AI-103 Implement Generative AI And Agentic Solutions Practice Test 2 — 15 Questions

Question 1

You are building a RAG application where documents are updated frequently. You need to ensure that the Azure AI Search index reflects document deletions and updates in near real-time without performing full re-indexing. How should you design the ingestion pipeline?

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

Accepted Answer

Configure Azure AI Search indexers with change tracking and scheduled incremental synchronization. Using incremental indexers with change tracking (such as integrated data change detection or custom push APIs with soft-delete metadata) allows efficient updates.

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Increase the Azure OpenAI model deployment concurrency quota

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Enable semantic caching on the OpenAI endpoint

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Delete the entire search service and recreate the index daily using a batch script

Question 2

You are developing a generative AI application that uses Azure OpenAI. You want to track token consumption per user department for chargeback purposes. Which feature should you enable and utilize?

Accepted Answer

Azure Monitor metrics and diagnostic logs with custom metadata tags. Enabling Azure OpenAI usage metrics combined with custom headers or Microsoft Entra ID claims allows tracking and auditing token consumption by department.

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Azure Cost Management budgeting alerts only

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Azure AI Content Safety severity reports

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Azure Advisor security recommendations

Question 3

You are designing a generative AI solution in Azure AI Foundry. You need to ensure that user data sent to Azure OpenAI is not used to train Microsoft's foundational models. What is Microsoft's default data privacy policy for Azure OpenAI?

Accepted Answer

Customer data is not used to train foundation models by default. Microsoft guarantees that customer data and prompts sent to Azure OpenAI are not used to train or improve foundation models.

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Customer data is stored in public Azure storage accounts

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Customer data is automatically published to public hugging face repositories

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Customer data is used for model training unless you opt out by submitting a support ticket

Question 4

You are tuning the prompt for a generative AI application in Azure AI Foundry. The model frequently hallucinates when asked complex multi-part questions. You decide to use chain-of-thought (CoT) prompting. How should you structure the prompt to implement CoT effectively?

Accepted Answer

Instruct the model to think step-by-step and show its intermediate reasoning before providing the final answer. Chain-of-thought prompting involves guiding the model to articulate intermediate reasoning steps before arriving at the final conclusion.

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Set the model seed parameter to a fixed integer value for reproducibility

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Reduce the maximum completion token limit to 150 tokens

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Instruct the model to output its response exclusively in JSON format with strict schema validation

Question 5

You are building an advanced agentic RAG solution. When a user asks a complex question, the agent needs to generate multiple search queries, execute them in parallel against Azure AI Search, synthesize the findings, and then formulate a final answer. Which agent pattern are you implementing?

Accepted Answer

Query decomposition and parallel retrieval. Query decomposition and parallel retrieval is an advanced RAG pattern where complex queries are broken into sub-queries and executed concurrently.

Answer

Model fine-tuning

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Single-shot keyword extraction

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Static prompt templating

Question 6

You are building an agent that needs to execute Python code safely to calculate financial projections requested by users. Which tool in Azure AI Agent Service should you enable to allow the agent to write and run code in an isolated environment?

Accepted Answer

Code Interpreter tool. The Code Interpreter tool in Azure AI Agent Service provides a secure, sandboxed Python execution environment where agents can run generated code and analyze data.

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Azure Container Instances dynamic scaling group

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Azure Functions webhook integration

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Azure AI Search vector embedding tool

Question 7

You are configuring automatic evaluation in Azure AI Foundry using the groundedness metric. The evaluation pipeline requires a source context and an LLM-generated response. What does the groundedness metric assess?

Accepted Answer

Whether the generated response is derived exclusively from the source context without unsupported claims. The groundedness metric measures whether the model's generated answers can be verified solely from the provided source context, detecting hallucinations.

Answer

The cost per token consumed during inference

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The sentiment and tone of the user prompt

Question 8

You are deploying an open-source model (such as Llama 3) via Model Catalog in Azure AI Foundry. You want to optimize inference latency and throughput for high-concurrency requests. Which deployment type should you select?

Accepted Answer

Managed compute with serverless API deployment. Serverless APIs with pay-as-you-go billing or managed compute provisioned through Azure AI Foundry Model Catalog provide optimized inference hosting.

Answer

Azure SQL Database scalar User Defined Functions

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Local developer workstation Docker container

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Azure Functions consumption plan

Question 9

You are designing a generative AI application in Azure AI Foundry and want to categorize user queries into predefined intent classes before routing them to specific agents. Which prompt engineering technique should you apply?

Accepted Answer

Few-shot prompting with labeled classification examples. Few-shot classification prompting provides examples of inputs paired with their corresponding categories to guide the model's classification accuracy.

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Zero-shot random generation

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Enabling JSON output mode without schema definition

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Increasing model temperature to 1.5

Question 10

You are implementing a RAG solution where documents contain complex hierarchical headings. To ensure search chunks retain sufficient context about which section they belong to, what chunking strategy should you adopt?

Accepted Answer

Hierarchical (parent-child) chunking retaining section headers. Hierarchical chunking or parent-child chunking retains parent section headings and metadata within smaller searchable child chunks, preserving document context.

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Fixed-size character chunking with zero overlap

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Random token sampling without structural awareness

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Single-chunk whole document ingestion

Question 11

You are monitoring an Azure OpenAI deployment in Azure AI Foundry and notice an increase in HTTP 429 (Too Many Requests) errors. Your application needs to handle these rate limit spikes gracefully. What is the recommended architectural solution?

Accepted Answer

Implement exponential backoff and jitter retry logic in the application SDK. Implementing retry logic with exponential backoff and jitter in the application code handles transient rate-limiting errors (HTTP 429) gracefully.

Answer

Restart the Azure OpenAI resource from the Azure Portal

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Set the model temperature parameter to 0.0

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Switch the authentication method from API keys to Managed Identity

Question 12

You are creating a custom prompt template in Azure AI Foundry. You want to insert the user's input safely while preventing prompt injection vulnerabilities. How should you design the template structure?

Accepted Answer

Use explicit XML tags or markdown delimiters to separate system instructions from user inputs. Separating system instructions from user input using clear delimiters (such as markdown blockquotes or XML tags like <user_input>) helps the model distinguish instructions from data.

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Remove all spaces and punctuation from the user input

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Concatenate user input directly into the middle of the system prompt string

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Encode all user inputs using Base64 before sending to the model

Question 13

You are designing a multi-agent solution in Azure AI Foundry where agents collaborate asynchronously. One agent produces an intermediate JSON payload that must be strictly validated before being consumed by the next agent. What feature should you implement?

Accepted Answer

Structured Outputs with strict JSON Schema enforcement. Structured Outputs (JSON mode with strict schema enforcement) ensures that model completions conform precisely to a defined JSON Schema.

Answer

Model temperature set to 1.0

Answer

Azure API Management XML transformation policy

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Azure AI Content Safety hate speech detection

Question 14

You are integrating Azure AI Search with Azure OpenAI for grounded chat generation. Users report that the generated answers are accurate, but the citations returned to the user point to raw internal file paths rather than user-friendly web URLs. How should you resolve this?

Accepted Answer

Store public URL equivalents in index metadata fields and configure citation mapping in your orchestration layer. Mapping internal file path fields to public URL fields in the Azure AI Search index mapping configuration ensures correct citation links are returned.

Answer

Enable Azure AI Content Safety citation filtering

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Decrease the search result top_k parameter to 1

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Modify the Azure OpenAI system prompt to guess web URLs based on file names

Question 15

You are building a generative AI application that summarizes long legal documents. Users complain that summaries omit important clauses located in the middle of the document due to the model's attention limitations over extremely long texts. What phenomenon does this represent, and how can you mitigate it?

Accepted Answer

Lost in the middle phenomenon; mitigate using map-reduce or recursive summarization patterns. The 'lost in the middle' phenomenon occurs when LLMs overlook information in the middle of long contexts. Mitigation involves chunking or map-reduce summarization patterns.

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Token overflow error; mitigate by increasing Azure AI Search semantic ranker depth

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Embedding drift; mitigate by retraining the text-embedding-ada-002 model

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Prompt injection; mitigate by enabling Azure AI Content Safety prompt shields