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AI 201 Knowledge Check & Self-Assessment

Test your intermediate AI knowledge and assess your readiness for advanced applications. This assessment validates your understanding of complex AI concepts and practical implementation skills.

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Content created with AI assistance - may contain errors or become outdated.

📋 Comprehensive Knowledge Check​

Section 1: Advanced AI Foundations​

1. How do different model architectures (transformer, diffusion, etc.) affect AI capabilities?

Click to reveal answer
  • Transformers: Excel at language tasks through attention mechanisms, enabling parallel processing and long-range dependencies
  • Diffusion Models: Generate high-quality images by learning to reverse noise corruption
  • Convolutional Neural Networks: Ideal for image recognition through spatial pattern recognition
  • Recurrent Neural Networks: Good for sequential data but limited by sequential processing constraints

Each architecture is optimized for specific data types and tasks.

2. What is the relationship between training data, model bias, and output quality?

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Training data directly shapes model behavior:

  • Data quality determines output accuracy and reliability
  • Data bias (demographic, cultural, temporal) gets encoded into model responses
  • Data scale affects model capabilities and generalization
  • Data recency impacts knowledge of current events and trends

Higher quality, diverse, recent training data generally produces better, less biased outputs.

3. When should you use fine-tuning vs. prompt engineering vs. RAG (Retrieval-Augmented Generation)?

a) Fine-tuning for all custom applications
b) Prompt engineering only for simple tasks
c) Choose based on data availability, update frequency, and performance requirements
d) RAG only for search applications

Details

Click to reveal answer Answer: c) Choose based on data availability, update frequency, and performance requirements

  • Prompt Engineering: First try - low cost, fast iteration, good for many tasks
  • RAG: When you need current/private data, frequent updates, or source citations
  • Fine-tuning: When you need specialized behavior, have quality training data, and can justify the cost

Section 2: Workflow Automation & Integration​

4. What are the key considerations when integrating AI into existing business workflows?

Example considerations
  • Data security and privacy compliance
  • User training and change management
  • API rate limits and cost management
  • Fallback procedures for AI failures
  • Quality control and human oversight
  • Integration with existing tools and databases
  • Performance monitoring and optimization

5. Design an API integration strategy for a customer service AI assistant.

Key components
  • Authentication: Secure API key management
  • Rate limiting: Handle request throttling gracefully
  • Error handling: Retry logic, fallback responses
  • Data flow: Customer query → context retrieval → AI processing → response formatting
  • Monitoring: Track usage, performance, and costs
  • Privacy: Data encryption, PII handling, retention policies

Section 3: Advanced Ethics & Governance​

6. How do you implement AI governance in an enterprise setting?

Core elements
  • AI Ethics Committee: Cross-functional oversight body
  • Risk Assessment Framework: Evaluate AI applications for potential harms
  • Audit Trail: Document AI decisions and data usage
  • Compliance Monitoring: Ensure adherence to regulations (GDPR, etc.)
  • Vendor Management: Evaluate third-party AI providers
  • Incident Response: Process for handling AI failures or bias issues

7. What is "AI explainability" and when is it crucial?

Click to reveal answer

AI Explainability: The ability to understand and interpret how an AI system makes decisions.

Crucial for:

  • Healthcare diagnostics
  • Financial lending decisions
  • Legal/judicial applications
  • High-stakes business decisions
  • Regulatory compliance (EU AI Act, etc.)

Less critical for:

  • Content recommendations
  • Image generation
  • General writing assistance

🛠️ Practical Skills Assessment​

Assessment 1: Advanced Prompt Engineering​

Task: Create a prompt system that can adapt its communication style based on audience.

Requirements:

  • One master prompt that works for technical, business, and general audiences
  • Include examples for each audience type
  • Test with a complex topic (e.g., machine learning, blockchain, or climate science)

Success Criteria:

  • Prompt produces appropriate complexity for each audience
  • Maintains factual accuracy across all versions
  • Uses relevant analogies and examples for each group
  • Demonstrates clear understanding of audience adaptation techniques

Assessment 2: Workflow Automation Design​

Task: Design an AI-enhanced workflow for a realistic business scenario.

Choose one scenario:

  1. Customer support ticket routing and response
  2. Content moderation for an online community
  3. Resume screening and candidate matching
  4. Financial report analysis and summary generation

Deliverables:

  • Workflow diagram showing human and AI touchpoints
  • Cost-benefit analysis (time saved, quality improvements, costs)
  • Risk mitigation strategies
  • Implementation timeline with milestones

Success Criteria:

  • Realistic assessment of AI capabilities and limitations
  • Clear integration points with existing systems
  • Appropriate human oversight and quality control
  • Consideration of edge cases and failure modes

Assessment 3: Evaluation Framework Design​

Task: Create an evaluation framework for an AI application.

Scenario: A company wants to use AI to generate product descriptions for their e-commerce site.

Requirements:

  • Define success metrics (accuracy, engagement, conversion, etc.)
  • Create test datasets and evaluation criteria
  • Design A/B testing methodology
  • Include bias detection and quality control measures

Success Criteria:

  • Comprehensive metrics covering quality, business impact, and safety
  • Realistic test data that represents edge cases
  • Clear protocols for ongoing monitoring and improvement
  • Cost-effective evaluation approach that scales

🎓 Readiness Assessment​

Ready for Advanced Applications?​

Rate your confidence (1-5 scale) in these areas:

Technical Understanding:

  • Model architectures and their use cases (1-5)
  • Training data impact on model behavior (1-5)
  • API integration and technical implementation (1-5)
  • Performance evaluation and optimization (1-5)

Strategic Implementation:

  • Business case development for AI projects (1-5)
  • Cost-benefit analysis and ROI calculation (1-5)
  • Risk assessment and mitigation planning (1-5)
  • Change management and user adoption (1-5)

Ethics and Governance:

  • Bias detection and mitigation strategies (1-5)
  • Privacy and security considerations (1-5)
  • Regulatory compliance requirements (1-5)
  • AI explainability and transparency (1-5)

Scoring Guide​

Advanced Practitioner (4-5 average): Ready for complex AI implementations and leadership roles
Intermediate User (3-4 average): Ready for supervised advanced projects
Developing Skills (2-3 average): Focus on hands-on practice and mentored projects
Foundation Building (1-2 average): Review AI 201 materials and complete additional exercises

📚 Next Steps Based on Assessment​

If you scored 4-5 average​

  • Progress to Model Context Protocol (MCP) for advanced AI orchestration
  • Consider specializing in AI governance or enterprise implementation
  • Explore cutting-edge applications in your field of interest

If you scored 3-4 average​

  • Complete additional hands-on exercises in weak areas
  • Seek mentorship or guided projects for practical experience
  • Focus on one advanced area (automation, ethics, or technical depth)

If you scored 2-3 average​

  • Revisit AI 201 modules where you scored lowest
  • Complete all hands-on exercises with additional practice
  • Consider finding a study partner or joining an AI learning community

If you scored 1-2 average​

  • Return to AI 101 for foundational review
  • Focus on hands-on practice with basic AI tools
  • Build confidence with simpler applications before advancing

🔄 Continuous Learning​

AI evolves rapidly. Even advanced practitioners should:

  • Follow AI research developments and new model releases
  • Participate in AI communities and professional networks
  • Regularly reassess and update skills based on industry changes
  • Contribute to open source projects or share knowledge with others

Remember: This assessment is a learning tool, not a test. Use the results to guide your continued AI education and practical application.