Quiz 2026 810-110: Valid Cisco AI Technical Practitioner Exam Review

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Agentic AI Systems		- Agent architecture 1. Autonomous agents and orchestration 2. Multi-step reasoning workflows
Topic 3: 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. 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
Topic 4: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 5: 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 6: Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Security and robustness 1. Prompt injection risks 2. Mitigation and defensive prompting - Prompting techniques 1. Few-shot prompting 2. Chained and iterative prompting

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Cisco AI Technical Practitioner Sample Questions (Q59-Q64):

NEW QUESTION # 59
In an AI-powered automation workflow, what is deterministic branching?

A. A process where the AI model autonomously selects the next step based on its internal confidence score.
B. A workflow state that pauses execution until a human manually determines the next step.
C. A dynamic system where branching criteria evolve over time using optimization algorithms.
D. A decision point where the next step is dictated by specific, pre-programmed rules.

Answer: D

Explanation:
Deterministic branching uses predefined rules or conditions to decide the next workflow step, making the path predictable and repeatable rather than model-driven or manually selected.

NEW QUESTION # 60
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. correlate
B. hypothesize
C. orient
D. visualize

Answer: B

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 # 61
What is the purpose of a vector database in a RAG architecture?

A. to allocate resources for response generation
B. to store numerical representations of data
C. to perform model weight updates
D. to manage the context window

Answer: B

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 # 62
Which metric is commonly used to evaluate classification models?

A. Voltage
B. Packet TTL
C. Throughput only
D. Accuracy

Answer: D

Explanation:
Accuracy measures how many predictions were correct out of total predictions.

NEW QUESTION # 63
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 # 64
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