1What is the primary goal of Predictive Analytics?
A.To describe what happened in the past
B.To prescribe the best course of action
C.To predict future outcomes based on historical data
D.To store large amounts of data
Correct Answer: To predict future outcomes based on historical data
Explanation:
Predictive analytics uses historical data and statistical algorithms to identify the likelihood of future outcomes.
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2Which of the following is NOT a phase in the standard predictive analytics lifecycle?
A.Deployment
B.Data Preparation
C.Hardware Manufacturing
D.Model Building
Correct Answer: Hardware Manufacturing
Explanation:
Hardware manufacturing is related to production, not the analytics lifecycle. The lifecycle typically includes problem definition, data prep, modeling, and deployment.
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3Descriptive Analytics differs from Predictive Analytics because Descriptive Analytics focuses on:
A.Forecasting future trends
B.Optimizing decision making
C.Summarizing past events
D.Creating machine learning models
Correct Answer: Summarizing past events
Explanation:
Descriptive analytics answers 'What happened?' by summarizing past data, whereas predictive analytics answers 'What could happen?'.
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4Which of the following is a common application of Predictive Analytics?
A.Generating an annual report
B.Creating a database schema
C.Real-time operating system scheduling
D.Credit scoring
Correct Answer: Credit scoring
Explanation:
Credit scoring predicts the likelihood of a borrower defaulting on a loan based on their history.
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5In the context of Machine Learning, what is 'Training Data'?
A.Data used to evaluate the final model
B.Data used to teach the algorithm patterns
C.Future data that has not occurred yet
D.Data that has been corrupted
Correct Answer: Data used to teach the algorithm patterns
Explanation:
Training data is the dataset used to fit the model parameters, allowing the algorithm to learn relationships.
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6Which type of analytics answers the question 'What should we do about it'?
A.Diagnostic Analytics
B.Predictive Analytics
C.Prescriptive Analytics
D.Descriptive Analytics
Correct Answer: Prescriptive Analytics
Explanation:
Prescriptive analytics suggests decision options to take advantage of a future opportunity or mitigate a future risk.
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7Machine Learning is best described as:
A.A subset of AI where computers learn from data without explicit programming
B.Strictly using statistical regression only
C.Data storage optimization
D.Hard-coding rules for every possible scenario
Correct Answer: A subset of AI where computers learn from data without explicit programming
Explanation:
ML focuses on algorithms that improve automatically through experience and data usage.
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8What is the 'Target Variable' in a predictive model?
A.The noise in the data
B.The variable used to predict
C.The variable being predicted
D.The index of the dataset
Correct Answer: The variable being predicted
Explanation:
The target variable (or dependent variable) is the outcome the model aims to predict.
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9Which of the following is a type of Supervised Learning?
A.Clustering
B.Association Rule Learning
C.Regression
D.Dimensionality Reduction
Correct Answer: Regression
Explanation:
Regression is a supervised learning task where the output variable is continuous.
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10In Supervised Learning, the dataset must contain:
A.Only images
B.Unlabeled data
C.Only input features
D.Labeled data (Input features and Output labels)
Correct Answer: Labeled data (Input features and Output labels)
Explanation:
Supervised learning requires ground truth (labels) to train the model to map inputs to outputs.
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11Unsupervised Learning differs from Supervised Learning because it:
A.Predicts a specific target
B.Is only used for text data
C.Uses labeled data
D.Finds hidden patterns in unlabeled data
Correct Answer: Finds hidden patterns in unlabeled data
Explanation:
Unsupervised learning deals with data that has no historical labels, focusing on discovering structure or clusters.
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12Predicting the price of a house based on its square footage is an example of:
A.Clustering
B.Reinforcement Learning
C.Regression
D.Classification
Correct Answer: Regression
Explanation:
Since price is a continuous numerical value, this is a regression problem.
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13Predicting whether an email is 'Spam' or 'Not Spam' is an example of:
A.Principal Component Analysis
B.Regression
C.Clustering
D.Classification
Correct Answer: Classification
Explanation:
The output is a categorical label (Spam/Not Spam), making it a classification problem.
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14Which of the following is an Unsupervised Learning algorithm?
A.Linear Regression
B.Decision Trees
C.K-Means Clustering
D.Logistic Regression
Correct Answer: K-Means Clustering
Explanation:
K-Means is used to group data points into clusters based on similarity, without pre-existing labels.
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15What is the main objective of Clustering?
A.To reduce the number of rows in a table
B.To classify data into known categories
C.To predict a continuous value
D.To group similar data points together
Correct Answer: To group similar data points together
Explanation:
Clustering identifies natural groupings in data such that items in the same group are more similar to each other than to those in other groups.
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16Reinforcement Learning involves an agent that learns by:
A.Cleaning database records
B.Analyzing static clusters
C.Mimicking a teacher
D.Interacting with an environment and receiving rewards or penalties
Correct Answer: Interacting with an environment and receiving rewards or penalties
Explanation:
Reinforcement learning is based on a reward-penalty system where an agent learns to maximize cumulative reward.
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17Which of the following is a common issue where a model performs well on training data but poorly on new data?
A.Normalization
B.Clustering
C.Overfitting
D.Underfitting
Correct Answer: Overfitting
Explanation:
Overfitting occurs when a model learns the noise and details of the training data to the extent that it negatively impacts the performance on new data.
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18What is the first step in Data Preprocessing?
A.Hyperparameter Tuning
B.Model Training
C.Feature Scaling
D.Data Cleaning
Correct Answer: Data Cleaning
Explanation:
Before scaling or training, data must be cleaned (handling missing values, noise, and inconsistencies).
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19Garbage In, Garbage Out (GIGO) implies that:
A.We should delete all data
B.Computer hardware needs regular cleaning
C.Poor quality input data leads to poor quality model output
D.More data always results in better models
Correct Answer: Poor quality input data leads to poor quality model output
Explanation:
The quality of the predictive model is directly constrained by the quality of the data used to train it.
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20Which technique is used to handle missing values in a dataset?
A.Regression
B.Overfitting
C.Clustering
D.Imputation
Correct Answer: Imputation
Explanation:
Imputation involves replacing missing data with substituted values (e.g., mean, median, or mode).
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21What is the purpose of 'Feature Scaling'?
A.To increase the number of features
B.To bring all features to a similar scale or range
C.To convert text to numbers
D.To remove missing values
Correct Answer: To bring all features to a similar scale or range
Explanation:
Feature scaling ensures that no single feature dominates the model due to having a larger magnitude (e.g., salary vs age).
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22Standardization (Z-score normalization) transforms data to have:
A.A mean of 0 and standard deviation of 1
B.A range between 0 and 1
C.No negative numbers
D.A mean of 100
Correct Answer: A mean of 0 and standard deviation of 1
Explanation:
Standardization rescales data so that it follows a standard normal distribution.
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23Which preprocessing technique is used to convert categorical variables into numerical format?
A.Imputation
B.Encoding
C.Scaling
D.Sampling
Correct Answer: Encoding
Explanation:
Encoding (like One-Hot Encoding or Label Encoding) converts text categories into numbers so algorithms can process them.
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24One-Hot Encoding helps in handling:
A.Nominal categorical data
B.Outliers
C.Missing values
D.Continuous variables
Correct Answer: Nominal categorical data
Explanation:
One-Hot Encoding creates binary columns for each category, which is ideal for nominal data where no order exists.
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25An outlier is defined as:
A.A categorical variable
B.A value that is exactly the mean
C.A missing value
D.A data point that differs significantly from other observations
Correct Answer: A data point that differs significantly from other observations
Explanation:
Outliers are extreme values that deviate remarkably from the rest of the dataset.
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26Which method is commonly used to detect outliers?
A.Box Plot
B.Confusion Matrix
C.Pie Chart
D.Gradient Descent
Correct Answer: Box Plot
Explanation:
Box plots visually display the distribution of data and identify outliers as points outside the whiskers (interquartile range).
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27Dimensionality Reduction aims to:
A.Create more rows in the dataset
B.Increase the number of variables
C.Remove all categorical variables
D.Reduce the number of input variables while retaining important information
Correct Answer: Reduce the number of input variables while retaining important information
Explanation:
It simplifies models, reduces computation time, and helps avoid the curse of dimensionality.
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28PCA (Principal Component Analysis) is a technique used for:
A.Supervised Classification
B.Data Imputation
C.Reinforcement Learning
D.Dimensionality Reduction
Correct Answer: Dimensionality Reduction
Explanation:
PCA transforms a large set of variables into a smaller one that still contains most of the information.
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29Why do we split data into Training and Testing sets?
A.To train two different models
B.To make the dataset smaller
C.To evaluate the model's performance on unseen data
D.To remove outliers
Correct Answer: To evaluate the model's performance on unseen data
Explanation:
The test set acts as a proxy for new, real-world data to check if the model generalizes well.
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30Underfitting occurs when:
A.The model is too simple to capture the underlying structure of the data
B.The model captures noise
C.The model is too complex
D.The training data is perfect
Correct Answer: The model is too simple to capture the underlying structure of the data
Explanation:
Underfitting happens when a model cannot learn the patterns in the training data, resulting in poor performance on both training and test data.
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31Which of the following is NOT a data preprocessing step?
A.Data Cleaning
B.Data Transformation
C.Hypothesis Testing
D.Feature Selection
Correct Answer: Hypothesis Testing
Explanation:
Hypothesis testing is a statistical method for inference, not typically considered a preprocessing step for preparing data for ML.
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32Min-Max Scaling transforms data into which range?
A.[0, 100]
B.[-infinity, +infinity]
C.[-1, 1]
D.[0, 1]
Correct Answer: [0, 1]
Explanation:
Min-Max scaling rescales the feature to a fixed range, typically 0 to 1.
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33In a dataset, a row usually represents:
A.A label
B.A statistical summary
C.An observation or instance
D.A feature
Correct Answer: An observation or instance
Explanation:
In tabular data, rows represent individual records (instances), and columns represent attributes (features).
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34Market Basket Analysis is an application of which learning type?
A.Regression
B.Classification
C.Association Rule Learning
D.Supervised Learning
Correct Answer: Association Rule Learning
Explanation:
It finds associations between items, such as 'people who buy bread also buy milk' (Unsupervised learning).
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35Which of the following describes 'Feature Selection'?
A.Scaling features
B.Creating new features from existing ones
C.Selecting the most relevant features to improve model performance
D.Handling missing values
Correct Answer: Selecting the most relevant features to improve model performance
Explanation:
Feature selection involves picking a subset of relevant features to reduce complexity and improve accuracy.
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36Noise in data refers to:
A.Missing values
B.Categorical labels
C.Meaningless or random variance in the data
D.Duplicate rows
Correct Answer: Meaningless or random variance in the data
Explanation:
Noise represents unwanted variation or random errors that obscure the true signal in the data.
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37Which of the following is a quantitative variable?
A.Color (Red/Blue)
B.Age
C.Zip Code
D.Gender (Male/Female)
Correct Answer: Age
Explanation:
Age is a numerical value that measures a quantity, whereas the others are categorical.
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38Label Encoding is best used when:
A.The categorical feature has no order
B.There are missing values
C.The categorical feature is ordinal (has an inherent order)
D.The data is continuous
Correct Answer: The categorical feature is ordinal (has an inherent order)
Explanation:
Label encoding assigns an integer to categories. If order matters (e.g., Low, Medium, High), this is appropriate. For nominal data, it may introduce false relationships.
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39The process of converting raw data into a clean dataset is often called:
A.Data Mining
B.Data Architecture
C.Data Wrangling/Munging
D.Data Visualization
Correct Answer: Data Wrangling/Munging
Explanation:
Data wrangling is the process of transforming and mapping data from one 'raw' data form into another format for analytics.
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40Which of these is a supervised learning algorithm used for classification?
A.K-Means
B.Apriori
C.Logistic Regression
D.PCA
Correct Answer: Logistic Regression
Explanation:
Despite its name, Logistic Regression is used for binary classification tasks.
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41If a dataset has duplicate records, the preprocessing step required is:
A.Imputation
B.Deduplication
C.Encoding
D.Normalization
Correct Answer: Deduplication
Explanation:
Deduplication removes identical rows to prevent bias in the model.
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42The 'Curse of Dimensionality' refers to problems caused by:
A.Too many features (variables) relative to the number of observations
B.Too much processing power
C.Too many missing values
D.Inaccurate labels
Correct Answer: Too many features (variables) relative to the number of observations
Explanation:
As the number of features increases, the data becomes sparse, making it difficult for models to find patterns without massive amounts of data.
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43Which variable type requires dummy variables creation during preprocessing?
A.Categorical
B.Numerical
C.Binary
D.Ordinal
Correct Answer: Categorical
Explanation:
Categorical variables (specifically nominal ones) often need to be converted into dummy variables (0/1 columns) to be used in mathematical models.
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44Semi-supervised learning uses:
A.Only labeled data
B.A small amount of labeled data and a large amount of unlabeled data
C.Only unlabeled data
D.Reinforcement signals
Correct Answer: A small amount of labeled data and a large amount of unlabeled data
Explanation:
This approach leverages the large volume of unlabeled data to improve learning accuracy when labeling data is expensive.
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45What 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
Correct Answer: Data is converted into a format suitable for modeling
Explanation:
Transformation includes smoothing, aggregation, generalization, normalization, etc., to make data model-ready.
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46Predicting the temperature for tomorrow is a:
A.Clustering task
B.Regression task
C.Preprocessing task
D.Classification task
Correct Answer: Regression task
Explanation:
Temperature is a continuous numerical variable, making this a regression problem.
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47Customer segmentation usually relies on which type of learning?