AI-103 Implement Generative AI And Agentic Solutions Practice Test 8 — 15 Questions

Question 1

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.. Option B is correct because Azure AI Foundry's automated evaluation flows let you quantify safety and quality metrics—groundedness, relevance, coherence, fluency, and protected-material/hate-and-unfairness scores—so you can objectively assess fairness and safety risks before deployment. Option D is correct because adversarial testing (red teaming) in Azure AI Foundry systematically probes the model with prompt injections, jailbreak attempts, and other malicious inputs to surface unintended behaviors and safety gaps that must be mitigated prior to release. Option A is wrong because internal-only audiences do not exempt an application from responsible AI security and safety reviews; risk assessments apply regardless of user population. Option C is wrong because removing system prompts strips guardrails and grounding instructions, increasing unsafe or off-topic outputs rather than enabling meaningful evaluation. Option E is wrong because hardcoding responses is not a fairness/safety evaluation technique and would defeat the purpose of testing a generative model's behavior.

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 2

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. Azure Private Link combined with a Managed Virtual Network ensures that the agent's traffic to the private internal database traverses the Azure backbone via a private endpoint, never exposing the database to the public internet. This is the standard secure pattern for private data access in Azure AI Foundry. It provides network-level isolation and prevents data exfiltration.

Answer

Public IP allowlisting

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Entra ID Application Proxy

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Shared Access Signatures (SAS)

Question 3

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. Cross-Encoder re-ranking is correct because it jointly encodes the query and each candidate document, producing a far more accurate relevance score than the bi-encoder cosine similarity used in initial vector retrieval. This precision filtering removes semantically similar but contextually irrelevant chunks before they reach the LLM, directly reducing hallucination caused by noisy or off-topic grounding context. In Azure AI Foundry, this is typically implemented via the semantic ranker or a custom re-ranking stage in the RAG orchestration pipeline.

Answer

Increase the temperature of the model to 1.0

Answer

Use a larger embedding model

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Increase the chunk size to include the entire document

Question 4

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.

Answer

Azure Policy

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Microsoft Purview Compliance Manager

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Azure Monitor Log Analytics

Question 5

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. Option A is correct because a tool-based agent in Prompt Flow requires an LLM to serve as the agent's reasoning brain, deciding which tools to invoke and how to interpret their outputs. Option B is correct because the agent must have tool definitions—provided as an OpenAPI specification or a Python function—so the LLM knows the available actions, their parameters, and how to call them. Option C is incorrect because a SQL Database is merely a possible backend a tool might query; it is not a required component of the agent definition. Option D is incorrect because a Power Automate cloud flow is an optional integration, not a mandatory element for defining a tool-based agent. Option E is incorrect because Azure Container Registry is only relevant for storing container images used in custom environments, not for defining the agent itself.

Answer

A SQL Database

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A Power Automate cloud flow

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An Azure Container Registry

Question 6

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 connector (via the SharePoint indexer or the newer SharePoint ingestion pipeline) is the supported, first-party way to ground an Azure AI Foundry agent on SharePoint content. The connector crawls SharePoint sites, extracts documents, chunks and indexes them, and exposes them through a retriever that the agent can call as a knowledge tool. This gives the agent permission-aware, up-to-date retrieval without custom code.

Answer

Blob Storage integration

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Direct REST API call to SharePoint

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Microsoft Graph Data Connect directly to the model

Question 7

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.

Answer

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 8

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. Instructional grounding — explicitly instructing the model to answer only from the provided context and to say it doesn't know otherwise — is the most direct and effective prompt-engineering technique to minimize hallucinations when the model lacks information. It constrains the model's generation to the retrieved context rather than its parametric memory.

Answer

Chain-of-thought prompting

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Increasing the Top_P parameter

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Few-shot prompting with varied examples

Question 9

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 (e.g., Azure AI Search index, Azure Blob, or other approved data sources) constrains the model's responses to a curated knowledge base, ensuring the agent only answers from company-approved documentation. This is the standard RAG pattern in Azure AI Foundry and directly satisfies the 'only approved documentation' requirement.

Answer

Enable prompt tuning

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Implement system message constraints

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Deploy a fine-tuned model

Question 10

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 Azure AI Foundry agentic workflows, tools are defined as callable functions with a name, description, and parameter schema; a Python function definition (often decorated or registered as a tool) is the standard way to expose an external REST API to the agent. The model then emits structured arguments that invoke that function. This is the correct component for defining the tool interface.

Answer

Azure AI Content Safety

Answer

LLM output parser

Answer

Flow input schema

Question 11

You are auditing a generative AI application for responsible AI compliance. Which THREE practices should you implement to mitigate risks? (Choose 3)

Accepted Answer

Configure system messages to explicitly define boundaries. Option C is correct because configuring system messages to explicitly define boundaries constrains the model's behavior, scope, and tone, helping prevent prompt injection, off-topic responses, and harmful or non-compliant outputs. Option D is correct because enabling Azure AI Content Safety filtering applies classifiers for hate, violence, sexual, and self-harm content (plus prompt shields and groundedness detection), directly reducing the risk of harmful or unsafe generated content. Option E is correct because implementing manual review cycles for high-impact outputs adds human oversight, which is a core responsible AI requirement for consequential decisions where automated errors could cause real harm. Option A is not appropriate because storing user PII in plain text logs violates privacy and data-protection principles and increases breach exposure rather than mitigating risk. Option B is not appropriate because raising the temperature to 1.0 increases randomness and creativity, which raises the likelihood of hallucinated, inconsistent, or unsafe outputs instead of reducing risk.

Answer

Store all user PII in plain text logs

Answer

Increase the temperature of the model to 1.0

Question 12

You are building an agentic workflow in Azure AI Foundry. You need the agent to perform a multi-step task: search a database, summarize results, and then send an email. Which component is best suited to manage this orchestration?

Accepted Answer

Azure AI Agent Service.. Azure AI Agent Service is purpose-built to orchestrate multi-step agentic workflows, including tool/function calling, planning, and chaining actions like database queries, summarization, and email dispatch. It manages conversation state, tool invocation, and step sequencing natively, which is exactly what this scenario requires.

Answer

Azure OpenAI fine-tuning service.

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Azure AI Search indexer.

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Azure AI Content Safety API.

Question 13

You are configuring an agent that must call a REST API to get current stock prices. Where must you define this integration to ensure the agent can reliably call the tool?

Accepted Answer

In the Agent Tool Definition.. In Azure AI Foundry, tools (including REST API calls, function calling, and OpenAPI-defined tools) are declared in the Agent Tool Definition, which the agent runtime uses to decide when and how to invoke the tool. This is where you register the API schema, authentication, and parameters so the agent can reliably call the stock price endpoint.

Answer

In the Azure OpenAI deployment configuration.

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In the Search index field mapping.

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In the System Prompt.

Question 14

You want to monitor the cost and latency of your agentic application in production. Which service should you integrate with your Azure AI Foundry project?

Accepted Answer

Azure Application Insights.. Azure Application Insights is the Application Performance Monitoring (APM) service that Azure AI Foundry integrates with to capture traces, latency, token usage, and cost telemetry for agentic applications. It provides the dashboards, query language (KQL), and alerting needed to monitor production behavior.

Answer

Azure OpenAI Studio.

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Azure AI Search.

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Azure AI Content Safety.

Question 15

A user asks a question that triggers a 'Blocked' response from Azure AI Content Safety, even though the query seems harmless. What is the best step to investigate this?

Accepted Answer

Check the Content Safety logs in the Azure AI Foundry portal.. When Azure AI Content Safety returns a 'Blocked' result for a seemingly benign prompt, the correct diagnostic step is to inspect the Content Safety logs and severity scores in the Azure AI Foundry portal. These logs show which category (hate, violence, sexual, self-harm) and which severity level triggered the block, letting you determine whether a threshold is misconfigured or a specific phrase is being flagged. This is a read-only investigation that preserves the safety posture.

Answer

Change the system prompt to ignore safety.

Answer

Increase the model temperature.

Answer

Disable Content Safety for the deployment.