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CCNA Implement agentic AI solutions Questions

15 questions · Implement agentic AI solutions · All types, answers revealed

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1
Multi-Selectmedium

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

Select 2 answers
A.Ensure the model is large enough to memorize the entire document library.
B.Fine-tune the Azure OpenAI model on the document library.
C.Index the documents into a vector database like Azure Cognitive Search.
D.Train a custom language model from scratch.
E.Use a retrieval step to fetch relevant document chunks before generating a response.
AnswersC, E

Indexing enables efficient retrieval of relevant content.

Why this answer

Option C is correct because indexing documents into a vector database like Azure Cognitive Search enables efficient similarity search over embeddings, which is the retrieval foundation of RAG. This allows the system to quickly find the most relevant document chunks based on semantic meaning, rather than relying on the model to memorize or be fine-tuned on the entire library.

Exam trap

The trap here is that candidates often confuse fine-tuning (which adapts model behavior) with RAG (which augments prompts with retrieved data), leading them to select Option B instead of understanding that RAG requires an external retrieval step and vector index.

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2
Multi-Selecthard

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

Select 3 answers
A.The temperature parameter is set too high.
B.The token limit is too low, truncating the function definitions.
C.The function parameter schemas are incorrect or incomplete.
D.The function descriptions are ambiguous or missing.
E.The model version is outdated.
AnswersB, C, D

Low token limits can cut off function definitions.

Why this answer

Option B is correct because if the token limit is too low, the model may not receive the full function definitions, causing it to omit or misinterpret available functions. This truncation prevents the model from correctly selecting or formatting function calls, leading to execution failures.

Exam trap

Microsoft often tests the misconception that temperature or model version are primary causes for function-calling failures, when in reality the core issues are token limits, schema correctness, and description clarity.

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3
MCQhard

A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?

A.Azure Cognitive Search and Azure AI Language.
B.Azure Bot Service and Azure Logic Apps.
C.Azure Machine Learning and Azure Functions.
D.Azure AI Content Safety and Azure OpenAI content filtering.
AnswerD

These services provide comprehensive content safety.

Why this answer

Azure AI Content Safety provides built-in models for detecting harmful content such as hate speech, self-harm, and sexual content, while Azure OpenAI content filtering applies configurable severity-level filters (e.g., low, medium, high) to model inputs and outputs. Together, they enable real-time monitoring of toxicity and bias in generated investment advice, meeting compliance requirements for financial services.

Exam trap

The trap here is that candidates may confuse general AI services (like Azure AI Language or Azure Machine Learning) with the specific, purpose-built content safety and filtering services required for monitoring toxicity and bias in generative AI outputs.

How to eliminate wrong answers

Option A is wrong because Azure Cognitive Search is a retrieval service for indexing and querying data, not a content safety or bias detection tool, and Azure AI Language provides NLP features like sentiment analysis but lacks dedicated toxicity and bias monitoring for generative AI outputs. Option B is wrong because Azure Bot Service is a framework for building conversational agents, and Azure Logic Apps is an integration workflow service; neither includes built-in content safety or bias detection capabilities. Option C is wrong because Azure Machine Learning is a platform for training and deploying custom ML models, and Azure Functions is a serverless compute service; while you could build custom safety logic, they do not provide the pre-built, configurable content filtering and toxicity detection that Azure AI Content Safety and Azure OpenAI content filtering offer out of the box.

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4
MCQhard

A company is using Azure OpenAI Service to power a customer support agent. The agent sometimes generates incorrect information when it cannot find an answer in the knowledge base. The team wants to ensure the agent only responds using information from the knowledge base and explicitly states when it does not know the answer. Which configuration should the team use?

A.Use a custom model fine-tuned on the knowledge base and disable content filtering.
B.Use a system message that says 'If you don't know, say you don't know' and rely on the model's training.
C.Use 'use your own data' feature with strict content filtering and set the model to only respond based on retrieved documents.
D.Use prompt engineering with a system message that instructs the model to only answer from the knowledge base, with no additional filtering.
AnswerC

This ensures responses are grounded in the provided data.

Why this answer

Option C is correct because the 'use your own data' feature in Azure OpenAI Service allows you to restrict the model to answer only from the retrieved documents, ensuring responses are grounded in the knowledge base. Strict content filtering further prevents the model from generating unverified information, and the explicit setting to respond based solely on retrieved documents directly addresses the requirement to state when it does not know the answer.

Exam trap

The trap here is that candidates often confuse prompt engineering (Option D) with a reliable grounding mechanism, not realizing that without a retrieval-augmented generation (RAG) architecture and strict content filtering, the model can still hallucinate even when instructed otherwise.

How to eliminate wrong answers

Option A is wrong because fine-tuning a custom model on the knowledge base does not guarantee the model will only use that knowledge; it can still hallucinate or generate information outside the training data, and disabling content filtering removes safety guardrails without solving the grounding issue. Option B is wrong because relying on a system message and the model's training is insufficient; the model may still generate incorrect information when it cannot find an answer, as it has no mechanism to enforce grounding to the knowledge base. Option D is wrong because prompt engineering alone, even with a system message instructing the model to only answer from the knowledge base, does not provide a technical enforcement mechanism; the model can still hallucinate or generate responses not present in the knowledge base without the retrieval-augmented generation (RAG) architecture that 'use your own data' provides.

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5
MCQmedium

A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?

A.Increase the training data for location entities.
B.Add a prebuilt entity component for date.
C.Train the entity extraction model with more examples of dates.
D.Use a different intent classification model.
AnswerC

More training examples improve entity recognition.

Why this answer

Option C is correct because the custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.

Exam trap

The trap here is that candidates confuse the need for more training data (Option C) with the use of prebuilt components (Option B), assuming prebuilt entities can fix custom model gaps, but prebuilt entities are not part of the custom entity extraction pipeline and would require a different project type.

How to eliminate wrong answers

Option A is wrong because increasing training data for location entities does not address the failure to extract date entities; the location entity already works correctly. Option B is wrong because adding a prebuilt entity component for date would override the custom entity extraction model's behavior, which contradicts the requirement to use a custom entity extraction model; prebuilt entities are used in orchestration workflows, not to fix custom model training deficiencies. Option D is wrong because the intent classification model ('BookFlight') is correctly identifying the intent; the problem lies with entity extraction, not intent classification, so changing the intent model would not resolve the date extraction failure.

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6
MCQmedium

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

A.Use a single prompt that includes the entire conversation history and the latest user question, then generate SQL.
B.Use a chain-of-thought prompt to generate SQL queries directly from user input, without maintaining conversation history.
C.Use embeddings-based retrieval to find relevant past interactions and include them in the prompt.
D.Use a conversational agent framework like AutoGen with a tool that executes SQL queries, and maintain conversation state.
AnswerD

AutoGen provides multi-turn conversation management and tool integration.

Why this answer

Option D is correct because AutoGen is a conversational agent framework designed for multi-turn, stateful interactions. It can maintain conversation context across turns and integrate a tool to execute SQL queries, which directly meets the requirement for complex multi-turn conversations with context retention. The other options either lack state management or rely on stateless prompt engineering, which is insufficient for maintaining context in a multi-turn agent.

Exam trap

Microsoft often tests the distinction between stateless prompt engineering (options A, B, C) and stateful agent frameworks (option D), where candidates mistakenly believe that simply including history in a prompt (option A) is sufficient for multi-turn context, ignoring token limits and the lack of structured state management.

How to eliminate wrong answers

Option A is wrong because using a single prompt with the entire conversation history quickly exceeds the token limit of the Azure OpenAI model (e.g., 4096 or 8192 tokens for GPT-4), leading to truncation or loss of context, and it does not provide a structured mechanism for maintaining conversation state across turns. Option B is wrong because chain-of-thought prompting without maintaining conversation history cannot handle multi-turn conversations; each turn would be treated as an isolated query, losing all prior context and making it impossible to handle follow-up questions or references to previous interactions. Option C is wrong because embeddings-based retrieval finds semantically similar past interactions but does not inherently maintain the current conversation's state or context; it is typically used for retrieval-augmented generation (RAG) in single-turn scenarios, not for managing the sequential state of a multi-turn dialogue.

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7
MCQeasy

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

A.Azure Logic Apps
B.Azure API Management
C.Azure Bot Service
D.Azure Functions
AnswerC

Azure Bot Service provides bot hosting and channel integration.

Why this answer

Azure Bot Service is the correct choice because it provides a managed environment for building, deploying, and scaling conversational AI agents that can be integrated with multiple channels, including websites and mobile apps. It supports the Bot Framework SDK, which enables the agent to handle order tracking, returns, and FAQs through natural language understanding (NLU) with LUIS or the newer CLU service.

Exam trap

The trap here is that candidates often confuse Azure Bot Service with Azure Logic Apps or Azure Functions, mistakenly thinking that workflow automation or serverless compute alone can serve as a conversational agent, but they lack the essential dialog management, channel integration, and NLU capabilities that Azure Bot Service provides.

How to eliminate wrong answers

Option A is wrong because Azure Logic Apps is a workflow automation service for integrating apps and data, not a conversational agent platform; it lacks built-in support for dialog management, NLU, and multi-channel deployment. Option B is wrong because Azure API Management is used to publish, secure, and monitor APIs, not to host interactive conversational agents; it cannot manage user intents or multi-turn dialogues. Option D is wrong because Azure Functions is a serverless compute service for running event-driven code, but it does not provide the necessary framework for building conversational flows, channel adapters, or state management required for an agent.

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8
Matchingmedium

Match each Azure AI pricing tier to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts
Matches

Limited transactions per month for evaluation

Production tier with higher throughput

Higher throughput than S0

Even higher throughput for large workloads

Highest throughput tier

Why these pairings

These are common pricing tiers for Azure Cognitive Services.

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9
Multi-Selecthard

An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?

Select 2 answers
A.Pre-process the text to handle negations and sarcasm.
B.Use a custom sentiment analysis model trained on domain-specific data.
C.Switch to a different language model without fine-tuning.
D.Increase the number of decimal places in the sentiment score.
E.Increase the confidence threshold for positive sentiment.
AnswersA, B

Pre-processing can improve sentiment detection.

Why this answer

Option A is correct because Azure AI Language's pre-built sentiment analysis models can struggle with linguistic nuances like negations (e.g., 'not good') and sarcasm (e.g., 'Great, another delay'). Pre-processing the text to explicitly handle these patterns—such as by expanding contractions or using negation detection—can improve the accuracy of the sentiment scores before they are passed to the model.

Exam trap

The trap here is that candidates often assume that increasing precision or switching models generically will fix inaccuracies, rather than recognizing that domain-specific fine-tuning and text pre-processing are the standard Azure AI Language approaches to handle linguistic edge cases like negations and sarcasm.

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10
MCQmedium

A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?

A.Use a different OCR API from Azure Cognitive Services.
B.Pre-process the image to adjust contrast and brightness before calling the OCR API.
C.Train a custom OCR model using Azure Custom Vision.
D.Increase the confidence threshold for text detection.
AnswerB

Image pre-processing improves OCR accuracy.

Why this answer

Option B is correct because Azure AI Vision's OCR API performs best on images with sufficient contrast and brightness. Pre-processing the image (e.g., using OpenCV or PIL to adjust contrast and brightness) enhances text visibility, directly improving OCR accuracy for low-contrast images without changing the API or training a custom model.

Exam trap

The trap here is that candidates may assume Azure Custom Vision can be repurposed for OCR or that adjusting confidence thresholds can fix recognition accuracy, when in fact pre-processing the image is the standard approach to improve OCR results for low-quality inputs.

How to eliminate wrong answers

Option A is wrong because Azure AI Vision's Read API is already the dedicated OCR service within Azure Cognitive Services; switching to a different OCR API (e.g., Form Recognizer) would not inherently solve low-contrast issues and may add unnecessary complexity. Option C is wrong because Azure Custom Vision is designed for image classification and object detection, not for reading text; it cannot be trained to perform OCR. Option D is wrong because increasing the confidence threshold for text detection would filter out more detections, potentially missing low-confidence but correct text reads, and does not improve the underlying OCR engine's ability to recognize text in low-contrast images.

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11
Drag & Dropmedium

Drag and drop the steps to implement an Azure AI Bot Service with QnA Maker into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order

Why this order

Start with QnA Maker, build the knowledge base, create the bot, connect it, and test.

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12
MCQeasy

A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?

A.Fine-tune the model on a dataset of correct function calls.
B.Reduce the number of functions to only the most common ones.
C.Set the temperature parameter to 0 for deterministic output.
D.Provide better function descriptions with examples of when to use each function.
AnswerD

Clear descriptions improve function selection.

Why this answer

Option D is correct because providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.

Exam trap

The trap here is that candidates often assume deterministic output (temperature=0) or reducing complexity (fewer functions) will fix reliability, when the real issue is semantic ambiguity in function definitions that the model cannot resolve without better descriptions.

How to eliminate wrong answers

Option A is wrong because fine-tuning on a dataset of correct function calls is unnecessary and inefficient; Azure OpenAI's base models already understand function calling patterns, and fine-tuning would require a large, curated dataset and could introduce overfitting or degrade general performance. Option B is wrong because reducing the number of functions limits the agent's capabilities and does not address the root cause of incorrect selection; the model may still invent names if descriptions are poor. Option C is wrong because setting temperature to 0 makes output deterministic but does not fix ambiguous or poorly defined function descriptions; the model will still confidently choose the wrong function if it misinterprets the intent.

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13
Multi-Selecteasy

A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?

Select 3 answers
A.Use the Bot Framework SDK to build the bot with Teams-specific features.
B.Create a Teams app manifest file with bot configuration.
C.Register the bot in the Azure portal and obtain a Microsoft App ID.
D.Deploy the bot code to an Azure Function.
E.Write the bot in C# using Azure SDK for .NET.
AnswersA, B, C

SDK provides Teams integration capabilities.

Why this answer

Option A is correct because the Bot Framework SDK provides the necessary APIs and tools to build bots that can leverage Teams-specific features such as adaptive cards, messaging extensions, and task modules. Without the SDK, you cannot implement the channel-specific logic required for Teams integration.

Exam trap

The trap here is that candidates often assume hosting (Azure Function) or language choice (C#) are mandatory steps, when in fact the three required steps are always: register the bot in Azure, build with the SDK, and create the Teams app manifest.

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14
MCQeasy

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

A.Microsoft Bot Framework SDK with a custom connector.
B.Azure Cognitive Search with custom analyzers.
C.Azure OpenAI Service with data processing enabled.
D.Azure AI Services deployed in a private endpoint with no data leaving the network.
AnswerD

Private endpoint ensures data stays within the network, aiding compliance.

Why this answer

Option D is correct because deploying Azure AI Services within a private endpoint, combined with ensuring no data leaves the network, aligns with HIPAA's requirement for data privacy and compliance. This configuration uses Azure Private Link to keep all traffic within the Microsoft backbone network, preventing exposure to the public internet and meeting the strict data residency and encryption standards mandated by HIPAA.

Exam trap

The trap here is that candidates often assume enabling data processing or using a specific SDK automatically satisfies compliance, but HIPAA requires explicit network isolation and data residency controls, which only private endpoints and network restrictions provide.

How to eliminate wrong answers

Option A is wrong because the Microsoft Bot Framework SDK with a custom connector focuses on building conversational interfaces and does not inherently enforce data privacy or compliance controls like HIPAA; it lacks built-in mechanisms to prevent data from leaving a secure network boundary. Option B is wrong because Azure Cognitive Search with custom analyzers is a search service that indexes and queries data, but it does not provide native HIPAA compliance features or guarantee that data remains within a private network; custom analyzers only affect tokenization and indexing, not data privacy. Option C is wrong because Azure OpenAI Service with data processing enabled does not automatically ensure HIPAA compliance; while it can process data, it may still transmit data to external endpoints or rely on public network paths unless explicitly configured with private endpoints and data residency controls, which are not guaranteed by simply enabling data processing.

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15
MCQmedium

You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?

A.Keep page-level chunking but add 50% overlap between chunks.
B.Use a different embedding model, such as text-embedding-3-large.
C.Increase the chunk size to 5 pages per chunk and reduce overlap.
D.Change chunking to use semantic boundaries: split at clause or section headings.
AnswerD

Semantic chunking improves relevance.

Why this answer

Option D is correct because splitting contracts at semantic boundaries (clause or section headings) preserves the natural meaning and context of each chunk, which is critical for legal document analysis. Page-level chunking often splits a clause across two pages, causing the vector search to retrieve incomplete or irrelevant information. By aligning chunks with the document's logical structure, the RAG pattern retrieves more coherent and relevant passages for the Azure OpenAI agent to process.

Exam trap

The trap here is that candidates often focus on tuning parameters like overlap or chunk size, or switching embedding models, without recognizing that the fundamental issue is the chunking strategy's failure to respect the document's logical structure.

How to eliminate wrong answers

Option A is wrong because adding 50% overlap to page-level chunking still splits clauses at arbitrary page boundaries, and the overlap only partially mitigates the issue without guaranteeing that a complete clause is captured in a single chunk. Option B is wrong because the embedding model is not the root cause; even a better model like text-embedding-3-large cannot fix retrieval accuracy if the chunking strategy destroys semantic coherence. Option C is wrong because increasing chunk size to 5 pages per chunk makes the chunks too large and reduces precision, and reducing overlap further increases the risk of missing relevant content that spans chunk boundaries.

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