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Unit 1: Introduction and Data Preparation - Practice Quiz

INT234 — Predictive Analytics 50 Questions
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1 What is the primary goal of Predictive Analytics?

A. To prescribe the best course of action
B. To store large amounts of data
C. To predict future outcomes based on historical data
D. To describe what happened in the past

2 Which of the following is NOT a phase in the standard predictive analytics lifecycle?

A. Deployment
B. Model Building
C. Data Preparation
D. Hardware Manufacturing

3 Descriptive Analytics differs from Predictive Analytics because Descriptive Analytics focuses on:

A. Forecasting future trends
B. Summarizing past events
C. Creating machine learning models
D. Optimizing decision making

4 Which of the following is a common application of Predictive Analytics?

A. Real-time operating system scheduling
B. Generating an annual report
C. Creating a database schema
D. Credit scoring

5 In the context of Machine Learning, what is 'Training Data'?

A. Data used to teach the algorithm patterns
B. Future data that has not occurred yet
C. Data that has been corrupted
D. Data used to evaluate the final model

6 Which type of analytics answers the question 'What should we do about it'?

A. Descriptive Analytics
B. Prescriptive Analytics
C. Diagnostic Analytics
D. Predictive Analytics

7 Machine Learning is best described as:

A. Data storage optimization
B. A subset of AI where computers learn from data without explicit programming
C. Strictly using statistical regression only
D. Hard-coding rules for every possible scenario

8 What is the 'Target Variable' in a predictive model?

A. The variable being predicted
B. The index of the dataset
C. The noise in the data
D. The variable used to predict

9 Which of the following is a type of Supervised Learning?

A. Regression
B. Association Rule Learning
C. Clustering
D. Dimensionality Reduction

10 In Supervised Learning, the dataset must contain:

A. Unlabeled data
B. Labeled data (Input features and Output labels)
C. Only images
D. Only input features

11 Unsupervised Learning differs from Supervised Learning because it:

A. Is only used for text data
B. Uses labeled data
C. Predicts a specific target
D. Finds hidden patterns in unlabeled data

12 Predicting the price of a house based on its square footage is an example of:

A. Regression
B. Clustering
C. Reinforcement Learning
D. Classification

13 Predicting whether an email is 'Spam' or 'Not Spam' is an example of:

A. Clustering
B. Classification
C. Regression
D. Principal Component Analysis

14 Which of the following is an Unsupervised Learning algorithm?

A. Decision Trees
B. Linear Regression
C. Logistic Regression
D. K-Means Clustering

15 What is the main objective of Clustering?

A. To predict a continuous value
B. To classify data into known categories
C. To reduce the number of rows in a table
D. To group similar data points together

16 Reinforcement Learning involves an agent that learns by:

A. Cleaning database records
B. Analyzing static clusters
C. Interacting with an environment and receiving rewards or penalties
D. Mimicking a teacher

17 Which of the following is a common issue where a model performs well on training data but poorly on new data?

A. Clustering
B. Overfitting
C. Normalization
D. Underfitting

18 What is the first step in Data Preprocessing?

A. Data Cleaning
B. Hyperparameter Tuning
C. Feature Scaling
D. Model Training

19 Garbage In, Garbage Out (GIGO) implies that:

A. Poor quality input data leads to poor quality model output
B. More data always results in better models
C. We should delete all data
D. Computer hardware needs regular cleaning

20 Which technique is used to handle missing values in a dataset?

A. Overfitting
B. Clustering
C. Imputation
D. Regression

21 What is the purpose of 'Feature Scaling'?

A. To convert text to numbers
B. To increase the number of features
C. To remove missing values
D. To bring all features to a similar scale or range

22 Standardization (Z-score normalization) transforms data to have:

A. No negative numbers
B. A range between 0 and 1
C. A mean of 0 and standard deviation of 1
D. A mean of 100

23 Which preprocessing technique is used to convert categorical variables into numerical format?

A. Encoding
B. Scaling
C. Sampling
D. Imputation

24 One-Hot Encoding helps in handling:

A. Outliers
B. Nominal categorical data
C. Continuous variables
D. Missing values

25 An outlier is defined as:

A. A value that is exactly the mean
B. A data point that differs significantly from other observations
C. A categorical variable
D. A missing value

26 Which method is commonly used to detect outliers?

A. Gradient Descent
B. Box Plot
C. Confusion Matrix
D. Pie Chart

27 Dimensionality Reduction aims to:

A. Reduce the number of input variables while retaining important information
B. Remove all categorical variables
C. Increase the number of variables
D. Create more rows in the dataset

28 PCA (Principal Component Analysis) is a technique used for:

A. Data Imputation
B. Dimensionality Reduction
C. Reinforcement Learning
D. Supervised Classification

29 Why do we split data into Training and Testing sets?

A. To train two different models
B. To remove outliers
C. To make the dataset smaller
D. To evaluate the model's performance on unseen data

30 Underfitting occurs when:

A. The model captures noise
B. The training data is perfect
C. The model is too complex
D. The model is too simple to capture the underlying structure of the data

31 Which of the following is NOT a data preprocessing step?

A. Feature Selection
B. Hypothesis Testing
C. Data Cleaning
D. Data Transformation

32 Min-Max Scaling transforms data into which range?

A. [-1, 1]
B. [-infinity, +infinity]
C. [0, 1]
D. [0, 100]

33 In a dataset, a row usually represents:

A. A feature
B. A label
C. An observation or instance
D. A statistical summary

34 Market Basket Analysis is an application of which learning type?

A. Classification
B. Association Rule Learning
C. Supervised Learning
D. Regression

35 Which of the following describes 'Feature Selection'?

A. Selecting the most relevant features to improve model performance
B. Handling missing values
C. Scaling features
D. Creating new features from existing ones

36 Noise in data refers to:

A. Meaningless or random variance in the data
B. Duplicate rows
C. Categorical labels
D. Missing values

37 Which of the following is a quantitative variable?

A. Gender (Male/Female)
B. Zip Code
C. Age
D. Color (Red/Blue)

38 Label Encoding is best used when:

A. The categorical feature has no order
B. The categorical feature is ordinal (has an inherent order)
C. The data is continuous
D. There are missing values

39 The process of converting raw data into a clean dataset is often called:

A. Data Wrangling/Munging
B. Data Visualization
C. Data Architecture
D. Data Mining

40 Which of these is a supervised learning algorithm used for classification?

A. Logistic Regression
B. PCA
C. Apriori
D. K-Means

41 If a dataset has duplicate records, the preprocessing step required is:

A. Deduplication
B. Encoding
C. Imputation
D. Normalization

42 The 'Curse of Dimensionality' refers to problems caused by:

A. Too many missing values
B. Too much processing power
C. Inaccurate labels
D. Too many features (variables) relative to the number of observations

43 Which variable type requires dummy variables creation during preprocessing?

A. Categorical
B. Binary
C. Ordinal
D. Numerical

44 Semi-supervised learning uses:

A. Only unlabeled data
B. A small amount of labeled data and a large amount of unlabeled data
C. Only labeled data
D. Reinforcement signals

45 What is the result of 'Data Transformation'?

A. Data is visualized
B. Data is deleted
C. Data is collected
D. Data is converted into a format suitable for modeling

46 Predicting the temperature for tomorrow is a:

A. Classification task
B. Preprocessing task
C. Regression task
D. Clustering task

47 Customer segmentation usually relies on which type of learning?

A. Regression
B. Reinforcement Learning
C. Supervised Learning
D. Unsupervised Learning (Clustering)

48 Which of the following represents 'Structured Data'?

A. Video files
B. Emails
C. Relational database tables
D. Audio recordings

49 Balanced data refers to:

A. Data with no missing values
B. Data that has been scaled
C. Data where the target classes are represented approximately equally
D. Data where all values are the same

50 Binning is a preprocessing technique used to:

A. Remove outliers
B. Normalize data
C. Fill missing values
D. Convert continuous variables into categorical intervals
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