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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Topic 2: Ethics and Security	15%	- AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - Data privacy and governance 1. Secure AI data handling
Topic 3: AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. Code generation and debugging 2. SDLC integration
Topic 4: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 5: Generative AI Models	20%	- Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits - Model hosting and architecture 1. Latency, cost, privacy considerations 2. Cloud vs local deployment trade-offs - Model fundamentals and use cases 1. LLMs and diffusion models overview 2. Use cases: summarization, code generation, content creation
Topic 6: Prompt Engineering	15%	- Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompt design principles 1. Roles, instructions, constraints

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Cisco AI Technical Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
What is the benefit of AI Ops (AIOps) in networking?

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

Answer: D

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

NEW QUESTION # 50
What is the purpose of a vector database in a RAG architecture?

A. to perform model weight updates
B. to allocate resources for response generation
C. to manage the context window
D. to store numerical representations of data

Answer: D

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.

NEW QUESTION # 51
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

A. minimizing the input prompt size
B. increasing the model sampling rate
C. increasing the model temperature
D. expanding the total context window

Answer: A

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.

NEW QUESTION # 52
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. multimodal model
B. embedding model
C. generative adversarial network
D. diffusion model

Answer: A

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 # 53
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

A. cloud-based API with Zero Data Retention
B. managed SaaS AI platform with SOC 2 audit
C. hybrid deployment with cloud-based backup
D. local LLM deployment on internal hardware

Answer: D

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.

NEW QUESTION # 54
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