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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Research and Analysis | 15% | - Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing |
| Topic 2: Agentic AI | 15% | - Agentic AI vs Generative AI use cases - Agent design principles and capabilities - Model Context Protocol (MCP) framework - Human-in-the-loop strategies |
| Topic 3: Prompt Engineering | 15% | - Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices |
| Topic 4: AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement |
| Topic 5: AI Ethics and Security | 20% | - AI-specific threats, risks and misinformation - Responsible AI principles - Data privacy and security in AI systems - Compliance and governance frameworks |
| Topic 6: Generative AI Models | 20% | - Generative AI model families and use cases - Context windows, token limits and response management - Model selection and evaluation in AI hubs - Model hosting options and trade-offs |
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NEW QUESTION # 98
Compared to a typical generative AI use case, which of the following represents an agentic AI use case?
Answer: A
Explanation:
Agentic AI refers to systems capable of pursuing objectives through coordinated, multi-step operations involving planning, reasoning, tool invocation, feedback evaluation, and authorized actions. Option C is correct because autonomously fixing a software build requires the AI system to investigate a problem and execute corrective operations rather than simply generate content.
An agentic development assistant might begin by examining a failed continuous integration pipeline, retrieving build logs, identifying the probable source of the failure, and inspecting relevant source files.
The agent can then propose or apply code changes, execute tests, evaluate the results, and continue troubleshooting until the build succeeds or a predefined execution limit is reached.
This represents an iterative observe-plan-act-evaluate workflow.
Option A is incorrect because generating a meeting summary is primarily a content synthesis task.
Option B is incorrect because writing a poem represents creative text generation.
Option D is incorrect because translating an email is a conventional language transformation task.
Although traditional generative AI capabilities can support every stage of an agentic workflow, the distinguishing characteristic is the ability to coordinate actions toward an operational objective.
Production agents should implement permissions, execution limits, audit trails, and human approval requirements for consequential changes.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; AI-Driven Software Engineering; Exam Objective 6.1 - Agentic versus generative AI use cases; Objective 6.2 - Autonomous capabilities and orchestration.
Cisco AI Technical Practitioner (810-110 AITECH) - Questions 51-75
The 25 questions are continued in the existing sequence and evaluated against Cisco's official AITECH v1.0 exam blueprint and course outline. These cover Generative AI Models, Prompt Engineering, Ethics and Security, Data Research and Analysis, Development and Workflow Automation, and Agentic AI.
NEW QUESTION # 99
During the Requirements and Planning phase of the SDLC, a project manager has hours of unprocessed meeting transcripts for a new branch-office rollout. Which AI capability delivers value during this phase?
Answer: C
Explanation:
The Requirements and Planning phase of the Software Development Lifecycle focuses on identifying stakeholder needs, defining functional expectations, documenting constraints, and establishing measurable acceptance criteria. Option A is correct because AI can transform unstructured meeting transcripts into organized requirements artifacts suitable for subsequent project planning and development activities.
For example, meeting transcripts discussing a branch-office rollout may contain requirements concerning WAN connectivity, network segmentation, wireless coverage, security policies, and service availability.
An AI assistant can extract these requirements, identify dependencies, organize them into structured user stories, and propose acceptance criteria.
A user story might specify that branch employees require secure wireless connectivity, with acceptance criteria defining authentication, coverage, and performance expectations.
Option B is not the best answer because PII identification supports privacy and compliance but does not directly satisfy the primary requirements engineering objective.
Option C is incorrect because deployment scripts belong primarily to implementation and deployment activities.
Option D is incorrect as the best answer because wireframe generation is associated more closely with design and prototyping.
AI-generated requirements should be reviewed by stakeholders to resolve ambiguity, validate assumptions, and confirm that the resulting documentation accurately represents business needs.
Specific Cisco AITECH Course Guide References:
* AI-Driven Software Engineering
* AI-Powered Discovery and Synthesis
* Exam Objective 5.1: AI across the SDLC, particularly requirements and prototyping
* Exam Objective 4.4: AI-assisted research and content drafting
NEW QUESTION # 100
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?
Answer: C
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 # 101
What occurs when the amount of input text supplied to a large language model goes beyond the size of its context window?
Answer: C
Explanation:
A context window represents the maximum number of tokens that a large language model can process within a single inference context, subject to the model's architecture and deployment configuration. Option B is the intended answer because many conversational AI systems manage excessive context by truncating older information to preserve more recent messages.
For example, when a conversation becomes longer than the permitted context window, a sliding-window truncation mechanism may remove the earliest messages, causing the model to lose access to previously supplied details.
This can affect response accuracy, continuity, and the model's ability to follow instructions or reference information from earlier interactions.
Option A is incorrect because a model cannot automatically expand its architectural context capacity simply because the input exceeds its limit.
Option C is incorrect because overflow information is not inherently cached for later processing. Prompt caching and context management are distinct mechanisms.
Option D is incorrect because exceeding token capacity does not automatically trigger a change to another inference algorithm.
An important implementation distinction is that truncation is not universal. Some APIs reject oversized requests, while others summarize earlier context or apply configurable truncation strategies.
Therefore, Option B accurately represents a common context-management behavior rather than a mandatory characteristic of every LLM.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI Architect's Toolkit
* Exam Objective 1.3: Context windows, token limits, and response management
* Exam Objective 5.4: Context-window management and output quality
Cisco includes context-window management as a fundamental learning objective. AWS documentation independently confirms sliding-window truncation and alternative context-management strategies.
Cisco Learning Content+2
NEW QUESTION # 102
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?
Answer: A
Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.
NEW QUESTION # 103
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