Decision-Making Programs Quiz: High School Data Science and AI

📚 Quick Study Guide

• 🤖 Decision-Making Programs: Algorithms designed to automate or assist in making choices based on available data.
• 📊 Data Preprocessing: Essential step involving cleaning, transforming, and preparing data for use in decision-making models.
  • 🧹 *Cleaning:* Handling missing values and outliers.
  • ⚙️ *Transformation:* Scaling or normalizing data.
• 🌲 Decision Trees: A tree-like model that uses a series of binary decisions to classify or predict outcomes.
• 🧮 Expected Value: The weighted average of possible outcomes, calculated as $\sum P(x) * x$, where $P(x)$ is the probability of outcome $x$.
• 💰 Cost-Benefit Analysis: A method for evaluating decisions by comparing the total expected costs to the total expected benefits.
  • ✅ *Benefits:* Positive outcomes or gains.
  • ❌ *Costs:* Negative outcomes or expenses.
• ⚖️ Ethical Considerations: Ensuring fairness, transparency, and accountability in decision-making programs to avoid bias and discrimination.

🤔 Practice Quiz

1. Question 1: What is the primary purpose of decision-making programs in data science and AI?
   1. A. To visualize data trends.
   2. B. To automate or assist in making choices.
   3. C. To store large datasets.
   4. D. To encrypt sensitive information.
2. Question 2: Which of the following is a crucial step in data preprocessing for decision-making models?
   1. A. Ignoring missing values.
   2. B. Scaling or normalizing data.
   3. C. Using raw data directly without cleaning.
   4. D. Avoiding data transformation.
3. Question 3: What type of model uses a series of binary decisions to classify or predict outcomes?
   1. A. Neural Network.
   2. B. Decision Tree.
   3. C. Support Vector Machine.
   4. D. Linear Regression.
4. Question 4: How is Expected Value calculated?
   1. A. $\sum x / P(x)$
   2. B. $\sum P(x) + x$
   3. C. $\sum P(x) * x$
   4. D. $\sum x - P(x)$
5. Question 5: In cost-benefit analysis, what are 'benefits' primarily referring to?
   1. A. Negative outcomes or expenses.
   2. B. Positive outcomes or gains.
   3. C. Neutral results.
   4. D. Unquantifiable factors.
6. Question 6: What is a key ethical consideration when developing decision-making programs?
   1. A. Maximizing computational speed.
   2. B. Ensuring fairness and transparency.
   3. C. Ignoring data privacy.
   4. D. Avoiding model complexity.
7. Question 7: Which of the following is a component of data cleaning during preprocessing?
   1. A. Ignoring outliers.
   2. B. Creating more missing values.
   3. C. Handling missing values.
   4. D. Avoiding data transformation.