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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
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. Misinformation and hallucinations 2. AI-specific attack surfaces
AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Prompt Engineering	15%	- Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting - Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Prompt injection risks 2. Mitigation and defensive prompting
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
Generative AI Models	20%	- 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 - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations
Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows

>> 810-110 Certified Questions <<

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Cisco AI Technical Practitioner Sample Questions (Q17-Q22):

NEW QUESTION # 17
What is a corporate data privacy risk when using a public AI chatbot?

A. The AI will provide biased technical advice.
B. The data will be encrypted at rest instead of in transit.
C. The input data may be used for model training.
D. The AI will provide hallucinated technical specifications.

Answer: C

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.

NEW QUESTION # 18
What happens when the input provided to an LLM exceeds its context window limit?

A. The model increases its context window size.
B. The model loses the earliest part of the text.
C. The model caches the overflow data.
D. The model switches to a different algorithm.

Answer: B

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 19
Which AI model type is best suited for image recognition tasks?

A. K-Means
B. Convolutional Neural Network (CNN)
C. Decision Tree only
D. Linear Regression

Answer: B

Explanation:
CNNs are optimized for image and visual pattern recognition.

NEW QUESTION # 20
Which concept helps reduce overfitting in machine learning?

A. Ignoring datasets
B. Regularization
C. Disabling validation
D. Increasing random noise only

Answer: B

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.

NEW QUESTION # 21
What is the purpose of a vector database in a RAG architecture?

A. to allocate resources for response generation
B. to manage the context window
C. to perform model weight updates
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 # 22
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