810-110 Actual Exam Dumps & New 810-110 Dumps Pdf

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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
How does context window management help control the cost of a long-running AI conversation?

A. by increasing the context window size to reduce the total number of API calls
B. by switching to an algorithm that reduces the character-to-token ratio
C. by automatically switching to a lower-cost model as the conversation history grows
D. by capping the cumulative number of input tokens sent with each new prompt

Answer: D

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 51
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

A. hypothesize
B. visualize
C. orient
D. correlate

Answer: A

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.

NEW QUESTION # 52
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

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

Answer: A

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 # 53
What is fine-tuning in generative AI?

A. Training a pre-trained model on specialized data
B. Formatting storage drives
C. Encrypting prompts
D. Replacing CPUs

Answer: A

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 54
Which activity is an agentic AI use case compared to a standard generative AI use case?

A. translating a short email
B. generating a meeting summary
C. autonomously fixing a build
D. writing a poem about routing

Answer: C

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.

NEW QUESTION # 55
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