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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
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
Prompt Engineering15%- Principles and patterns
  • 1. Constraints
  • 2. Instructions
  • 3. Roles
- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Security and safety
  • 1. Prompt injection attacks
  • 2. Improving response accuracy
  • 3. Reducing hallucinations
  • 4. Defensive prompting methods
Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Grounding AI outputs in external knowledge
  • 3. Vector databases
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Common use cases
  • 1. Code generation
  • 2. Content creation
  • 3. Text summarization
Data Research and Analysis- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis
Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging

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

NEW QUESTION # 50
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: C

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.


NEW QUESTION # 51
What is aggregation bias in the context of AI-assisted data analysis?

Answer: B

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.


NEW QUESTION # 52
What is the main advantage of cloud-based AI services?

Answer: C

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


NEW QUESTION # 53
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?

Answer: A

Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.


NEW QUESTION # 54
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

Answer: B

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.


NEW QUESTION # 55
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