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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 15% | - Prompt design principles and best practices - Defensive prompting and error mitigation - Advanced prompting techniques |
| Generative AI Models | 20% | - Model hosting options and trade-offs - Generative AI model families and use cases - Context windows, token limits and response management - Model selection and evaluation in AI hubs |
| Data Research and Analysis | 15% | - Data quality, bias and validation - Data visualization and insight generation - Data collection, preparation and preprocessing |
| AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes |
| Agentic AI | 15% | - Human-in-the-loop strategies - Agent design principles and capabilities - Model Context Protocol (MCP) framework - Agentic AI vs Generative AI use cases |
| AI Ethics and Security | 20% | - Compliance and governance frameworks - Responsible AI principles - Data privacy and security in AI systems - AI-specific threats, risks and misinformation |
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NEW QUESTION # 54
A developer is creating an agent that must transfer data from a GitHub repository into a database. The GitHub API returns a nested JSON object, whereas the database requires a flat CSV format. What step is necessary for the agentic workflow to handle this?
Answer: C
Explanation:
Data transformation and schema mapping are essential integration processes that convert information between different representations while preserving the structure and meaning required by the destination system.
Option C is correct because the agent must convert nested JSON data into a flattened tabular representation compatible with the target CSV schema.
JSON supports hierarchical objects, nested properties, and arrays, while CSV represents data in rows and columns.
For example, a GitHub API response may contain repository metadata, contributor details, and nested owner information.
The agent can extract selected fields, map nested properties to flat column names, and generate CSV records matching the database import requirements.
Option A is incorrect because ignoring schema requirements may cause invalid or unusable database records.
Option B is incorrect because selecting an LLM capable of understanding both formats does not eliminate the need for explicit mapping and validation.
Option D is incorrect as the best approach because changing the target database schema is unnecessary when the source data can be transformed appropriately.
A robust transformation stage should validate required fields, handle missing values, define array-flattening rules, preserve data types, and escape CSV content correctly.
These controls improve interoperability and reduce ingestion errors.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; AI Systems Integration with APIs; Objective 6.5 - Data transformation and mapping within AI agents; Objective 4.2 - Formatting and transformation.
NEW QUESTION # 55
A customer support team currently uses a chatbot that delivers AI-generated responses to common FAQs using a knowledge base. They want to enhance it into a system that can also automatically process customer refunds and update shipping addresses. What shift in AI architecture does this represent?
Answer: A
Explanation:
Agentic AI extends conventional generative AI capabilities by enabling systems to plan and execute actions through interaction with external tools, APIs, and enterprise applications. Option C is correct because the customer support system is evolving from generating informational responses to performing operational transactions.
A conventional generative AI chatbot can answer questions using pretrained knowledge or retrieved documentation.
However, processing refunds and modifying shipping addresses require the system to interact with transactional business services.
For example, an agentic customer support assistant may authenticate a customer, retrieve an order, check refund eligibility, initiate an authorized refund, and provide a confirmation.
These activities require tool access, workflow state management, validation, and potentially human approval.
Option A is incorrect because a chatbot can already use RAG to retrieve information from a knowledge base without possessing autonomous transaction capabilities.
Option B is incorrect because switching from local to cloud hosting does not inherently introduce agentic behavior.
Option D is incorrect because local hosting similarly does not establish autonomous workflow execution.
Agentic systems require stronger governance because they can affect real business records and financial transactions.
Appropriate controls include authorization, transaction limits, audit logging, and Human-in-the-Loop approval for high-risk operations.
Specific Cisco AITECH Course Guide References: AI-Powered Workflows and Agentic AI; AI Systems Integration with APIs; Objective 6.1 - Differentiate agentic AI from generative AI; Objective 6.2 - Autonomous capabilities and orchestration.
NEW QUESTION # 56
Drag and drop the use case from the left onto the AI system type on the right.
Answer:
Explanation:
Explanation:
Correct Drag-and-Drop Placement:
Generative AI
Drafting a technical summary of a meeting transcript for content creation.
Producing realistic synthetic datasets to validate software edge cases during testing.
Agentic AI
Executing a multi-step sequence to autonomously manage database record deletions.
Observing a failed build, analyzing logs, and deciding which diagnostic tool to run next.
Generative AI and Agentic AI represent different approaches to applying artificial intelligence, particularly regarding content creation, autonomous decision-making, and workflow execution.
1. Generative AI - Technical Summary: Generative AI produces new content based on supplied information.
Summarizing meeting transcripts involves processing existing text, identifying important information, and generating a coherent technical summary. The task does not require autonomous execution of external actions.
2. Generative AI - Synthetic Test Data: Generative AI can create realistic artificial datasets to support software testing. These datasets can simulate normal transactions, boundary conditions, and exceptional scenarios without exposing actual customer information.
3. Agentic AI - Database Record Deletion: Agentic AI can coordinate multiple actions toward a defined objective. Managing database deletions may involve identifying qualifying records, validating deletion criteria, invoking database tools, and verifying results. Production deletions should require appropriate human authorization.
4. Agentic AI - Build Troubleshooting: An agentic system can observe a failed software build, analyze diagnostic logs, select troubleshooting tools, and adapt its next action based on execution results. This demonstrates iterative, goal-directed problem-solving.
The fundamental distinction is that Generative AI primarily creates content, whereas Agentic AI coordinates decisions and actions to accomplish operational goals.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI-Driven Software Engineering
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.1: Differentiate Agentic AI from Generative AI use cases
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration
* Exam Objective 5.1: AI across the software development lifecycle
Cisco's published AITECH blueprint explicitly covers these distinctions.
NEW QUESTION # 57
What does NLP stand for in AI?
Answer: B
Explanation:
NLP enables computers to understand and generate human language.
NEW QUESTION # 58
Which activity is an indirect prompt injection attack?
Answer: D
Explanation:
An indirect prompt injection occurs when malicious instructions are hidden in external content that the AI reads or retrieves, causing the model to follow attacker-controlled instructions from a poisoned data source.
NEW QUESTION # 59
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