810-110 Simulation Questions, 810-110 Exam Registration

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Code and Workflow Optimization		- Workflow automation 1. Task optimization 2. Process automation - AI-assisted code development 1. Code review and debugging 2. Code generation
Topic 2: Generative AI Models	20%	- Model hosting strategies 1. Cloud-based vs. local deployments 2. Cost, latency, privacy, and scalability considerations - Context windows and token management 1. Token limits 2. Response management - Model selection 1. Selection from AI hubs 2. Reasoning and multimodal tasks - Retrieval Augmented Generation (RAG) 1. Embeddings 2. Grounding AI outputs in external knowledge 3. Vector databases - Major generative model families 1. Large language models (LLMs) 2. Diffusion models - Common use cases 1. Code generation 2. Content creation 3. Text summarization
Topic 3: Agentic AI	20%	- AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration - Differentiation from Generative AI 1. Use cases comparison 2. Autonomous capabilities - Model Context Protocol (MCP) 1. MCP framework primitives 2. Context in agentic AI - Data transformation and mapping 1. Within AI Agents
Topic 4: Prompt Engineering	15%	- Principles and patterns 1. Constraints 2. Instructions 3. Roles - Security and safety 1. Improving response accuracy 2. Prompt injection attacks 3. Reducing hallucinations 4. Defensive prompting methods - Prompting techniques 1. Chained prompting 2. Few-shot prompting 3. Iterative prompting - Prompt structures 1. Image generation 2. Audio generation 3. Text generation
Topic 5: AI Ethics and Security		- Security threats and mitigations 1. Data privacy 2. Model security - Ethical considerations in AI 1. Bias and fairness 2. Transparency and explainability
Topic 6: Data Research and Analysis		- Data analysis techniques 1. Statistical analysis 2. Data visualization - Data collection and preparation 1. Data cleaning and preprocessing 2. Data gathering methodologies

>> 810-110 Simulation Questions <<

810-110 - Authoritative Cisco AI Technical Practitioner Simulation Questions

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Cisco AI Technical Practitioner Sample Questions (Q70-Q75):

NEW QUESTION # 70
Which AI model type is best suited for image recognition tasks?

A. Linear Regression
B. Decision Tree only
C. K-Means
D. Convolutional Neural Network (CNN)

Answer: D

Explanation:
CNNs are optimized for image and visual pattern recognition.

NEW QUESTION # 71
Which activity is an indirect prompt injection attack?

A. A user types a malicious command.
B. AI reads a poisoned data source.
C. A prompt forces AI to ignore safety filters.
D. The model generates a false fact.

Answer: B

Explanation:
An indirect prompt injection occurs when malicious instructions are hidden in external content that the AI reads or retrieves, causing the model to follow attacker-controlled instructions from a poisoned data source.

NEW QUESTION # 72
What is the main advantage of cloud-based AI services?

A. Scalability and flexible compute resources
B. No need for networking
C. Unlimited free hardware
D. Elimination of cybersecurity risks

Answer: A

Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.

NEW QUESTION # 73
What is the role of an MCP server in the MCP open standard?

A. to provide the GUI for the end-user interaction
B. to expose tools and resources to an AI model
C. to host the LLM and perform token inference
D. to route requests between different AI providers

Answer: B

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.

NEW QUESTION # 74
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 dynamic system where branching criteria evolve over time using optimization algorithms.
C. A workflow state that pauses execution until a human manually determines the next step.
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 # 75
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