AI-103 Implement Generative AI And Agentic Solutions Practice Test 10 — 15 Questions

Question 1

How do you handle sensitive user data when using an Azure AI Agent?

Accepted Answer

Use Data Masking/PII filtering before the data reaches the agent.. Responsible handling of sensitive data in Azure AI Agents requires filtering or masking PII before it reaches the agent, so that personal data is not sent to the model, logged in prompts, or persisted in agent threads. Data masking/PII filtering (for example via Azure AI Language PII detection or a pre-processing step in Prompt Flow) enforces this at the boundary. This aligns with privacy-by-design and data-minimization principles required for Responsible AI compliance.

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Send all data to the model; the model will ignore it.

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Increase the model parameter size.

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Store user PII in the vector index.

Question 2

You need to ensure that your agent is compliant with regional data residency requirements. Where should you check this?

Accepted Answer

In the Azure AI Foundry resource's region settings.. Data residency for Azure AI Foundry is governed by the region of the Azure AI Foundry resource (and its dependent resources such as the model deployment and storage). To verify compliance with regional data residency requirements, you check the resource's region settings, because that determines where data is stored and processed. Content safety policies, prompt flow runtime, and model configuration do not control geographic placement.

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In the Prompt Flow runtime.

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In the Content Safety policy.

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In the model configuration settings.

Question 3

You are using Prompt Flow to iterate on an agent. What is the benefit of using 'Environment Variables' in your flow?

Accepted Answer

To manage configuration settings like API keys and endpoints across environments.. Environment variables in Prompt Flow let you externalize configuration such as API keys, endpoints, and deployment names so the same flow can run against different environments (dev, test, prod) without code changes. This is the standard twelve-factor app pattern and is the primary benefit of using environment variables in a flow.

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To reduce the model response time.

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To store large data files.

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To increase the prompt length.

Question 4

An agent is designed to summarize meeting transcripts but keeps including internal meeting notes in the final summary. What is the most effective way to address this?

Accepted Answer

Update the system prompt to clarify which sections of the transcript to include.. The system prompt is the primary control surface for defining an agent's scope, tone, and what content it should include or exclude. When an agent incorrectly includes internal meeting notes, the root cause is usually ambiguous instructions about which transcript sections are in-scope, so clarifying the inclusion/exclusion rules in the system prompt directly fixes the behavior. This is a prompt engineering fix, not a model parameter fix, because the issue is about instruction-following scope rather than randomness or creativity.

Answer

Lower the temperature.

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Switch to a different model.

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Add a 'stop_sequence' to the API call.

Question 5

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.

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Set the model temperature to 1.0 to increase diversity.

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Allow the agent to access the public internet without restrictions.

Question 6

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 7

You are configuring tool integration for an agent. Which THREE aspects must be clearly defined in the tool definition for the agent to correctly use it?

Accepted Answer

The tool's description, explaining what it does and when to call it.. Option A is correct because the tool's description tells the agent what the tool does and, critically, when it should be invoked, which guides the model's decision to call it. Option D is correct because the tool's name is the identifier the agent uses to reference and invoke the specific function in its tool-call output. Option E is correct because the JSON schema for the function arguments defines the exact parameter names, types, and required fields the agent must supply, ensuring valid, parseable arguments. Option B is not required in the tool definition; rate limiting is an operational/policy concern handled outside the tool's schema. Option C is wrong because hardcoding authentication credentials in the tool definition is insecure and unnecessary—credentials should be managed via secure configuration, not embedded in the tool spec.

Answer

The total number of requests allowed per hour.

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The authentication credentials (hardcoded).

Question 8

Which THREE types of tools can be integrated into an Azure AI Agent?

Accepted Answer

Azure AI Search indexes.. Azure AI Agents support tool integration through several mechanisms, and Azure AI Search indexes (A) are correct because they can be attached as knowledge/retrieval tools, enabling the agent to ground responses in indexed enterprise data via the Azure AI Search service. Custom Python functions (B) are correct because agents can call user-defined functions (function calling/tool use) to execute custom logic, calculations, or data processing at runtime. External REST APIs (D) are correct because agents can invoke OpenAPI-specified or HTTP-based tools to reach external services and return results into the conversation. Hard drive file systems (C) are not a supported agent tool type, since agents run as cloud services without direct access to a user's local disk, and operating system kernel drivers (E) are not integrable tools because agents operate at the application/service layer, not the OS kernel level.

Answer

Hard drive file systems (local).

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Operating system kernel drivers.

Question 9

Which TWO components are essential for creating an effective system prompt?

Accepted Answer

A clear definition of the agent's persona and objective.. Option A is correct because an effective system prompt must establish the agent's persona and objective, giving the model a clear role, tone, and goal that shape all subsequent responses. Option D is correct because system prompts need explicit constraints and instructions for edge cases or disallowed topics, which set behavioral boundaries and safety guardrails for the agent. Options B, C, and E are not essential: authorized-user lists are typically enforced by authentication and access-control systems rather than the prompt, the base model's full training dataset is not something the prompt author supplies, and data center hardware details are irrelevant to prompt design.

Answer

A list of all users authorized to use the agent.

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The full training dataset of the base model.

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A list of all hardware components in the data center.

Question 10

Which TWO strategies are recommended to prevent 'Model Over-Reliance' or 'Automation Bias' in agents?

Accepted Answer

Include a clear disclaimer that the agent is an AI and may provide incorrect information.. Option A is correct because a clear disclaimer that the agent is an AI and may provide incorrect information sets appropriate user expectations and prompts critical evaluation of the agent's output, directly countering automation bias (the tendency to over-trust automated systems). Option C is correct because providing citations and links back to original source documents lets users verify claims against authoritative sources, which reduces blind reliance on the model and supports grounded, evidence-based decision-making. Options B, D, and E do not belong: disabling 'I don't know' responses encourages the agent to fabricate answers instead of expressing uncertainty, removing human oversight eliminates the human-in-the-loop check that mitigates over-reliance, and using the highest available model temperature increases randomness and hallucination risk rather than improving trustworthiness.

Answer

Disable the ability for the agent to say 'I don't know'.

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Remove all human oversight from the process.

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Use the highest available model temperature.

Question 11

Which THREE settings in the Azure AI Foundry playground can directly affect the 'creativity' of an agent's response?

Accepted Answer

Temperature.. Temperature (A) is correct because it scales the randomness of the model's token probability distribution—higher values like 1.0+ flatten the distribution and produce more varied, 'creative' outputs, while lower values sharpen it toward deterministic responses. Top-P (C), or nucleus sampling, is correct because it restricts sampling to the smallest set of tokens whose cumulative probability exceeds P; raising P (e.g., 0.9–1.0) admits more candidate tokens and increases output diversity. Frequency Penalty (D) is correct because it penalizes tokens proportionally to how often they have already appeared, discouraging repetition and thereby pushing the model toward more varied, novel phrasing. The search index threshold (B) governs retrieval grounding—how many or how similar documents must be before they are surfaced—so it affects factual grounding, not generative creativity. The system prompt length (E) is not a creativity parameter; prompt content and length shape behavior and context, but they do not directly tune the sampling distribution the way temperature, top-P, and frequency penalty do.

Answer

The search index threshold.

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The system prompt length.

Question 12

You are developing an Azure AI Search index for a RAG application. You need to ensure that the search results include semantic relevance scores to improve the quality of the model's grounding. Which feature must you enable?

Accepted Answer

Enable Semantic Ranker on the search service. Semantic Ranker is the Azure AI Search feature that applies a Microsoft-hosted language model to re-rank the top results from an initial retrieval pass, producing semantic relevance scores (semantic captions and answers) that improve grounding quality for RAG. Enabling it on the search service (Standard tier or higher) and then configuring it on the index/query is what surfaces the semantic score. Without Semantic Ranker, results are ranked only by BM25 or vector similarity, not by deep semantic understanding.

Answer

Use Custom Analyzers

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Enable Knowledge Store

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Enable Hybrid Search with vectorization

Question 13

You are building an agent in Azure AI Foundry using a prompt flow. You need to ensure that the agent only accesses specific internal documents and avoids hallucinating outside the provided context. Which grounding technique should you implement?

Accepted Answer

Implement grounding with data using Azure AI Search. Grounding with data using Azure AI Search connects the agent to a specific indexed document set, so responses are generated only from retrieved content. This directly prevents hallucination by constraining the model's context to the internal documents. System messages alone cannot guarantee the model will ignore its pretrained knowledge.

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Use a system message to forbid outside information

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Enable prompt caching

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Increase the temperature parameter to 1.0

Question 14

When designing an agentic workflow in Azure AI Foundry, which component is used to manage the execution order and decision-making logic of multiple agents?

Accepted Answer

Orchestrator. The orchestrator component in Azure AI Foundry manages the execution order and decision-making logic of multiple agents. It coordinates tasks, routes requests, and determines which agent acts next based on the workflow. This is essential for building complex agentic systems.

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Vector Index

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Model Catalog

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Prompt Template

Question 15

You are implementing a tool call in a Prompt Flow. The tool needs to interact with an external REST API. What is the recommended way to manage the API credentials securely?

Accepted Answer

Use an Azure AI Foundry Connection of type Custom. Azure AI Foundry Connections of type Custom are the recommended way to store and manage credentials for external services such as REST APIs. A Custom connection securely stores the API key or token in the Foundry workspace, and the Prompt Flow tool references the connection by name, so credentials are never exposed in code or prompts. This integrates with Azure RBAC and Key Vault for secure secret storage.

Answer

Pass the key as a parameter in the input prompt

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Hardcode the API key in the Prompt Flow code

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Use an environment variable in the flow definition