AI-103 Practice Test 9 — 10 Questions

Question 1

You are implementing responsible AI practices in Azure AI Foundry for a public-facing chatbot. Which THREE safety evaluations or controls should you implement to protect users and the organization? (Choose THREE)

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

Accepted Answer

Protected material detection for source code and text copyright verification. Protected material detection, content safety filtering for harmful categories, and jailbreak/prompt shielding defenses are core responsible AI controls in Azure AI.

Answer

Azure SQL database transparent data encryption (TDE)

Answer

Azure DDoS network layer volumetric scrubbing

Question 2

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 3

An agent is prone to 'hallucinations' when responding to specific niche queries. What is the most effective way to reduce these hallucinations using RAG?

Accepted Answer

Set the grounding threshold to a higher value in the agent configuration.. Increasing the 'top_k' or relevance threshold ensures the retrieved data is highly specific to the query, reducing ambiguity.

Answer

Retrain the base LLM on the niche dataset.

Answer

Increase the system prompt length.

Answer

Lower the temperature setting to 0.0.

Question 4

You are using Azure AI Foundry to deploy an agent. You want to ensure that the agent only uses approved data sources. Which feature should you configure?

Accepted Answer

Grounding data configuration in the Agent settings.. Grounding with specific data sources restricts the agent to provided data, preventing the use of external knowledge.

Answer

Prompt engineering for 'Do not use external info'.

Answer

Model temperature control.

Answer

Content Safety policy filters.

Question 5

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.. Modifying the system prompt to explicitly define the inclusion/exclusion criteria for the summary is the most effective way to control agent output.

Answer

Lower the temperature.

Answer

Switch to a different model.

Answer

Add a 'stop_sequence' to the API call.

Question 6

You notice your Azure AI agent is producing offensive content. You need to configure a guardrail that intercepts the model output before it is sent to the user. Which service should you integrate?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the standard guardrail for filtering model output.

Answer

Azure AI Search Filters

Answer

Prompt Template Validation

Answer

Azure Monitor

Question 7

What is the primary benefit of using the Model Catalog in Azure AI Foundry?

Accepted Answer

Centralized discovery and deployment of models. The Model Catalog provides a centralized location to discover and deploy models.

Answer

Automated prompt engineering

Answer

Network security configuration

Answer

Automatic data labeling

Question 8

You are troubleshooting an Azure OpenAI deployment in Azure AI Foundry where agents are failing to complete tasks due to excessive timeouts. Which THREE diagnostic steps should you take? (Choose three.)

Accepted Answer

Check if the tokens-per-minute (TPM) quota is throttling requests, causing queued delays.. Troubleshooting agent timeouts involves examining token limits, network latency, and model complexity.

Answer

Double the model temperature to 2.0 to accelerate reasoning speed.

Answer

Delete all vector indices to force local memory processing.

Question 9

You are designing an agentic workflow where an agent needs to maintain conversational context and remember user preferences across multiple disparate sessions over several weeks. Which architectural component should you implement?

Accepted Answer

A persistent semantic memory store integrated with the agent's context pipeline.. Long-term memory implemented via vector stores or external databases allows agents to retrieve historical interactions across sessions.

Answer

Increasing the model context window to 1,000,000 tokens for every request.

Answer

Using a stateless function with zero memory persistence.

Answer

Storing the entire conversation history in a local client-side cookie.

Question 10

You are configuring an agent in Azure AI Foundry that needs to perform mathematical calculations accurately. Large language models often make arithmetic errors. How should you equip the agent to handle calculations reliably?

Accepted Answer

Provide a code interpreter tool so the model can write and execute Python code for calculations.. Giving the agent a code interpreter or calculator tool allows it to execute calculations deterministically rather than relying on internal probabilistic math.

Answer

Set the model temperature to 0.0 and ask it to guess the result.

Answer

Increase the presence penalty to force new tokens.

Answer

Instruct the model in the system prompt to double-check its arithmetic mentally.