810-110 Sample Questions & Answers
Three areas tie for the heaviest weight: hosting tradeoffs across generative AI models, building and deploying AI through the development lifecycle, and agentic AI under the Model Context Protocol, alongside prompt engineering, AI ethics, and data analysis.
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- Question 1IntermediateSelect 2
Generative AI Models · Understand model selection in AI model hubs and repositories for appropriate use-cases
When browsing AI model hubs like Hugging Face to select a model for analyzing network topology diagrams (images) and producing a written summary of potential single points of failure, which TWO model characteristics are strictly required? (Select TWO)
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Correct answers: D, E
To analyze network topology diagrams (images) and output text, the model must be multimodal (specifically, a Vision-Language Model). Furthermore, identifying single points of failure requires logical deduction and architectural understanding, meaning the model must possess strong reasoning capabilities rather than just basic image captioning.
To analyze network topology diagrams (images) and output text, the model must be multimodal (specifically, a Vision-Language Model). Furthermore, identifying single points of failure requires logical deduction and architectural understanding, meaning the model must possess strong reasoning capabilities rather than just basic image captioning.
- Question 2Intermediate
Generative AI Models · Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases
In a Retrieval-Augmented Generation (RAG) architecture built to query Cisco Nexus configuration guides, what is the primary role of the vector database during the retrieval phase?
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Correct answer: A
In a RAG system, the vector database stores embeddings (mathematical representations) of document chunks. When a user submits a query, it is also converted into an embedding. The vector database performs semantic search by calculating the mathematical distance (e.g., cosine similarity) between the query vector and the stored vectors to return the most relevant context chunks.
flowchart LR Query[User Query] --> Embed[Embedding Model] Embed --> VectorDB[(Vector DB)] VectorDB -->|Cosine Similarity| Context[Top-K Results] Context --> LLM[Generative Model] - Question 3Advanced
Generative AI Models · Explain role of context windows, token limits and response management
A data engineer is optimizing a RAG pipeline that indexes Cisco design guides. They notice that the LLM frequently hallucinates details when answering complex architectural questions, even though the correct documents are being retrieved. Upon inspection, the chunk size is set to 150 tokens with no overlap.
How does this chunking strategy negatively impact the model's response management?
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Correct answer: C
Chunking strategy directly impacts what context is injected into the prompt. A very small chunk size (150 tokens) with no overlap often cuts sentences or concepts in half. When the vector DB retrieves these fragmented chunks, the LLM lacks the surrounding context required to formulate a complete, accurate answer, leading to hallucinations as it tries to fill in the missing information.
- Question 4Beginner
Generative AI Models · Explain role of context windows, token limits and response management
True or False: If a user submits a prompt containing 5,000 tokens to an LLM that has a strict maximum context window of 4,096 tokens, the model will automatically compress the prompt using a diffusion process to fit the entire request into the window.
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Correct answer: B
False. LLMs do not use diffusion to compress text. If a prompt exceeds the context window token limit, the system will typically either reject the request with an error or truncate (cut off) a portion of the text (often the oldest/beginning tokens) to fit within the hard limit. Diffusion is an architecture used for generating images or audio, not for text compression.
- Question 5Intermediate
Generative AI Models · Describe major generative AI model families and common use cases
A developer is building a system to parse syslog messages from Cisco routers and generate a natural language summary of network health. Which fundamental characteristic of the chosen generative AI model family makes it suitable for this sequence-to-sequence text task?
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Correct answer: C
Large Language Models (LLMs) are built on the Transformer architecture. The defining feature of this architecture is the self-attention mechanism, which allows the model to look at the entire sequence of text (like a syslog message) and determine which tokens are most relevant to each other, making it highly effective for text summarization and translation.
- Question 6Beginner
Prompt Engineering · Understand prompt engineering principles and patterns
A prompt engineer is designing an AI assistant for a corporate IT helpdesk. The assistant must only provide answers related to Cisco networking and must always respond in a professional, courteous tone. Which prompt engineering principle is best suited to enforce these overarching behaviors across all user interactions?
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Correct answer: B
Role prompting within the System prompt is the standard practice for setting the persona, boundaries, and tone of an AI model across an entire session. By instructing the model "You are a professional Cisco IT helpdesk assistant. You only answer networking questions..." in the system prompt, the model adopts these constraints globally before processing any user prompts.
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