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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q68-Q73):

NEW QUESTION # 68
In supervised machine learning, what is required?

A. Labeled datasets
B. Random hardware failures
C. No data
D. Only encrypted traffic

Answer: A

NEW QUESTION # 69
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. encrypting the data payload during transit to the AI service provider
C. utilizing system prompts to instruct the AI to ignore sensitive data fields
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 # 70
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

A. lower network latency for all end users
B. full control over data residency and privacy
C. elimination of per-token operational costs
D. rapid elastic scaling for unpredictable inference demand

Answer: D

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.

NEW QUESTION # 71
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

A. Tokenization has no impact because pricing is based on a flat-rate subscription.
B. Tokenization increases cost by requiring additional GPU run time.
C. Costs are determined by the number of API calls.
D. The number of tokens determines the per-request cost.

Answer: D

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.

NEW QUESTION # 72
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

A. It ensures the most creative text.
B. It allows for easy repurposing.
C. It maintains contextual alignment.
D. It minimizes token consumption.

Answer: B

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.

NEW QUESTION # 73
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