Exam 810-110 Training & Questions 810-110 Pdf

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
AI for Code and Workflow Optimization		- AI-assisted software development 1. SDLC integration 2. Code generation and debugging - Automation workflows 1. API integration basics 2. Workflow orchestration concepts
Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Generative AI Models	20%	- Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs - Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model fundamentals and use cases 1. LLMs and diffusion models overview 2. Use cases: summarization, code generation, content creation
Prompt Engineering	15%	- Security and robustness 1. Prompt injection risks 2. Mitigation and defensive prompting - Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting
Ethics and Security	15%	- Data privacy and governance 1. Secure AI data handling - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations
AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation

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Cisco AI Technical Practitioner Sample Questions (Q69-Q74):

NEW QUESTION # 69
In an AI-powered automation workflow, what is deterministic branching?

A. A decision point where the next step is dictated by specific, pre-programmed rules.
B. A process where the AI model autonomously selects the next step based on its internal confidence score.
C. A dynamic system where branching criteria evolve over time using optimization algorithms.
D. A workflow state that pauses execution until a human manually determines the next step.

Answer: A

Explanation:
Deterministic branching uses predefined rules or conditions to decide the next workflow step, making the path predictable and repeatable rather than model-driven or manually selected.

NEW QUESTION # 70
What is the purpose of tokenization in Large Language Models (LLMs)?

A. Splitting text into manageable units
B. Encrypting passwords
C. Routing packets
D. Compressing images

Answer: A

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.

NEW QUESTION # 71
What is the benefit of AI Ops (AIOps) in networking?

A. Automates monitoring and incident analysis
B. Disables telemetry
C. Eliminates all routers
D. Removes cloud computing

Answer: A

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.

NEW QUESTION # 72
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

A. embedding model
B. diffusion model
C. multimodal model
D. generative adversarial network

Answer: C

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.

NEW QUESTION # 73
What is the role of embeddings in AI systems?

A. Assigning IP addresses
B. Representing data numerically in vector space
C. Compressing logs
D. Encrypting traffic

Answer: B

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.

NEW QUESTION # 74
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