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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. Misinformation and hallucinations 2. AI-specific attack surfaces - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - Data privacy and governance 1. Secure AI data handling
Topic 3: Generative AI Models	20%	- 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 - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits
Topic 4: AI for Code and Workflow Optimization		- AI-assisted software development 1. Code generation and debugging 2. SDLC integration - Automation workflows 1. API integration basics 2. Workflow orchestration concepts
Topic 5: Prompt Engineering	15%	- Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting
Topic 6: 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 (Q53-Q58):

NEW QUESTION # 53
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 # 54
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

A. parameter count of the model
B. training duration of the model
C. release frequency of the model
D. response creativity level of the model

Answer: A

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.

NEW QUESTION # 55
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

A. automated unit testing
B. AI-assisted debugging
C. continuous integration optimization
D. code scaffolding

Answer: B

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.

NEW QUESTION # 56
Which action demonstrates the principle of fairness in a generative AI deployment?

A. redacting PII from the training dataset
B. auditing model outputs for bias
C. requiring users to sign Acceptable Use Policies
D. hosting the model in a carbon-neutral data center

Answer: B

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 57
Which approach protects PII when utilizing a public AI service for data analysis?

A. anonymizing or redacting PH from the dataset before transmitting the request
B. utilizing system prompts to instruct the AI to ignore sensitive data fields
C. encrypting the data payload during transit to the AI service provider
D. ensuring the AI provider maintains industry-standard SOC 2 compliance

Answer: A

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.

NEW QUESTION # 58
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