Data Mining Practice Quiz

Question 1

What is the core principle of spaced repetition as a memorization technique?

Accepted Answer

Reviewing material at increasing intervals over time.

Answer

Reviewing material immediately after learning it.

Answer

Reviewing material only when you are about to forget it.

Answer

Reviewing material in a random order to test recall.

Question 2

According to the flashcard strategy, when is the optimal time to review your flashcards for Data Mining concepts?

Accepted Answer

At increasing intervals, starting shortly after learning and extending over days and weeks.

Answer

Only the night before the exam.

Answer

Immediately after learning a new concept.

Answer

When you encounter a question you can't answer.

Question 3

How does active recall, a key flashcard technique, enhance learning?

Accepted Answer

By forcing the brain to retrieve information from memory without looking at the answer.

Answer

By passively rereading notes and textbook chapters.

Answer

By summarizing concepts in your own words without testing.

Answer

By discussing concepts with peers without self-testing.

Question 4

Which Data Mining task involves identifying groups of similar data points without prior knowledge of the group labels?

Accepted Answer

Clustering

Answer

Classification

Answer

Regression

Answer

Association Rule Mining

Question 5

In the context of association rule mining, what does the 'support' metric measure for an itemset?

Accepted Answer

The number of transactions that contain the entire itemset.

Answer

The probability that a transaction contains the itemset, given that it contains the antecedent.

Answer

The confidence that the consequent will be present if the antecedent is present.

Answer

The overall popularity of the itemset compared to all other itemsets.

Question 6

Consider a dataset of customer purchase history. If you are building a classification model to predict whether a customer will churn (stop using a service), what would be the 'target variable'?

Accepted Answer

Whether the customer churned or not

Answer

Customer's age

Answer

Customer's monthly spending

Answer

The type of service the customer uses

Question 7

What is the primary goal of data preprocessing in a Data Mining project?

Accepted Answer

To transform raw data into a clean, understandable, and usable format for modeling.

Answer

To build the final predictive model.

Answer

To visualize the relationships between all variables.

Answer

To identify the most important features for the model.

Question 8

You are analyzing transaction data from a supermarket and find the rule {Bread, Butter} -> {Milk} with a confidence of 0.7. What does this confidence value signify?

Accepted Answer

70% of transactions that contain Bread and Butter also contain Milk.

Answer

70% of all transactions contain Bread, Butter, and Milk.

Answer

70% of transactions that contain Milk also contain Bread and Butter.

Answer

70% of all items purchased are Bread, Butter, or Milk.

Question 9

Which of the following is a common technique for handling missing values in a dataset for Data Mining?

Accepted Answer

Imputing missing values with the mean or median of the column.

Answer

Removing all rows with any missing values.

Answer

Replacing all missing values with a random number.

Answer

Ignoring missing values during model training.

Question 10

In decision tree algorithms, what is the purpose of a 'pruning' step?

Accepted Answer

To reduce the complexity of the tree by removing branches that provide little predictive power, thus preventing overfitting.

Answer

To increase the depth of the tree to capture more complex patterns.

Answer

To randomly select features at each node.

Answer

To ensure all leaf nodes contain at least 10 data points.

Question 11

When applying the flashcard method to learn the steps of the K-Means clustering algorithm, which of the following would be the most effective way to structure your review?

Accepted Answer

Create flashcards for each step (Initialization, Assignment, Update) and review them daily for a week.

Answer

Review all steps at once after learning them.

Answer

Only review the 'Update' step as it's the most complex.

Answer

Read through the steps repeatedly without actively trying to recall them.

Question 12

You are studying for your Data Mining final exam, which covers classification, clustering, and association rules. You have created flashcards for each topic. What is the best strategy for using these flashcards in the week leading up to the exam?

Accepted Answer

Review all flashcards every day, focusing on active recall and spaced repetition.

Answer

Review only the topics you find most difficult.

Answer

Review flashcards for one topic each day.

Answer

Create new flashcards for complex concepts and review those exclusively.

Question 13

Consider the task of predicting house prices based on features like size, location, and number of bedrooms. This is an example of which type of Data Mining task?

Accepted Answer

Regression

Answer

Classification

Answer

Clustering

Answer

Anomaly Detection

Question 14

You've created flashcards for the evaluation metrics of classification models (e.g., Accuracy, Precision, Recall, F1-score). You notice you consistently get questions about Precision and Recall wrong. What study strategy should you employ based on the flashcard principles?

Accepted Answer

Increase the frequency of reviewing Precision and Recall flashcards, and try different active recall methods like explaining them aloud.

Answer

Stop reviewing Precision and Recall to focus on easier concepts.

Answer

Create more flashcards for Accuracy and F1-score.

Answer

Only review these metrics when you encounter them in practice problems.

Question 15

Imagine you are tasked with identifying fraudulent credit card transactions. This task requires distinguishing between normal and fraudulent activities, which are often rare and distinct from the majority. Which Data Mining concept is most relevant here, and how would you best study it using flashcards?

Accepted Answer

Anomaly Detection (Outlier Detection); create flashcards defining what an anomaly is, common detection methods (e.g., Isolation Forest, One-Class SVM), and key evaluation metrics like precision/recall for the minority class.

Answer

Clustering; create flashcards defining cluster centroids and inertia.

Answer

Association Rule Mining; create flashcards for support, confidence, and lift.

Answer

Regression; create flashcards for R-squared and Mean Squared Error.

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