatory requirement for key ownership and revocation. Option B is not correct because granting anonymous public network access on AI service firewalls exposes the services to unauthenticated internet traffic and violates the security and compliance goals of the scenario. Option D is not correct because storing API keys in plain text in public GitHub repositories leaks credentials and is a severe security anti-pattern that contradicts every stated requirement.

Answer

Grant anonymous public network access on all AI service firewalls

Answer

Store API keys in plain text configuration files committed to public GitHub repositories