Back to Subject

Unit 1: Introduction and Data Preparation - Practice Quiz

INT234 — Predictive Analytics 50 Questions
0 Correct 0 Wrong 50 Left
0/50

1 What is the primary goal of Predictive Analytics?

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

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

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

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

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

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

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

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

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

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

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

7 Machine Learning is best described as:

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

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

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

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

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

10 In Supervised Learning, the dataset must contain:

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

11 Unsupervised Learning differs from Supervised Learning because it:

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

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

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

16 Reinforcement Learning involves an agent that learns by:

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

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

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

18 What is the first step in Data Preprocessing?

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

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

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

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

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

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

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

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

24 One-Hot Encoding helps in handling:

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

25 An outlier is defined as:

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

26 Which method is commonly used to detect outliers?

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

27 Dimensionality Reduction aims to:

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

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

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

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

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

30 Underfitting occurs when:

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

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

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

32 Min-Max Scaling transforms data into which range?

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

33 In a dataset, a row usually represents:

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

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

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

35 Which of the following describes 'Feature Selection'?

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

36 Noise in data refers to:

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

37 Which of the following is a quantitative variable?

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

38 Label Encoding is best used when:

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

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

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

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

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

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

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

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

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

43 Which variable type requires dummy variables creation during preprocessing?

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

44 Semi-supervised learning uses:

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

45 What is the result of 'Data Transformation'?

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

46 Predicting the temperature for tomorrow is a:

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

47 Customer segmentation usually relies on which type of learning?

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

48 Which of the following represents 'Structured Data'?

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

49 Balanced data refers to:

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

50 Binning is a preprocessing technique used to:

A. Convert continuous variables into categorical intervals
B. Fill missing values
C. Remove outliers
D. Normalize data
Next MCQ
Unit 2