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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering	15%	- Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting - Prompt design principles 1. Roles, instructions, constraints
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
Generative AI Models	20%	- Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations - 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
AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
Ethics and Security	15%	- AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Responsible AI principles 1. Fairness, transparency, accountability 2. Bias mitigation - Data privacy and governance 1. Secure AI data handling
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 (Q77-Q82):

NEW QUESTION # 77
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 switches to a different algorithm.
C. The model caches the overflow data.
D. The model loses the earliest part of the text.

Answer: D

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 # 78
To reduce hallucinations in a technical report, a practitioner directs an AI to "Use only the provided text to answer, and say 'I don't know' when the information is absent." Which strategy is being applied?

A. Expanding the context
B. Increasing temperature
C. Grounding constraints
D. Using multimodal AI

Answer: C

Explanation:
Grounding constraints are instructions that limit an AI model's responses to specified evidence, authoritative documents, or permitted information sources. Option A is correct because the practitioner explicitly restricts the model to the provided text and requires acknowledgment when supporting information is unavailable.
Grounding helps reduce unsupported claims by discouraging the model from introducing unrelated facts or relying on information not established by the source material.
For example, an engineer summarizing a network security assessment may instruct an AI assistant to use only the supplied audit report and avoid inventing compliance findings or remediation details.
The instruction to acknowledge missing information is particularly important because models can otherwise produce plausible but unsupported technical statements.
Option B is incorrect because multimodal AI concerns processing multiple input types, such as text and images.
Option C is incorrect because expanding context capacity does not inherently improve factual grounding or eliminate fabricated statements.
Option D is incorrect because increasing temperature generally increases sampling variability rather than strengthening evidence-based accuracy.
Grounding instructions are valuable but insufficient by themselves. Stronger implementations verify source references, validate generated claims, and use retrieval mechanisms that supply relevant authoritative evidence.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Debugging and Correcting AI-Generated Outputs; Objective 2.4 - Hallucination mitigation and defensive prompting.

NEW QUESTION # 79
An AI-powered system must dynamically determine which tools to use when troubleshooting complex network outages. Why is an autonomous agentic system typically more costly to operate than a directive workflow for this task?

A. Agents are billed by the hour of uptime, whereas workflows are billed only when a request is made.
B. Agents consume tokens for every step of their internal reasoning loop, whereas workflows execute fixed, pre-programmed logic.
C. Agents require specialized, high-cost GPU hardware clusters to maintain the state of the reasoning loop during tool execution.
D. Agents require multiple API keys for every tool they access, which increases subscription costs.

Answer: B

NEW QUESTION # 80
Why are prompts important in generative AI systems?

A. They eliminate hallucinations completely
B. They determine model behavior and output quality
C. They replace training datasets
D. They encrypt AI hardware

Answer: B

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.

NEW QUESTION # 81
Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

A. implementing predictive function logic
B. performing code scaffolding
C. executing legacy code refactoring
D. providing inline co-pilot assistance

Answer: B

Explanation:
Code scaffolding uses AI to generate the initial structure and boilerplate for a new component, such as a complete Python class for an API client based on a high-level description.

NEW QUESTION # 82
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