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| Section | Weight | Objectives |
|---|---|---|
| Agentic AI Systems | - Agent architecture
| |
| Ethics and Security | 15% | - Data privacy and governance
|
| AI for Data Research and Analysis | - Data exploration and transformation
| |
| AI for Code and Workflow Optimization | - Automation workflows
| |
| Generative AI Models | 20% | - Context and retrieval systems
|
| Prompt Engineering | 15% | - Prompt design principles
|
>> 810-110 Hot Spot Questions <<
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NEW QUESTION # 66
A practitioner is using AI to analyze customer support tickets and usage data in order to compare complaint patterns across different customer segments. Following the 4-step EDA (Exploratory Data Analysis) methodology, what should be done immediately after producing a high-level summary of the feedback data?
Answer: B
Explanation:
Exploratory Data Analysis (EDA) is a systematic process for understanding datasets, identifying recurring patterns, investigating relationships, and developing evidence-based hypotheses. Option D is correct when following the four-step sequence Orient # Visualize # Correlate # Hypothesize.
The initial high-level summary represents the Orient stage, during which the practitioner establishes an overview of customer feedback, dominant complaint categories, and customer segments.
After completing this orientation, the next step is Visualize. During this stage, AI-assisted tools can generate bar charts, frequency distributions, segmented comparisons, and other visual representations that make underlying patterns easier to identify.
For example, a bar chart might reveal that enterprise customers report substantially more integration problems than small-business customers. Such observations establish a basis for subsequent analysis.
Option A is incorrect because orientation has already been completed through the high-level summary.
Option B is incorrect because correlation belongs to a later stage, where analysts investigate relationships across customer characteristics, complaint patterns, and usage metrics.
Option C is incorrect under the stated sequence because hypothesis generation follows exploration and correlation. Developing explanations prematurely can introduce confirmation bias.
The workflow helps analysts move from initial observations toward defensible, testable business hypotheses.
Specific Cisco AITECH Course Guide References:
* AI for Data Engineering and Exploration
* AI-Powered Discovery and Synthesis
* Exam Objective 4.1: AI's role in exploratory data analysis
Cisco's official blueprint confirms EDA coverage under Objective 4.1. The four-step ordering is supported by a separate published methodology description, not explicitly enumerated in the public Cisco blueprint.
Answer-key validation note: Several third-party AITECH question banks identify Hypothesize (C) as the answer to this question. That conflicts with the published four-stage sequence, in which Visualize (D) immediately follows Orient. Therefore, D is the methodologically consistent selection; the exact Cisco course- book key has not been independently established.
NEW QUESTION # 67
Which AI concern focuses on protecting sensitive information?
Answer: B
Explanation:
AI systems must ensure sensitive data is securely handled and protected.
NEW QUESTION # 68
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?
Answer: D
Explanation:
Human-in-the-Loop (HITL) is an AI governance mechanism in which human reviewers participate in validating, supervising, or authorizing AI-generated decisions, particularly when those decisions may produce significant consequences. Option D is correct because HITL provides human oversight to evaluate the validity, appropriateness, and reliability of AI recommendations before consequential actions occur.
In healthcare, an AI system may analyze diagnostic information and recommend a treatment. A qualified healthcare professional must assess the recommendation against clinical evidence and patient circumstances.
Similarly, in financial services, an AI system might recommend rejecting a credit application. Human review can examine supporting evidence, regulatory requirements, and possible discriminatory outcomes.
Option A is incorrect because protecting sensitive customer information primarily requires privacy controls, encryption, access restrictions, and appropriate data governance.
Option B is incorrect because unauthorized access prevention depends on authentication, authorization, and security enforcement mechanisms.
Option C is incorrect because human intervention generally introduces an additional review stage rather than maximizing response speed.
HITL supports accountability, risk reduction, regulatory oversight, and responsible deployment. However, human review does not automatically guarantee decision correctness; it must involve qualified reviewers with sufficient information and authority.
Specific Cisco AITECH Course Guide References:
* Generative AI Security and Privacy Fundamentals
* AI-Powered Workflows and Agentic AI
* Exam Objective 3.4: AI governance, policy, risk management, and compliance
* Exam Objective 6.4: Human-in-the-Loop strategies
Cisco includes both governance and HITL as explicit exam objectives.
NEW QUESTION # 69
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?
Answer: C
Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.
NEW QUESTION # 70
In the MCP (Model Context Protocol) open standard, what function does an MCP server perform?
Answer: A
Explanation:
Model Context Protocol (MCP) is an open standard that enables AI applications to interact with external tools, data sources, and services through standardized interfaces. Option D is correct because an MCP server exposes capabilities that an AI application can discover and use through an MCP client.
MCP follows a client-server architecture in which the server publishes supported capabilities, including tools, resources, and prompts.
Tools represent executable operations, such as querying an API, retrieving network telemetry, or performing a calculation. Resources provide contextual information that applications can read, while prompts define reusable interaction templates.
For example, an enterprise MCP server might expose a network inventory lookup tool and a resource containing device configuration information.
An AI assistant connected through an MCP client can discover these capabilities and invoke authorized operations when completing a technical task.
Option A is incorrect because an MCP server does not inherently host a language model or execute inference.
Option B is incorrect because end-user interfaces are generally provided by the AI host application.
Option C is incorrect because routing requests among model providers is not MCP's defining purpose.
MCP standardizes capability discovery and interaction, reducing the need to create separate proprietary integrations for every external system.
Specific Cisco AITECH Course Guide References:
* AI Systems Integration with APIs
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.3: MCP framework primitives in agentic AI
The MCP specification confirms that servers expose callable tools and associated schemas.
NEW QUESTION # 71
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