AI-103 Implement Generative AI And Agentic Solutions Practice Test 7 — 15 Questions

Question 1

You are designing an advanced agentic solution in Azure AI Foundry that requires tool integration and state management. Which TWO practices should you implement to ensure robust agent reliability and security? (Choose two)

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

Accepted Answer

Implement human-in-the-loop confirmation steps before the agent executes high-impact or destructive transactional tool calls.. Validating tool inputs and implementing human-in-the-loop validation for sensitive actions are critical best practices for agent reliability and security.

Answer

Disable all logging and telemetry to minimize latency during agent execution loops.

Answer

Grant the agent unrestricted administrative permissions to all corporate databases to maximize autonomous problem-solving.

Answer

Rely solely on the model's internal memory to maintain multi-session conversation state without external persistence.

Question 2

When engineering prompts for Azure OpenAI models, you want to guide the model's output format and persona effectively. Which practice represents the most reliable prompt engineering technique?

Accepted Answer

Providing clear role definition, explicit formatting instructions, and few-shot examples within the system message.. Providing clear instructions, role definition, and formatting constraints along with few-shot examples yields the most consistent and accurate model behavior.

Answer

Embedding all context and instructions exclusively inside user messages without utilizing system messages.

Answer

Using overly complex, ambiguous phrasing to encourage the model to explore diverse latent spaces.

Answer

Relying entirely on zero-shot inference without system messages to maximize model creativity.

Question 3

When implementing Retrieval-Augmented Generation (RAG) using Azure AI Search and Azure OpenAI, which THREE strategies help optimize chunk retrieval relevance and reduce noise? (Choose three)

Accepted Answer

Configure hybrid search combining vector embeddings and keyword full-text search.. Hybrid search (combining vectors and keywords), semantic ranking, and appropriate chunk sizing are proven strategies for optimizing RAG relevance.

Answer

Store all corporate documents in a single unindexed raw text blob without metadata fields.

Answer

Set the embedding model dimensions to the lowest possible setting to maximize retrieval speed.

Question 4

You are implementing a grounded RAG solution in Azure AI Foundry. Users report that the model occasionally hallucinates answers even when relevant documents are retrieved by Azure AI Search. What is the most effective prompt engineering strategy to mitigate this grounding failure?

Accepted Answer

Add strict prompt instructions stating that the model must rely solely on the retrieved context chunks and explicitly output a refusal statement if the answer is not present.. Instructing the model explicitly to answer using only the provided context and to state 'I don't know' when information is missing heavily reduces hallucinations.

Answer

Remove the system message entirely so the model can freely blend internal training weights with search results.

Answer

Increase the temperature parameter to 1.5 to encourage creative synthesis of the retrieved data.

Answer

Disable chunking in Azure AI Search so the entire document is passed as a single massive token block.

Question 5

As part of responsible AI practices in Azure AI Foundry, which TWO actions should you perform to evaluate and mitigate model fairness and safety risks before deployment? (Choose two)

Accepted Answer

Run automated evaluations in Azure AI Foundry to measure metrics such as groundedness, relevance, and protected material presence.. Evaluating models using automated evaluation metrics (such as groundedness and relevance) and conducting red teaming are essential responsible AI practices.

Answer

Bypass security reviews if the application is intended for internal enterprise employees only.

Answer

Remove all system prompts to allow the model complete autonomy during evaluation runs.

Answer

Hardcode responses for all possible user inputs to eliminate model non-determinism entirely.

Question 6

You are building an agent in Azure AI Foundry that requires access to a private internal database. Which component should you configure to ensure the agent retrieves data securely without exposing the database endpoint publicly?

Accepted Answer

Azure Private Link and Managed Virtual Network. Managed Virtual Networks and Private Endpoints in Azure AI Foundry ensure that traffic between your agent and data sources remains within the Microsoft backbone network.

Answer

Public IP allowlisting

Answer

Entra ID Application Proxy

Answer

Shared Access Signatures (SAS)

Question 7

You are optimizing a RAG pipeline in Azure AI Foundry. The model is hallucinating when retrieving documents from a large corpus. Which strategy should you implement to improve grounding accuracy?

Accepted Answer

Implement a Cross-Encoder re-ranking step. Adding a re-ranking step after the initial search significantly improves the relevance of the context provided to the LLM.

Answer

Increase the temperature of the model to 1.0

Answer

Use a larger embedding model

Answer

Increase the chunk size to include the entire document

Question 8

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 integrated service for monitoring model inputs and outputs for harmful content.

Answer

Azure Policy

Answer

Microsoft Purview Compliance Manager

Answer

Azure Monitor Log Analytics

Question 9

You are designing an agentic workflow in Prompt Flow. Which TWO components are required to define a tool-based agent? (Choose two)

Accepted Answer

A LLM to act as the agent brain. Agents require a tool definition (to perform actions) and a base model (to reason).

Answer

A SQL Database

Answer

A Power Automate cloud flow

Answer

An Azure Container Registry

Question 10

You are building an agent in Azure AI Foundry that needs to retrieve information from a company SharePoint site. Which component should you implement to ground the agent?

Accepted Answer

Azure AI Search with a SharePoint connector. Azure AI Search with a SharePoint indexer is the standard way to ground agents in external document stores.

Answer

Blob Storage integration

Answer

Direct REST API call to SharePoint

Answer

Microsoft Graph Data Connect directly to the model

Question 11

You are designing an agentic workflow in Azure AI Foundry that needs to perform multiple tasks sequentially. Which THREE components are essential for defining the orchestration logic?

Accepted Answer

Prompt flow. Prompt flows, tool definitions, and control flow nodes are standard in Azure AI Foundry agent orchestration.

Answer

Azure Data Factory

Answer

Azure Logic Apps integration

Question 12

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. Private endpoints provide secure access to Azure AI resources within a virtual network.

Answer

Enable public internet access with IP firewall rules

Answer

Enable Managed Identity on the endpoint

Answer

Deploy a dedicated Azure Application Gateway

Question 13

You are implementing prompt engineering for a customer support agent. To minimize hallucinations when the model lacks information, which Prompt Flow technique is most effective?

Accepted Answer

Instructional grounding to only answer from context. Prompt engineering for RAG requires explicit instructions to use provided context and say 'I don't know' if missing.

Answer

Chain-of-thought prompting

Answer

Increasing the Top_P parameter

Answer

Few-shot prompting with varied examples

Question 14

You are building an agent in Azure AI Foundry using a prompt flow. You need to ensure that the agent only uses company-approved documentation for its responses. Which feature should you implement to meet this requirement?

Accepted Answer

Configure Grounding with data. Grounding with data is the primary mechanism in Azure AI Foundry to limit an agent's knowledge to specific data sources.

Answer

Enable prompt tuning

Answer

Implement system message constraints

Answer

Deploy a fine-tuned model

Question 15

You are designing an agentic workflow in Azure AI Foundry that requires the agent to call an external REST API. Which component should you use to define the tool interface?

Accepted Answer

Tool definition using a Python function. In prompt flow, tools are defined using Python functions or specific tool definitions that map the schema for the LLM to understand how to invoke the external function.

Answer

Azure AI Content Safety

Answer

LLM output parser

Answer

Flow input schema