Free 810-110 sample questions

Correct answer: Large Language Models (LLMs), because their transformer architecture is optimized for predicting sequential text and code tokens.

Large Language Models (LLMs) rely on transformer architectures that excel at understanding and generating sequential data, making them ideal for text summarization, content creation, and code generation (like Python scripts for Cisco devices). Diffusion models are primarily used for image/audio generation, and GANs are typically used for synthesizing realistic media rather than logical code structures.

Correct answer: Deploy a locally hosted, quantized open-weights model (e.g., Llama 3 8B) on existing on-premises CPU/entry-level GPU servers.

The strict constraint that data 'cannot leave the corporate network boundary' mandates a locally hosted (on-premises) model to ensure data privacy and compliance. To address the 'limited capital budget for GPU hardware' constraint, using a smaller, quantized model allows inference to run efficiently on lower-end hardware or even CPUs, successfully balancing the privacy and cost trade-offs.

Correct answer: The RAG architecture is optimal because it significantly reduces token consumption per query, lowering costs and latency, while the retrieval step inherently provides the specific source chunks for traceability.

RAG is the optimal choice here. Feeding a 500-page document into a massive context window for every query results in extremely high token costs and high latency due to the attention mechanism processing the entire document each time. RAG mitigates this by retrieving only the relevant chunks (lowering cost and latency) and inherently provides traceability by showing exactly which retrieved chunks were used to generate the answer. quadrantChart title RAG vs Long Context Trade-offs x-axis Low Cost --> High Cost y-axis High Latency --> Low Latency quadrant-1 Unfavorable quadrant-2 Ideal quadrant-3 Suboptimal quadrant-4 Acceptable Long Context Window: [0.85, 0.80] RAG Architecture: [0.20, 0.30]

Correct answers: Multimodality (Vision-Language capability); Reasoning capabilities

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.

Correct answer: It performs a semantic similarity search by calculating the distance between the query embedding and document chunk embeddings.

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]

Correct answer: Small chunks without overlap sever the semantic context between adjacent paragraphs, depriving the LLM of the complete architectural concept needed for an accurate response.

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.