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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI for Code and Workflow Optimization	15%	- Integration of AI tools in development processes - AI-assisted code generation and improvement - Automation and workflow enhancement
Topic 2: AI Ethics and Security	20%	- AI-specific threats, risks and misinformation - Compliance and governance frameworks - Responsible AI principles - Data privacy and security in AI systems
Topic 3: Data Research and Analysis	15%	- Data visualization and insight generation - Data collection, preparation and preprocessing - Data quality, bias and validation
Topic 4: Prompt Engineering	15%	- Advanced prompting techniques - Prompt design principles and best practices - Defensive prompting and error mitigation
Topic 5: Agentic AI	15%	- Model Context Protocol (MCP) framework - Agent design principles and capabilities - Agentic AI vs Generative AI use cases - Human-in-the-loop strategies
Topic 6: Generative AI Models	20%	- Generative AI model families and use cases - Model selection and evaluation in AI hubs - Context windows, token limits and response management - Model hosting options and trade-offs

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Cisco AI Technical Practitioner Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

A. data poisoning
B. gradient inversion
C. direct prompt injection
D. algorithmic bias

Answer: A

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.

NEW QUESTION # 63
Which metric is commonly used to evaluate classification models?

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

Answer: C

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

NEW QUESTION # 64
In an AI-powered automation workflow, what is deterministic branching?

A. A dynamic system where branching criteria evolve over time using optimization algorithms.
B. A process where the AI model autonomously selects the next step based on its internal confidence score.
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 # 65
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to reduce the token count of the agent's system prompt
B. to encrypt the payload before it is sent to the external service
C. to define the required parameters and data types the agent must return
D. to map relationships between multiple tools in an agent workflow

Answer: C

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

NEW QUESTION # 66
What is the role of embeddings in AI systems?

A. Compressing logs
B. Encrypting traffic
C. Assigning IP addresses
D. Representing data numerically in vector space

Answer: D

NEW QUESTION # 67
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