AI-103 Implement Generative AI And Agentic Solutions Practice Test 9 — 15 Questions

Question 1

What is the primary purpose of a 'Vector Store' in a RAG-based agentic application?

Accepted Answer

To store embeddings for efficient semantic similarity search.. A vector store's primary purpose in a RAG-based agentic application is to persist embeddings and enable efficient semantic similarity search. When a user query arrives, it is embedded and compared against stored vectors using approximate nearest neighbor algorithms to retrieve the most relevant chunks. This retrieval step grounds the LLM's response in external knowledge, which is the core of RAG.

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To generate text responses.

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To log user conversations for analytics.

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To fine-tune the LLM.

Question 2

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.

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Increase the temperature to 1.0.

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Switch to a smaller model to increase speed.

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Decrease the max_tokens parameter.

Question 3

You are configuring a Prompt Flow to use a custom Python tool. Which configuration is required to ensure the environment has the necessary libraries?

Accepted Answer

Include them in a 'requirements.txt' file in the flow directory.. Prompt Flow resolves Python dependencies for a flow by reading a requirements.txt file located in the flow directory. This file lists the packages and versions needed by custom Python tools, and the Prompt Flow runtime installs them when the flow is executed or built. This ensures reproducibility across local development and cloud execution.

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Install them manually on the host machine.

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Add them to the System Prompt.

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Add them to the deployment manifest.

Question 4

When using Prompt Flow, you notice that your flow is failing at the 'aggregate' node. What is the most likely cause?

Accepted Answer

The input data from the previous node does not match the expected schema of the aggregate node.. The aggregate node in Prompt Flow is used to reduce or summarize outputs from a loop (e.g., computing an average or concatenating results). It expects a specific input schema—typically a list or a structured object produced by the preceding node. If the upstream node emits data that does not match that schema (wrong type, missing keys, or not a list), the aggregate node fails.

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The system prompt is too long.

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The search index is empty.

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The model is too busy.

Question 5

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 data configuration in the Agent settings is where you specify the approved data sources (e.g., Azure AI Search indexes, uploaded files, or connected knowledge bases) that the agent can use. By configuring only approved sources, you restrict the agent's knowledge to vetted content, ensuring it does not pull from unapproved external information. This is the correct control for data source governance in Azure AI Foundry agents.

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Prompt engineering for 'Do not use external info'.

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Model temperature control.

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Content Safety policy filters.

Question 6

Which Azure AI Foundry feature allows you to visualize the execution path of an agentic workflow?

Accepted Answer

Prompt Flow visual designer.. Prompt Flow's visual designer (DAG view) in Azure AI Foundry renders each node (LLM call, Python tool, retrieval step) and the directed edges between them, letting you trace the exact execution path of an agentic workflow. It also surfaces per-node inputs/outputs and token usage, which is why it is the designated tool for visualizing agent orchestration. The other options are single-purpose surfaces that do not expose flow topology.

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Azure OpenAI Playground.

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Azure AI Search dashboard.

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Model catalog view.

Question 7

You need to ensure that your agent's response is formatted as valid JSON. How should you approach this?

Accepted Answer

Define the JSON schema in the system prompt instructions.. Defining the JSON schema in the system prompt instructions guides the model to generate output that conforms to the specified structure. This approach leverages the model's instruction-following capabilities to produce valid JSON without external parsing. It is the most direct and reliable method for ensuring format compliance.

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Set the temperature to 0.5.

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Use an external library to convert text to JSON.

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Ask the user to request JSON.

Question 8

Your team is implementing 'Human-in-the-loop' (HITL) for an agent that performs account updates. Which mechanism should you use to pause the agent and wait for approval?

Accepted Answer

Use a Prompt Flow 'Pause' node or custom state management logic.. Human-in-the-loop requires the agent to suspend execution, persist its state, and resume only after an external approval signal. In Azure Prompt Flow this is implemented with a Pause node (or equivalent custom state management) that halts the flow and waits for an external event or API call to continue. This preserves the agent's context and prevents the account update from executing until a human explicitly approves it.

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Set a long timeout in the API call.

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Increase the model's 'stop_sequences'.

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Use an infinite loop in Python.

Question 9

You are testing an agent in the Azure AI Foundry playground. You observe that the agent answers questions about its internal prompt. What is the most effective way to prevent this?

Accepted Answer

Update the system prompt to explicitly refuse requests about its instructions.. The most effective and direct mitigation for an agent revealing its system prompt is to harden the system prompt itself with explicit instructions to refuse any request about its instructions, configuration, or internal prompt. This is a prompt-level guardrail that addresses the observed behavior without disabling functionality or removing knowledge sources.

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Delete the search index.

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Use a higher-parameter model.

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Disable the playground.

Question 10

What is the purpose of 'Batch Inference' in Azure AI Foundry?

Accepted Answer

To run multiple prompts against a large dataset for evaluation.. Batch Inference in Azure AI Foundry is designed to process a large dataset of prompts asynchronously against a deployed model, producing outputs for evaluation, scoring, or bulk generation. It is the mechanism used to run evaluation datasets through a model to compute metrics like groundedness, relevance, and coherence at scale. This makes it the correct answer for running multiple prompts against a large dataset for evaluation.

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To create a backup of the agent.

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To test the agent in real-time.

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To debug individual chat turns.

Question 11

You need to optimize the retrieval performance of your RAG agent. You find that the search engine returns too many irrelevant documents. What should you adjust?

Accepted Answer

Configure Hybrid Search settings in the search index.. Hybrid Search in Azure AI Search combines keyword (BM25) and vector similarity with Reciprocal Rank Fusion (RRF), and lets you tune parameters like k-nearest neighbors count, semantic ranker, and weighting to improve precision. When too many irrelevant documents are returned, adjusting hybrid search settings — such as enabling semantic reranking or reducing top-k — directly addresses retrieval relevance. The other options affect generation, not retrieval.

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Increase the model temperature.

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Change the model to a newer version.

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Reduce the system prompt length.

Question 12

You are experiencing high latency in your RAG agent. You confirm the model response time is fine, but the retrieval step is slow. What should you investigate?

Accepted Answer

The index retrieval configuration and semantic ranker parameters.. Since the LLM response time is confirmed fine, the bottleneck is in the retrieval pipeline. Index configuration (chunk size, vector dimensions, HNSW/IVF parameters) and semantic ranker settings (which add an L2 reranking pass) directly determine retrieval latency. Tuning these — e.g., reducing semantic ranker usage or adjusting efSearch/kNN — is the correct investigation path.

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The system prompt length.

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The content safety threshold.

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The model temperature.

Question 13

You want to add a new tool to an agent. Which step is required to link the tool to the model?

Accepted Answer

Register the tool in the agent configuration within Azure AI Foundry.. In Azure AI Foundry, tools are exposed to an agent through the agent's configuration — you register the tool (function, Azure AI Search, Bing grounding, OpenAPI, etc.) as a tool definition so the orchestrator can pass its schema to the model and invoke it during a run. The model only knows about tools that are declared in the agent/run configuration; there is no implicit discovery from the environment. Registering the tool in the agent configuration is therefore the required linking step.

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Hardcode the tool logic in the system prompt.

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Add the tool code to the global Python environment.

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Update the content safety policy.

Question 14

Which of the following is a core principle of Responsible AI in Azure AI Foundry?

Accepted Answer

Transparency and explainability.. Transparency and explainability is a core principle of Responsible AI in Azure AI Foundry, ensuring that AI systems are understandable, their decisions can be explained, and users can comprehend how outputs are generated. This aligns with Microsoft's Responsible AI principles: fairness, reliability & safety, privacy & security, inclusiveness, transparency, and accountability. The other options describe anti-patterns that violate Responsible AI.

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Bypassing all safety filters.

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Maximizing cost savings at any cost.

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Forcing the model to agree with the user.

Question 15

When evaluating an AI agent, what does the 'Coherence' metric measure?

Accepted Answer

The logical flow and clarity of the generated text.. The Coherence metric in AI agent evaluation measures the logical flow and clarity of the generated text. It assesses whether the response is well-structured, easy to follow, and logically consistent. This is one of the key quality metrics for generative AI outputs.

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The cost of the API call.

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The number of sources cited.

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The speed of the response.