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AI/Machine Learning

AI/Machine Learning

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Match each region with its defining AI governance approach.

China

USA

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Regarding explainable AI (XAI) techniques, which statements are accurate?

LIME and SHAP provide feature importance scores indicating which inputs most influenced model outputs

100%

The EU AI Act requires high-risk systems to explain their decisions to affected individuals

100%

Counterfactual explanations show what input changes would alter the model's decision

100%

XAI techniques can produce misleading explanations that increase user trust even when models are wrong

100%

Different XAI methods applied to the same model and input always produce consistent explanations

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Which of the following statements about AI accessibility and ethics are true?

AI tools are always neutral and objective

Generative AI tools can sometimes produce biased or inaccurate content

100%

Ethical AI practices include transparency and accountability

100%

Free web-based AI tools increase accessibility for education

100%

Responsible AI use requires understanding data privacy and bias

100%

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Match each AI category with a representative tool.

Text generation

Image generation

Machine learning framework

Speech recognition

Code assistance

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In AI agent development, the Perception Module is only relevant for agents processing visual or audio data; text-based agents do not require a perception component.

True

False

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The ReAct architecture typically requires more LLM API calls than Plan-and-Execute for the same task because ReAct reasons at each step while Plan-and-Execute creates the complete plan upfront with a single planning call.

Concerning the Reflection/Learning component of AI agents, which mechanisms are correctly described?

Performance metrics include success rate tracking and efficiency measurements

100%

Self-evaluation involves the agent assessing whether it achieved its goals

100%

Reflection eliminates the need for the reasoning engine

In-context learning is the only approach to agent improvement

Error analysis identifies what went wrong to adjust future strategies

100%

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Connect pairs:

Feature filtering

High-frequency N-grams (stop-words)

Data sparsity

Low-frequency N-grams

N-gram model

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Text-based sentiment analysis, which calculates the total sentiment score by summing up all sentiment scores of detected words, can handle complex contexts, sarcasm, idioms, negations, and domain-specific language.

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