List of practice Questions

What is Phishing, and how can users identify a suspicious email?
Describe the role of a Firewall in protecting a private network.
What is Digital Steganography, and how does it differ from encryption?
Explain the concept of Two-Factor Authentication (2FA) and its importance.
Explain the purpose of the Train-Test Split technique in evaluating model performance.
Explain the biological motivation behind Artificial Neural Networks (ANN).
Define Computer Vision and list its primary pipeline stages (Acquisition, Preprocessing, Feature Extraction).
Explain the Turing Test and its significance in measuring machine intelligence.
Discuss the major ethical issues in AI, such as Bias, Privacy, and Accountability.
What is a Confusion Matrix, and how do you calculate Accuracy, Precision, and Recall from it?
Define the three pillars of the CIA Triad.
Explain the 10 stages of the Foundational Methodology for Data Science.
Why is the "Business Understanding" stage considered the most critical phase of an AI project?
Define the 5 Vs of Big Data: Volume, Velocity, Variety, Veracity, and Value.
Describe the five stages of Design Thinking: Empathize, Define, Ideate, Prototype, and Test.
Differentiate between Supervised and Unsupervised Learning with real-world examples.
Explain the concept of Linear Regression and the role of the Mean Squared Error (MSE) loss function.
What is the Gradient Descent algorithm and how does it help in model optimization?
Explain the concept of Data Normalization and why it is used in scaling features.
Define Mean, Median, and Mode and explain which is most affected by outliers.
What is a Hypothesis Test, and what do the Null and Alternative hypotheses represent?
What is a DataFrame in Pandas, and how does it differ from a Series?
How do you perform a Join and Merge operation on two different datasets in Python?
What are the key principles of Effective Data Visualization?
When should you use a Box Plot versus a Bar Chart?