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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is the role of embeddings in AI systems?

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

Answer: B

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.

NEW QUESTION # 15
Within a multi-agent AI framework, what is the role of a coordinator agent?

A. It serves as the external data source that each agent queries for grounding information.
B. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
C. It orchestrates task delegation across specialized agents and aggregates the outputs.
D. It fine-tunes each agent's model weights to optimize performance on assigned tasks.

Answer: C

Explanation:
A coordinator agent is a supervisory component within a multi-agent AI architecture responsible for organizing work, delegating tasks, managing dependencies, and consolidating results from specialized agents.
Option A is correct because orchestration and output aggregation are defining responsibilities of a coordinator.
Multi-agent architectures distribute responsibilities among agents with different skills, tools, or operational roles.
For example, an AI-powered infrastructure assessment framework might contain a discovery agent, security assessment agent, capacity planning agent, and documentation agent.
The coordinator receives the overall objective, decomposes it into manageable subtasks, assigns responsibilities, and coordinates execution according to task dependencies.
After the specialized agents complete their assignments, the coordinator evaluates their outputs, resolves inconsistencies where possible, and consolidates the findings into a coherent response.
Option B is incorrect because replacing specialized agents with one reasoning component describes a single- agent architecture rather than multi-agent coordination.
Option C is incorrect because external knowledge sources are typically provided through databases, retrieval systems, APIs, or other tools.
Option D is incorrect because model fine-tuning is a training process involving parameter updates, not a normal coordinator-agent responsibility.
Effective coordination improves modularity, task specialization, workflow traceability, and operational control.
Specific Cisco AITECH Course Guide References:
* AI-Powered Workflows and Agentic AI
* AI Systems Integration with APIs
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration Cisco's blueprint directly identifies orchestration as an essential agentic AI competency.

NEW QUESTION # 16
When designing an AI agent for a high-stakes deployment task, which feature combination ensures that the system functions safely and predictably in a production environment?

A. Bounded goals, explicit tool definitions, cost/latency budgets, and human approval gates.
B. Unbounded token limits, parallel tool execution, and self-healing error bypasses.
C. High temperature settings, implicit tool definitions, and automated deployment triggers.
D. High temperature settings, unlimited token access, and autonomous self-correction.

Answer: A

Explanation:
Safe production deployment of agentic AI requires explicit operational boundaries, well-defined tool interfaces, resource controls, and authorization mechanisms. Option A is correct because it combines complementary safeguards that promote predictable agent behavior and limit the impact of errors.
Bounded goals restrict an agent to clearly defined objectives and prevent uncontrolled expansion of its responsibilities.
Explicit tool definitions establish permitted operations, required parameters, and expected responses. This reduces ambiguity when the agent interacts with external systems.
Cost and latency budgets control resource consumption by establishing limits on model calls, execution duration, and operational expenditure.
Human approval gates prevent consequential operations from proceeding without authorization.
For example, a production deployment agent might generate a Kubernetes configuration, validate it, run automated checks, and request engineering approval before applying the change.
Option B is incorrect because unrestricted token usage and error bypasses undermine operational control.
Option C is incorrect because high temperature and unlimited autonomy can increase behavioral variability and resource consumption.
Option D is incorrect because implicit tool definitions and uncontrolled deployment triggers increase execution risk.
These safeguards should be supplemented with observability, least-privilege permissions, rollback mechanisms, and defined failure-handling procedures.
Specific Cisco AITECH Course Guide References:
* AI-Powered Workflows and Agentic AI
* Generative AI Security and Privacy Fundamentals
* Exam Objective 6.2: AI agent design principles and orchestration
* Exam Objective 6.4: Human-in-the-Loop strategies

NEW QUESTION # 17
Which AI capability supports the Design and Prototyping phase of the SDLC when establishing initial API contracts and endpoint structures for a new microservice?

A. Analyzing requirements to suggest initial technical blueprints and API endpoint structures.
B. Automatically deploying the service to a production Kubernetes cluster.
C. Translating raw meeting transcripts into structured user stories and acceptance criteria.
D. Generating comprehensive unit and integration tests for the final production codebase.

Answer: A

Explanation:
The Design and Prototyping phase of the Software Development Lifecycle translates approved requirements into preliminary technical architectures, interface definitions, and implementation specifications. Option B is correct because AI can analyze functional requirements and recommend initial system blueprints, API contracts, and endpoint structures before full development begins.
For example, when designing a microservice for inventory management, an AI assistant can propose REST endpoints such as GET /products, POST /products, and PATCH /products/{id}.
It can also recommend request and response schemas, HTTP status codes, authentication requirements, and suitable service boundaries.
AI-generated OpenAPI specifications can provide an initial contract for collaboration between frontend developers, backend engineers, and integration teams.
Option A is incorrect because deploying a service to Kubernetes belongs primarily to the deployment phase.
Option C is incorrect because comprehensive validation of finalized production code is associated with testing activities.
Option D is incorrect because extracting user stories and acceptance criteria from meeting transcripts primarily supports requirements engineering.
AI-assisted prototyping accelerates architectural exploration, but generated designs require validation against security, scalability, maintainability, and integration requirements.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI Systems Integration with APIs
* Exam Objective 5.1: AI across SDLC phases
* Exam Objective 5.2: AI-assisted code generation and rapid prototyping

NEW QUESTION # 18
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. To provide administrative credentials required to bypass MFA requirements
B. To allow the AI agent to bypass the integrity constraints of the database
C. To allow the AI agent to automatically resolve transaction deadlocks
D. To serve as a safety gate against unintended data modifications

Answer: D

Explanation:
Human-in-the-Loop (HITL) is an AI governance and workflow design approach that requires human oversight or authorization before selected automated actions are executed. Option D is correct because providing an AI agent with production database write access introduces the possibility of unintended, inaccurate, or destructive data modifications.
For example, an AI agent investigating duplicate customer records might propose deleting records or merging database entries.
Without an authorization checkpoint, the agent could execute an incorrect operation that damages critical data or affects dependent applications.
A HITL workflow can require an authorized database administrator to inspect the proposed changes, evaluate their impact, and approve execution.
Option A is incorrect because database integrity constraints are essential safeguards that should remain enforced.
Option B is incorrect because resolving transaction deadlocks is primarily a database transaction management concern.
Option C is incorrect because MFA requirements must not be bypassed. Human approval should complement authentication and authorization mechanisms.
HITL controls should be supported by least-privilege permissions, transaction validation, audit logging, rollback capabilities, and clearly defined approval thresholds.
Human oversight is especially important for operations involving deletions, bulk updates, financial records, or sensitive customer information.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; Generative AI Security and Privacy Fundamentals; Exam Objective 6.4 - HITL strategies; Objective 3.4 - Governance and risk management.
Cisco Learning Content

NEW QUESTION # 19
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