50 Multiple Choice Questions with Answers for Data Science

The Data Science Process: Understanding, Collecting, Cleaning, Modeling, and Insights

Data science is the use of data to find solutions and predict outcomes. This interdisciplinary field uses scientific methods and processes to extract knowledge or insights from structured or unstructured data. The data science process involves understanding the business problem, collecting data, cleaning and exploring it, building a model, and collecting insights.

Artificial intelligence, machine learning, and deep learning are all part of data science. Machine learning is the science of getting computers to learn without explicit programming. It works on the concept of understanding through experiences and allowing computers to learn automatically without human interaction.

Machine learning has two types: supervised and unsupervised learning. Supervised learning uses labeled data to train the machine to predict new outputs. Unsupervised learning deals with unlabeled data and forces the machine to discover information on its own.

Artificial Intelligence is a booming field that aims to create man-made thinking machines. Its goal is to replicate human intelligence to make meaningful decisions through machines that can perform tasks requiring intelligence. AI is currently in fields such as robotics, self-driving cars, chess, and mathematical theorem proving and is bringing about a global revolution.

Data science is a vast field of study, so below are a few multiple choice questions to test your knowledge.

1. What is the primary aim of machine learning? A) To allow machines to learn automatically without human interaction B) To create man-made thinking machines C) To replicate human intelligence D) To extract knowledge from data

2. What are the five stages of the data science process? A) Collecting, cleaning, exploring, modeling, and insights B) Understanding, collecting, cleaning, modeling, and insights C) Exploring, modeling, collecting, cleaning, and insights D) Cleaning, modeling, exploring, collecting, and insights

3. What is the difference between supervised and unsupervised learning? A) Supervised learning deals with labeled data while unsupervised learning deals with unlabeled data B) Unsupervised learning deals with well-defined data while supervised learning deals with undefined data C) Supervised learning allows machines to learn automatically while unsupervised learning requires human interaction D) There is no difference, they are the same thing.

Answers: 1. A, 2. B, 3. A

Language Used in Data Science

Ruby is a programming language that is commonly used in data science alongside Python and R.

#Example code in Ruby for data science

#creating an array of numbers from 1 to 10
numbers = (1..10).to_a
puts "Array of numbers: #{numbers}"

#Finding the sum of numbers using reduce method
sum = numbers.reduce(:+)
puts "Sum of numbers: #{sum}"

#Finding the average of numbers
average = sum.to_f / numbers.length
puts "Average of numbers: #{average}"

The code above demonstrates a simple example of how Ruby can be used for basic data analysis tasks. It creates an array of numbers, finds the sum and average using Ruby's built-in methods.

Components of Data Science

In Data Science, the main components are:

Domain expertise, Data engineering and Advanced computing

All of these components are important in their own way and contribute to the effective implementation of data science.

Identifying the Missing Part of the Data Science Process

The data science process involves several steps that need to be followed systematically. These steps include discovery, model planning, operationalization, and communication. Out of these, communication building is not a direct part of the data science process. However, effective communication is essential for successful data science. To effectively communicate the results of a data science project, visualizations, reports, and dashboards must be created.

Therefore, while communication building isn't a direct part of the data science process, it's still an important aspect of the overall project.

Total Number of Categories for Data

In what ways can data be characterized?

  1. Structured
  2. Unstructured

Answer: Data can be categorized into two groups: structured and unstructured data.

// No code to optimize since this is not a code-related question

Understanding Unstructured Data in Data Science

In data science, unstructured data refers to data that does not have a specific, predefined data model or organizational structure. This can include text documents, images, videos, audio recordings, and other forms of data that are not easily organized into a traditional database structure. Unstructured data can be more difficult to analyze and interpret than structured data because it does not have a clear framework or set of rules for organization. However, with the right tools and techniques, data scientists can still extract valuable insights from unstructured data.

Regarding the given statement, it is true that unstructured data is not organized. This is one of the features that distinguishes it from structured data, which is typically organized into tables with predefined columns and rows. Without a specific structure, unstructured data can be more challenging to manage and make sense of from a data science perspective.

In conclusion, understanding unstructured data is an essential part of data science, as it is becoming increasingly prevalent in today's data-driven world. By leveraging the right tools and techniques, data scientists can extract valuable insights from unstructured data and gain a more comprehensive understanding of the world around us.


Column Representation of Data

Answer: B) A column is a vertical representation of data.

// Example code of creating a column in HTML table

    <th>Column 1</th>
    <th>Column 2</th>
    <th>Column 3</th>
    <td>Data 1</td>
    <td>Data 2</td>
    <td>Data 3</td>
    <td>Data 4</td>
    <td>Data 5</td>
    <td>Data 6</td>
    <td>Data 7</td>
    <td>Data 8</td>
    <td>Data 9</td>

In the above example, we have created a table with three columns and three rows. Each vertical section of this table is a column that represents a set of related data.

Correction: Understanding Data Frame

In data science, a data frame is a highly structured representation of data, consisting of rows and columns. Therefore, the statement "A data frame is an unstructured representation of data" is false.

Identifying the impact of dimensionality reduction

In terms of dimensionality reduction, the factor that it reduces is collinearity. Collinear variables have a high degree of association and can create a negative effect on model performance by reducing its accuracy and interpretability. Therefore, reducing collinearity through dimensionality reduction can improve model performance.

Machine Learning is a Subset of Artificial Intelligence

Out of the given options, the correct answer is A) Machine learning is a subset of artificial intelligence.

// No code to optimize or rephrase in this question.


The FIND-S algorithm is a machine-learning algorithm utilized to construct a hypothesis based on given training data containing only positive instances. It neglects negative examples while creating a maximally specific hypothesis. This algorithm is similar to the basic concept of the Candidate-Elimination algorithm, with the difference that it only concentrates on the most specific hypothesis rather than the most general and the most particular. Hence, the correct answer to this question is B) Negative.

# Python code for the FIND-S algorithm that only considers the positive examples.

# Initializing most specific hypothesis h
hypothesis = ["0"] * 6

# Reading the dataset
data = [['Sunny', 'Warm', 'Normal', 'Strong', 'Warm', 'Same', 'Yes'],
        ['Sunny', 'Warm', 'High', 'Strong', 'Warm', 'Same', 'Yes'],
        ['Rainy', 'Cold', 'High', 'Strong', 'Warm', 'Change', 'No'],
        ['Sunny', 'Warm', 'High', 'Strong', 'Cool', 'Change', 'Yes']]

# Training the dataset
for instance in data:
    if instance[-1] == "Yes":
        for i in range(len(hypothesis)):
            if hypothesis[i] == "0" or hypothesis[i] == instance[i]:
                hypothesis[i] = instance[i]
                hypothesis[i] = "?"

# Printing the hypothesis generated by the FIND-S algorithm
print("Hypothesis generated by FIND-S algorithm:", hypothesis)

What does PAC stand for?

The acronym PAC stands for Probably Approximate Correct.

// No code to optimize or fix as this is just a question and answer.

RBF Neural Networks Layers

The total number of layers in a Radial Basis Function Neural Network is three.

In RBF Neural Network, the three layers are as follows:

  1. Input Layer: This layer contains the input neurons which receive the input data.
  2. Hidden Layer: This layer consists of radial basis function neurons that transform the input data into a higher dimensional space, allowing it to be separated more easily.
  3. Output Layer: This layer contains a single output neuron that computes the final output value of the neural network.

Can Decision Trees be used for Clustering?

Answer: False.

A decision tree is a supervised learning algorithm used mainly for classification problems, whereas clustering is an unsupervised learning algorithm used for grouping unlabelled data. Hence, decision trees cannot be used for clustering.

Procedural Domain Knowledge in a Rule-Based System

In a rule-based system, the procedural domain knowledge is represented in the form of production rules. These rules are used to infer new knowledge from existing knowledge and derive conclusions. This process is done by evaluating the rules and applying them to the known data.

Meta-rules and control rules are also used in a rule-based system, but they are not considered as forms of procedural domain knowledge. Meta-rules are used to reason about the system's rules, while control rules are used to manage the inference process.

Therefore, the correct answer is C) Production Rules.

Understanding MISD Architecture

The MISD architecture is a type of parallel computing architecture where multiple processing units, called cells, operate in a synchronized manner to perform a single task. This architecture is sometimes also referred to as systolic arrays.

Out of the given options, the correct answer is A) MISD. This architecture is different from SISD (Single Instruction Single Data), where only one processing unit executes a single operation at a time. It is also different from SIMD (Single Instruction Multiple Data), where multiple processing units perform the same operation on different data sets simultaneously.

Machines running LISP are also called what?

The machines running LISP are also known as AI workstations.

LISP is a programming language that was specifically designed for Artificial Intelligence research and development. Thus, it is quite natural to call the machines running this language as AI workstations.

Understanding Hybrid Bayesian Networks

A hybrid Bayesian network involves the combination of both discrete and continuous variables as numerical inputs to support decision-making processes. In such a network, both types of variables are utilized in establishing probabilistic relationships among the variables.

Hence, the correct answer to the question is option (C) which indicates that a hybrid Bayesian network consists of both discrete and continuous variables.

//No code provided for this question

Identifying Key Data Science Skills

In data science, there are several key skills that are highly valued. These skills can help a data scientist be successful in their role. The following skills are considered to be key skills:

  • Data visualization
  • Machine learning
  • Statistics

The correct answer is D) All of the above are the key data science skills.

Processing Raw data

It is false that raw data should be processed only one time. Depending on the purpose, raw data may need to be processed multiple times. However, it is important to have a well-defined processing pipeline and ensure that the same steps are repeated consistently to avoid inconsistencies in the final output.

// Example processing pipeline for raw data
raw_data = read_csv('raw_data.csv')
clean_data = preprocess(raw_data)
transformed_data = transform(clean_data)
final_output = analyze(transformed_data)

Identifying Revision Control System for Scipy, Numpy, Git and Slidify

The revision control system used for Scipy and Numpy is Git. On the other hand, it is not specified what revision control system is used for Slidify.

Git is a widely used distributed revision control system for tracking changes in source code during software development. It provides a mechanism to manage and store different versions of code, collaborate with other developers, and keep track of changes over time.

    Example of Git command:
    git commit -m "Added new feature"

Identifying False Statements About Regression

Out of the following options about regression, which one is false?

  • It is used for prediction.
  • It is used for interpretation.
  • It relates inputs to outputs.
  • It discovers causal relationships.

Answer: The false option is D, which states that regression discovers causal relationships.

General Limitations of Backpropagation Rule

The backpropagation algorithm has some general limitations that should be taken into account while implementing it. These limitations include:

  • Slow convergence
  • Scaling issues
  • Local minima problem

Therefore, the correct answer is D) All of the above limitations apply to the backpropagation rule.

  // The code implementation of backpropagation rule will depend on the specific neural network architecture and programming language used.

Choosing an Instance-Based Learner

In machine learning, there are two types of instance-based learners: eager learners and lazy learners. Eager learners create the model during the training phase and make predictions during execution. On the other hand, lazy learners wait until the execution phase to create the model and make predictions.

A lazy learner is an instance-based learner that defers the model creation until the prediction time. This type of learner is also called a lazy evaluator or case-based reasoning. In a lazy learner, all the instances are stored and used for predicting the target function value of new inputs.

On the contrary, an eager learner creates the model during the training phase itself. These learners specify a function that maps input features to output labels or values. When the function is trained, it can be used to make predictions for new inputs.

In this context, option B is correct - lazy learner is an instance-based learner.

// Code example of a lazy learner

class LazyLearner:
    def __init__(self, data):
        self.data = data
    def predict(self, new_input):
        nearest_neighbor = None
        nearest_distance = float('inf')
        # Compute distance to all instances
        for instance in self.data:
            distance = distance(instance, new_input)
            if distance < nearest_distance:
                nearest_distance = distance
                nearest_neighbor = instance
        # Predict output of nearest neighbor
        return nearest_neighbor.output

Is Artificial Intelligence the Process that Allows Computers to Learn and Make Decisions like Humans?

The statement is True.


# No code required for this question.

P: Artificial Intelligence (AI) is a field in computer science that deals with building machines and programs which can perform tasks that typically require human intelligence such as learning, problem-solving, decision making, and understanding natural language. AI involves the use of algorithms and statistical models to enable computers to learn from data and experience, and make decisions based on that learning. While AI systems may not learn in the same way as humans, they can still mimic and improve upon many aspects of human intelligence, making them valuable tools for a wide range of applications.

Understanding the K-Mean Algorithm

In the K-mean algorithm, the letter K represents the number of iterations required to converge. The algorithm starts by choosing K number of centroids (cluster centers), and then iteratively re-assigns data points to their nearest centroid. This process continues until the centroids no longer move, or the maximum number of iterations is reached. Hence, K means the number of times the algorithm will iterate to form the clusters.

Machine Learning Algorithm Based on Bagging

The machine learning algorithm that is based on the idea of bagging is Random Forest.

Random Forest is an ensemble learning method that combines multiple decision trees, where each tree is trained on a random subset of the training data. The algorithm then aggregates the outputs of each decision tree to make a prediction. This method helps to reduce overfitting and improve the accuracy of the model.

Disadvantage of Decision Trees

One of the main disadvantages of decision trees is that they are prone to overfitting. This means that the tree may become too complex and specific to the training data, which can lead to poor performance on new, unseen data. Other disadvantages, such as their robustness to outliers and relevance to factor analysis, are not as significant.

// Example of overfitting in decision tree
tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)
train_score = tree.score(X_train, y_train)
test_score = tree.score(X_test, y_test)
print("Training accuracy: ", train_score) 
print("Test accuracy: ", test_score) 

// If the test accuracy is significantly lower than the training accuracy, it may indicate overfitting.

Identification of Unsupervised Learning Algorithm

Out of the given options, Principal Component Analysis (PCA) is an unsupervised learning algorithm, not a supervised learning algorithm.

In supervised learning, the model learns from labeled data to make predictions on new, unseen data. Examples of supervised learning algorithms include Naive Bayesian, linear regression, and decision tree.

PCA is an unsupervised learning algorithm used for dimensionality reduction and feature extraction. It aims to reduce the number of features while retaining the variance in the data with a smaller feature set. It works with unlabeled data to find patterns and correlations within the data.


from sklearn.decomposition import PCA
pca = PCA(n_components=2)
new_data = pca.transform(data)

Clustering Method for Variance in Data

The correct answer to identify the clustering method that takes care of variance in data is option B) Gaussian mixture model. This method is a probabilistic approach that allows the modeling of the variance in data.

Option A) Decision tree is a classification method that does not specifically take into account variance in data.

Option C) K means is a centroid-based method that does not consider variance in the data.

Therefore, the correct answer is B) Gaussian mixture model.


There are many Python packages that implement the Gaussian mixture model, such as scikit-learn, TensorFlow, and Pyro. Here is an example of using scikit-learn:

from sklearn.mixture import GaussianMixture

# Instantiate the model
gmm = GaussianMixture(n_components=3)

# Fit the model to the data

# Predict the clusters
labels = gmm.predict(X)

Identifying the Focus of Data Analysis Techniques

In the field of data analysis, several techniques are used to extract meaningful insights from data. Among them, the following options are available:

  • Big data
  • Data mining
  • Machine learning
  • Data wrangling

Out of these options, the technique that focuses on the discovery of unknown properties on the data is Data mining.

Gold Standard Model for Data Analysis

The gold standard model for data analysis is usually causal.

Causal analysis involves identifying the cause and effect relationship between variables. This is often done through experimental studies where one variable is manipulated to observe the effect on another variable.

In contrast, inferential analysis involves making predictions or generalizations about a larger group based on a smaller sample, while descriptive analysis involves summarizing and describing data without making any inferences or predictions.

Alternate Term for Data Dredging

The alternate term for data dredging is data fishing.

// No code provided in this question

What does CLI stand for?

In computing, CLI stands for Command-line interface.

A CLI is a text-based interface used to interact with a computer's operating system or a software application by typing commands into a terminal or command prompt. It allows users to perform tasks, run programs, and access files quickly through the use of text commands.

Some popular examples of CLI-based operating systems include Linux and macOS, while Windows also has a CLI known as Command Prompt or PowerShell.

Understanding Time Deltas

Time deltas are indeed differences in times that can be expressed in different units. They represent temporal durations and can be used to perform operations on dates and times, such as addition or subtraction.

For example, a time delta of 1 hour can be expressed as 3600 seconds or 60 minutes, depending on the desired unit of measurement.

Overall, time deltas are a useful tool in time-related calculations and can be easily manipulated in Python using the built-in datetime module.

import datetime
delta = datetime.timedelta(hours=2)

The above code creates a time delta of 2 hours and converts it to total seconds. This would output the value 7200.

Applications of Data Science in Healthcare

Data science has numerous applications in various fields, and healthcare is no exception. Healthcare has greatly benefited from the advent of data science methods.

Some of the important applications of data science in healthcare are as follows:

  1. Data Science for Genomics: Data science tools and techniques are used to analyze the vast amount of genomic data generated by next-generation sequencing technologies. Data science is used to help in understanding the genetic basis of diseases and identifying potential drug targets.
  2. Data Science for Medical Imaging: Medical imaging generates large volumes of data that can be analyzed with data science methods. Data science is used to develop innovative imaging techniques to detect early-stage diseases such as cancer.
  3. Drug Discovery with Data Science: Data science is used to analyze large volumes of chemical, biological, and pharmacological data to identify new drug candidates. Data science methods are used to model drug-target interactions, predict the activity of new compounds, and optimize drug efficacy.
  4. All of the Above: All of the above are applications of data science in healthcare.
// Example of how to use data science in healthcare

Identifying Incorrect CLI Command




, and


are all commands used in the Command Line Interface (CLI) of various operating systems. However, the incorrect command in this list is


. There is no such command in the CLI syntax, and it is not recognized by most operating systems. Therefore, option C is the correct answer. It is important to be familiar with the correct syntax of CLI commands to avoid errors and potential damage to the system.

Principles of Analytical Graphs

There are a total of 6 principles of analytical graphs.

//Example usage:
int numOfPrinciples = 6;

Representation of Knowledge in AI

In Artificial Intelligence, knowledge can be represented using two logics: Predicate Logic and Propositional Logic. Therefore, option C is the correct answer.

Predicate Logic is used for representing and reasoning about relations between objects and Propositional Logic is used for representing and reasoning about propositions.

Having knowledge represented in these logics helps in making decisions and producing solutions in AI systems.

Working of Inference Engines

Inference engines operate using two modes - forward chaining and backward chaining. The correct answer is option C, which states that they work on both modes.

Forward chaining involves reasoning from existing facts or data to draw new conclusions, while backward chaining begins with a goal and works backwards to find evidence and support to reach that goal.

Having a clear understanding of how inference engines operate is important in fields such as artificial intelligence and machine learning.

// No code present in this Q&A

Components of an expert system

An expert system is composed of the following components:

  1. Knowledge base - contains facts and rules
  2. User interface - allows the user to interact with the system
  3. Inference engine - applies logical rules to the knowledge base to generate new information or conclusions

Therefore, the correct answer is D) All of the above.

Different Types of Observing Environments

There are two types of observing environments - fully and partial.

    //Example code for different types of observing environments
    const fully = true;
    const partial = false;

    if (fully) {
        console.log("This is a fully observing environment");
    } else if (partial) {
        console.log("This is a partial observing environment");
    } else {
        console.log("No observing environment is present");

Alternative Names for Data Dredging

In the field of data analysis, the practice of digging through large amounts of data to find patterns or relationships that may not be meaningful is called data dredging. This technique goes by several names, but the most commonly used alternative term is data snooping.

Other names for data dredging include:

  • Data fishing
  • Data peeking
  • Spurious correlation
  • Cherry picking

It is important to note that while data dredging can be useful for generating hypotheses, it is not a reliable method for drawing conclusions. It is always important to approach data analysis with caution and carefully consider the potential biases and limitations of any technique used.

// Example usage:
const data = [1, 2, 3, 4, 5];
const pattern = findPattern(data);

Algorithm Memory Usage Comparison

Of the given options, DFS uses the least memory. This is because DFS only stores the path from the node to the root in memory stack, one node at a time, unlike BFS which stores the whole tree in memory.

    void DFS(Node n){
        for(Node adj: n.adjacent){

Overview of Machine Learning Methods

Machine learning methods are techniques used by machines to learn and improve their performance on a task. These methods can be broadly classified into the following categories:


Memorization involves learning by heart without understanding. This method is typically used for tasks where the machine is required to recognize patterns in large datasets, such as image recognition or speech recognition.


Analogy involves learning by comparing similar examples. This method is typically used for tasks where the machine is required to make predictions based on past experience, such as recommendation systems.


Deduction involves learning by applying logic to data. This method is typically used for tasks where the machine is required to learn rules or principles that are used to make decisions, such as fraud detection or medical diagnosis.

Therefore, the correct answer is D) All of the above.

The Types of Machine Learning

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

  • Supervised Learning: In this type of machine learning, the algorithm is trained using labeled data. The algorithm learns from the labeled data to make predictions or classifications on new, unlabeled data.
  • Unsupervised Learning: In unsupervised learning, the algorithm is not given labeled data. Instead, it must identify patterns and relationships on its own in the input data.
  • Reinforcement Learning: Reinforcement learning involves the use of trial and error to learn optimal decision-making. The algorithm receives feedback in the form of rewards or penalties based on its actions, allowing it to learn from its mistakes and improve its decision-making over time.

Therefore, the answer to the question of what are the different types of machine learning is D, all of the above: supervised learning, unsupervised learning, and reinforcement learning.

// Example code for supervised learning using scikit-learn library:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = datasets.load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)


Which computer generation is associated with Artificial Intelligence?

The fifth generation of computers is associated with Artificial Intelligence.

// Code example:

int computerGeneration = 5;
String associatedAI = "Artificial Intelligence";
System.out.println("The " + computerGeneration + "th Generation computer is associated with " + associatedAI);

The code above prints: "The 5th Generation computer is associated with Artificial Intelligence."


PEAS is an acronym for Performance, Environment, Actuators, and Sensors.

// Example usage:

class Robot {
  constructor(performance, environment, actuators, sensors) {
    this.performance = performance;
    this.environment = environment;
    this.actuators = actuators;
    this.sensors = sensors;

const myRobot = new Robot(5, "indoor", ["arms", "legs"], ["camera", "microphone"]);

In the above example, we create a Robot class that takes in four parameters: performance, environment, actuators, and sensors. We then create a new instance of the Robot class and pass in some values for these parameters.


Out of the given SGD (Stochastic Gradient Descent) variants, Adam is the optimizer that combines both Momentum and adaptive learning. RMSprop and Adagrad also use adaptive learning but not momentum. Nesterov combines Momentum with an enhancement called Nesterov Acceleration. Therefore, option B is the correct answer.


This question doesn't require a code implementation.

Identifying the Zero-Centered Activation Function Output

The activation function output that is zero-centered is the Hyperbolic Tangent function. The other activation functions - Rectified Linear Unit (ReLU), Sigmoid, and Softmax - are not zero-centered.

# Example implementation of hyperbolic tangent activation function in Python
import numpy as np

def hyperbolic_tangent(x):
    return np.tanh(x)

Implementing Logic Functions with a Perceptron

The logic functions that can be implemented by a perceptron with two inputs are OR, AND and NOR. However, XOR cannot be implemented by a perceptron with two inputs.

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