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| Section | Weight | Objectives |
|---|---|---|
| AI for Code and Workflow Optimization | - Automation workflows
| |
| Ethics and Security | 15% | - Data privacy and governance
|
| AI for Data Research and Analysis | - Data exploration and transformation
| |
| Prompt Engineering | 15% | - Security and robustness
|
| Agentic AI Systems | - Agent architecture
| |
| Generative AI Models | 20% | - Model hosting and architecture
|
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NEW QUESTION # 38
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?
Answer: D
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 # 39
A developer is prototyping an AI assistant that must send a large amount of conversation history with every prompt. They observe that, as they enlarge the context window, the cost of each API call rises substantially.
What is the main reason for this increase in cost?
Answer: C
Explanation:
Cloud-hosted large language model APIs commonly use token-based pricing, where inference charges depend on the quantity of input and output tokens processed. Option C is correct because including extensive conversational history increases the number of input tokens submitted with each request, potentially increasing operational expenditure.
For example, consider an application sending 20,000 input tokens and producing 2,000 output tokens. If the provider charges $2 per million input tokens and $8 per million output tokens, the request costs approximately
$0.056 before applicable discounts or additional fees.
As conversation history grows, repeated transmission of earlier messages can create substantial cumulative costs.
Option A is incorrect because temperature is a configurable sampling parameter and does not automatically increase when a longer prompt is submitted.
Option B is incorrect as the primary explanation because longer contexts can increase computational and memory requirements, but the observed billing increase is directly explained by the growth in processed tokens.
Option D is incorrect because expanding supplied context does not automatically select a different model.
Cost-management strategies include conversation summarization, selective history retention, prompt caching where supported, and retrieval of relevant past exchanges.
Specific Cisco AITECH Course Guide References: Generative AI Ecosystem; AI Architect's Toolkit; Objective 1.3 - Context windows and token limits; Objective 5.4 - Token usage and prototyping economics.
NEW QUESTION # 40
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?
Answer: D
Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.
NEW QUESTION # 41
What distinguishes generative AI from agentic AI?
Answer: B
Explanation:
The primary distinction between generative AI and agentic AI concerns the degree of autonomy, task execution capability, and workflow orchestration. Option C is the best answer because it contrasts conventional prompt-response generation with agentic systems designed to execute multi-step decision and action cycles.
Generative AI models primarily produce content such as text, images, audio, software code, or structured data in response to input.
Agentic AI extends these capabilities by incorporating goal-directed planning, external tool invocation, environmental feedback, and iterative task execution.
For example, an agentic network operations assistant might investigate a service outage, retrieve device telemetry, identify probable causes, recommend remediation, and request authorization before executing changes.
Option A is incorrect because both conventional generative AI applications and agentic systems can use RAG and external knowledge sources.
Option B is incorrect because agentic systems are specifically designed to perform authorized actions without requiring a new user prompt at every intermediate step.
Option D is incorrect because generative models do not inherently require tools, while agentic applications frequently depend on external tools and APIs.
A technical qualification is important: generative AI is not limited to single-turn conversations. It can support multi-turn interactions. The meaningful distinction is autonomous orchestration and action-oriented execution.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.1: Differentiate agentic AI from generative AI use cases
* Exam Objective 6.2: Autonomous capabilities and orchestration
Cisco identifies agentic autonomy and workflow execution as central learning outcomes.
NEW QUESTION # 42
To reduce hallucinations in a technical report, a practitioner directs an AI to "Use only the provided text to answer, and say 'I don't know' when the information is absent." Which strategy is being applied?
Answer: A
Explanation:
Grounding constraints are instructions that limit an AI model's responses to specified evidence, authoritative documents, or permitted information sources. Option A is correct because the practitioner explicitly restricts the model to the provided text and requires acknowledgment when supporting information is unavailable.
Grounding helps reduce unsupported claims by discouraging the model from introducing unrelated facts or relying on information not established by the source material.
For example, an engineer summarizing a network security assessment may instruct an AI assistant to use only the supplied audit report and avoid inventing compliance findings or remediation details.
The instruction to acknowledge missing information is particularly important because models can otherwise produce plausible but unsupported technical statements.
Option B is incorrect because multimodal AI concerns processing multiple input types, such as text and images.
Option C is incorrect because expanding context capacity does not inherently improve factual grounding or eliminate fabricated statements.
Option D is incorrect because increasing temperature generally increases sampling variability rather than strengthening evidence-based accuracy.
Grounding instructions are valuable but insufficient by themselves. Stronger implementations verify source references, validate generated claims, and use retrieval mechanisms that supply relevant authoritative evidence.
Specific Cisco AITECH Course Guide References: Prompt Engineering for Technical Precision; Debugging and Correcting AI-Generated Outputs; Objective 2.4 - Hallucination mitigation and defensive prompting.
NEW QUESTION # 43
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