Data Science | Assesments

A Data Science internship offers hands-on experience in analyzing large datasets, building predictive models, and using data-driven methods to solve business problems. Interns learn to gather, clean, visualize, and model data using modern tools and techniques in machine learning, statistics, and programming.

Objectives

Understand the full lifecycle of data — from collection to actionable insights.
Gain real-world exposure to machine learning, statistical modeling, and data visualization.
Apply tools like Python, SQL, R, and libraries such as pandas, NumPy, scikit-learn, etc.
Learn to work with structured and unstructured data across domains.

Key Responsibilities

Collect and clean raw datasets from various sources (APIs, databases, CSVs, etc.)
Perform Exploratory Data Analysis (EDA) to find patterns and trends.
Build and test predictive models (e.g., regression, classification, clustering).
Visualize results using tools like Matplotlib, Seaborn, or Power BI/Tableau.
Generate reports and dashboards to communicate findings to stakeholders.
Work closely with Data Engineers and Analysts to improve data pipelines.

Tools & Technologies You’ll Use

Languages: Python, R, SQL
Libraries: pandas, NumPy, scikit-learn, TensorFlow, Keras, PyTorch
Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI
Big Data: Hadoop, Spark (optional)
Databases: MySQL, PostgreSQL, MongoDB
Version Control: Git, GitHub

Skills You’ll Gain

Data cleaning and preprocessing
Statistical analysis and hypothesis testing
Predictive modeling and machine learning
Business problem-solving with data
Data visualization and storytelling
Model evaluation and tuning

Important Notice:
Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Science D1

1 / 45

1) Function to compute standard deviation in NumPy:

a) np.var()

b) np.mean()

c) np.std()

d) np.dev()

2 / 45

2) Which function computes pairwise distances between rows?

a) np.dist()

b) pdist()

c) scipy.spatial.distance.pdist()

d) df.pairwise_dist()

3 / 45

3) What is the output of: a = [1,2,3]; print(a*2)

a) [1,2,3,1,2,3]

b) [1,2,3,2]

c) [2,4,6]

d) Error

4 / 45

4) How to shuffle rows of a DataFrame?

a) df.sample(frac=1)

b) df.randomize()

c) df.permute()

d) df.shuffle()

5 / 45

5) Export a DataFrame to Excel:

a) df.save_excel()

b) df.to_csv()

c) df.to_excel()

d) df.export_excel()

6 / 45

6) In Pandas, axis=0 refers to:

a) None

b) Diagonal

c) Row-wise

d) Column-wise

7 / 45

7) How to drop rows where all elements are NaN?

a) df.dropna(how='all')

b) df.dropna(how='any')

c) df.dropna(all=False)

d) df.dropna(all=True)

8 / 45

8) arr = np.arange(6).reshape(2,3); print(arr.T.shape)

a) (3,2)

b) 6,)

c) Error

d) (2,3)

9 / 45

9) Which method shows memory usage of DataFrame?

a) df.mem()

b) df.memory_usage()

c) df.size()

d) df.info(memory=True)

10 / 45

10) Which method slices rows by label range?

a) df.range('start','end')

b) df.iloc['start':'end']

c) df.slice('start','end')

d) df.loc['start':'end']

11 / 45

11) Why is vectorization in NumPy faster?

a) Uses recursion

b) Skips memory

c) Uses compiled C code internally

d) Uses dictionaries

12 / 45

12) NumPy broadcasting allows:

a) Splitting arrays

b) Iterating over lists

c) Creating copies

d) Operations on arrays of different shapes

13 / 45

13) Which method calculates the rolling median?

a) df.rolling.median()

b) df.median_rolling()

c) df.rolling().median()

d) df.rolling_median()

14 / 45

14) Difference between np.dot() and np.matmul():

a) No difference

b) np.dot() only works for scalars

c) np.dot() works for 1D/2D; np.matmul() mainly for 2D+

d) np.matmul() works for strings

15 / 45

15) Which method returns the first valid index in a Series?

a) series.first_valid_index()

b) series.idxmin()

c) series.first_index()

d) series.valid_index()

16 / 45

16) How to interpolate missing numeric values?

a) df.fillna(interpolate=True)

b) df.replace(np.nan, interpolate)

c) df.fillna(method='interpolate')

d) df.interpolate()

17 / 45

17) Remove duplicate rows in Pandas:

a) df.clean()

b) df.remove_duplicates()

c) df.unique()

d) df.drop_duplicates()

18 / 45

18) NumPy broadcasting allows:

a) Operations on arrays of different shapes

b) Splitting arrays

c) Iterating over lists

d) Creating copies

19 / 45

19) Which function calculates quantiles?

a) df.quant()

b) df.q()

c) df.quantile()

d) df.percentile()

20 / 45

20) Function to create a pivot table in Pandas:

a) df.pivot_table()

b) df.pivoting()

c) df.group()

d) pd.table()

21 / 45

21) Convert a categorical column into dummy variables:

a) df.to_dummy()

b) pd.get_dummies()

c) pd.dummy()

d) df.encode()

22 / 45

22) Pandas method to compute rolling mean:

a) df.moving_avg()

b) df.mean(rolling)

c) df.rolling().mean()

d) df.shift().mean()

23 / 45

23) How to convert column types in Pandas?

a) df['col'].convert('int')

b) df.change_type('col','int')

c) df.set_type('col','int')

d) df['col'].astype('int')

24 / 45

24) .agg() in Pandas is used for:

a) Filter rows

b) Apply multiple aggregations

c) Merge DataFrames

d) Plot data

25 / 45

25) Which method returns descriptive stats for categorical columns?

a) df.describe_cats()

b) df.cat_summary()

c) df.describe(include=['object'])

d) df.stats_categorical()

26 / 45

26) Which method returns the indices of missing values?

a) df.isnull().index()

b) df.isnull().any()

c) df[df.isnull().any(axis=1)]

d) df.isnull().sum()

27 / 45

27) Which function gives cumulative sum of a column?

a) df.cum_sum('col')

b) df['col'].cum_sum()

c) df['col'].cumsum()

d) df.cumsum('col')

28 / 45

28) Which function is used to pivot a table by index and columns?

a) df.pivoting(index='i', columns='c', values='v')

b) df.pivot_table(index='i', columns='c', values='v')

c) df.pivot(index='i', columns='c', values='v')

d) df.pivot_all(index='i', columns='c', values='v')

29 / 45

29) Which function creates a 3×3 identity matrix?

a) np.eye(3)

b) list.set()

c) np.ones(3,3)

d) np.identity(3,3)

30 / 45

30) Which is a benefit of list comprehension?

a) Often more readable

b) All of the above

c) Faster than loops

d) Shorter code

31 / 45

31) Python sets do not allow:

a) Duplicates

b) Numbers

c) Strings

d) Iteration

32 / 45

32) Compute correlation between numeric columns:

a) df.corr()

b) df.correlation()

c) df.relate()

d) f.corr_matrix()

33 / 45

33) Approximate time complexity of dictionary lookup:

a) O(log n)

b) O(1)

c) O(n)

d) O(n^2)

34 / 45

34) Method to add/update a key-value pair in a dict:

a) dict.insert()

b) dict.update()

c) dict.append()

d) dict.add()

35 / 45

35) Which method joins DataFrames by index?

a) df.merge()

b) df.combine()

c) df.join()

d) df.concat()

36 / 45

36) Which parameter in read_csv handles large file chunks?

a) chunks

b) chunksize

c) size

d) blocksize

37 / 45

37) Which function converts a DataFrame to NumPy array?

a) All of the above

b) df.to_numpy()

c) df.values

d) np.array(df)

38 / 45

38) Pandas method to compute rolling mean:

a) df.shift().mean()

b) df.mean(rolling)

c) df.shift().mean()

d) df.rolling().mean()

39 / 45

39) Which function returns unique values across the entire DataFrame?

a) np.unique(df.values)

b) df.values.unique()

c) df.unique_all()

d) df.unique()

40 / 45

40) Which function computes pairwise distances between rows?

a) pdist()

b) df.pairwise_dist()

c) np.dist()

d) scipy.spatial.distance.pdist()

41 / 45

41) Which of the following is used to create a dictionary?

a) ()

b) {}

c) []

42 / 45

42) Which method returns the first valid index in a Series?

a) series.first_valid_index()

b) series.valid_index()

c) series.idxmin()

d) series.first_index()

43 / 45

43) Which data structure is unordered and mutable in Python?

a) Dictionary

b) List

c) Tuple

d) Set

44 / 45

44) Which method performs one-hot encoding in Pandas?

a) pd.get_dummies(df['col'])

b) df.encode_onehot('col')

c) df.one_hot('col')

d) df.dummies('col')

45 / 45

45) What will be printed? x = {1,2,2,3,4}; print(x)

a) {1,2,2,3,4}

b) [1,2,3,4]

c) {1,2,3,4}

d) Error

Your score is

The average score is 84%

Exit

Important Notice:
Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Science D2

1 / 45

1) What does NumPy mainly provide?

a) Graph plotting functions

b) High-performance multidimensional arrays

c) Database management

d) Machine learning algorithms

2 / 45

2) Which function in Pandas is used to join two datasets?

a) .merge()

b) .join()

c) .concat()

d) All of these

3 / 45

3) Which metric is used for classification evaluation?

a) R-squared

b) Precision, Recall, F1-score

c) RMSE

d) MSE

4 / 45

4) Which machine learning library is commonly used for building models?

a) Pandas

b) Flask

c) Matplotlib

d) Scikit-learn

5 / 45

5) In statistics, which measure shows the spread of data?

a) Mean

b) Variance

c) Median

d) Mode

6 / 45

6) Which measure of central tendency is affected most by extreme values?

a) Median

b) All of the above

c) Mean

d) Mode

7 / 45

7) Which visualization is best for showing correlation between two variables?

a) Scatter plot

b) Histogram

c) Bar chart

d) Pie chart

8 / 45

8) In supervised learning, the dataset is divided into:

a) Only one dataset

b) Labeled and Unlabeled sets

c) Training and Testing sets

d) Images and Texts

9 / 45

9) Which Python package provides tools for statistical modeling?

a) Statsmodels

b) NumPy

c) TensorFlow

d) Matplotlib

10 / 45

10) Which of the following is an unsupervised learning algorithm?

a) K-Means

b) Logistic Regression

c) Linear Regression

d) Decision Trees

11 / 45

11) Which of the following describes “Big Data”?

a) Data too large and complex for traditional tools

b) Small structured datasets

c) Data only stored in Excel

d) Simple flat files

12 / 45

12) Which of the following is an ensemble method in machine learning?

a) KNN

b) Random Forest

c) Decision Tree

d) Linear Regression

13 / 45

13) Which metric is used to evaluate regression models?

a) Mean Squared Error (MSE)

b) Accuracy

c) Confusion Matrix

d) Precision

14 / 45

14) Which of the following describes overfitting?

a) Model ignores training data

b) Model performs poorly on training data

c) Model performs well on training but poorly on test data

d) Model performs well on both training and test data

15 / 45

15) What is the role of hypothesis testing in Data Science?

a) Visualizing data

b) Making decisions based on sample data

c) Collecting raw data

d) Scaling data

16 / 45

16) Which Python library is most widely used for data analysis?

a) Scikit-learn

b) TensorFlow

c) Pandas

d) Matplotlib

17 / 45

17) Data Science is mainly a combination of:

a) Networking, Hardware, and Security

b) Drawing, Music, and Literature

c) Biology, Chemistry, and Physics

d) Statistics, Computer Science, and Domain Knowledge

18 / 45

18) Which data visualization is best for time-series data?

a) Pie chart

b) Line chart

c) Scatter plot

d) Bar chart

19 / 45

19) Which of the following techniques reduces dimensionality?

a) Logistic Regression

b) PCA (Principal Component Analysis)

c) Random Forest

d) K-Means

20 / 45

20) Which of the following best describes a DataFrame in Pandas?

a) Scalar value

b) Multidimensional tensor

c) 1D labeled array

d) 2D tabular data structure

21 / 45

21) A confusion matrix is used to evaluate:

a) Clustering models

b) Classification models

c) Regression models

d) Dimensionality reduction

22 / 45

22) The process of cleaning and preparing raw data is called:

a) Data Modeling

b) Data Preprocessing

c) Data Mining

d) Data Visualization

23 / 45

23) Which cloud platforms provide Data Science services?

a) Azure

b) Google Cloud

c) All of the above

d) AWS

24 / 45

24) Which programming language is most popular in Data Science?

a) Python

b) C++

c) PHP

d) HTMLv

25 / 45

25) Which of these is an AI-based data visualization tool?

a) Power BI

b) TensorFlow

c) NumPy

d) Hadoop

26 / 45

26) Which of these is NOT a data visualization tool?

a) Matplotlib

b) Docker

c) Power BI

d) Tableau

27 / 45

27) Which of the following is NOT part of the Data Science workflow?

a) Music Production

b) Model Training

c) Data Cleaning

d) Data Collection

28 / 45

28) Which of these plots is best to visualize data distribution?

a) Line chart

b) Pie chart

c) Histogram

d) Scatter plot

29 / 45

29) Which of these is a common file format for datasets?

a) .exe

b) .csv

c) .mp3

d) .jpg

30 / 45

30) Which database is often used for unstructured big data?

a) PostgreSQL

b) SQLite

c) MongoDB

d) MySQL

31 / 45

31) Which type of chart is best to visualize categorical data distribution?

a) Bar chart

b) Histogram

c) Scatter plot

d) Line chart

32 / 45

32) What does “.isnull()” function in Pandas do?

a) Fills missing values

b) Checks for missing values

c) Returns dataset shape

d) Drops missing values

33 / 45

33) Logistic Regression is used for:

a) Clustering customers

b) Predicting categorical outcomes

c) Predicting continuous values

d) Dimensionality reduction

34 / 45

34) What does the Pandas function .groupby() do?

a) Joins multiple DataFrames

b) Splits dataset into train/test

c) Groups data based on conditions

d) Cleans missing values

35 / 45

35) Which SQL command is used to extract data from a database?

a) DELETE

b) SELECT

c) UPDATE

d) INSERT

36 / 45

36) In Data Science, what does EDA stand for?

a) Easy Data Application

b) Extended Data Analytics

c) Exploratory Data Analysis

d) Enhanced Data Algorithm

37 / 45

37) What does one-hot encoding do?

a) Combines multiple datasets

b) Converts categorical values into numerical form

c) Removes duplicate values

d) Normalizes data

38 / 45

38) Which visualization is best for showing correlation between two variables?

a) Pie chart

b) Bar chart

c) Histogram

d) Scatter plot

39 / 45

39) Which of the following is an example of supervised learning?

a) Grouping documents

b) Clustering customers

c) Market basket analysis

d) Predicting house prices

40 / 45

40) Which of the following is a regression algorithm?

a) K-Means

b) Decision Tree

c) Linear Regression

d) KNN

41 / 45

41) Which of these Python libraries is best for numerical computation?

a) Matplotlib

b) NumPy

c) Seaborn

d) Pandas

42 / 45

42) Which of the following is used for data visualization in Python?

a) SQL

b) Matplotlib

c) NumPy

d) Pandas

43 / 45

43) Which algorithm is used for classification tasks?

a) Apriori

b) Linear Regression

c) KNN (K-Nearest Neighbors)

d) PCA

44 / 45

44) In Data Science, “feature scaling” is required because:

a) It removes missing values

b) It reduces dataset size

c) It creates new features

d) It makes features comparable in scale

45 / 45

45) Which function is used in Pandas to view the first few rows of data?

a) .tail()

b) .info()

c) .describe()

d) .head()

Your score is

The average score is 96%

Exit

Important Notice:
Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Analyst D2

1 / 45

1) What is the role of a Data Analyst?

a) Write networking protocols

b) Develop operating systems

c) Build computer hardware

d) Extract insights from data

2 / 45

2) What is data visualization mainly used for?

a) Encrypt data

b) Make insights easier to understand

c) Increase data errors

d) Hide data

3 / 45

3) In analytics, KPI stands for:

a) Key Performance Indicator

b) Kernel Process Integration

c) Key Power Index

d) Knowledge Processing Input

4 / 45

4) Which database language is widely used in analytics?

a) SQL

b) C++

c) HTML

d) Java

5 / 45

5) Which visualization shows parts of a whole?

a) Pie chart

b) Scatter plot

c) Line chart

d) Histogram

6 / 45

6) Data Analytics mainly focuses on:

a) Analyzing past data for insights

b) Computer hardware maintenance

c) Creating new programming languages

d) Collecting data only

7 / 45

7) Which chart is suitable for showing cumulative values?

a) Area chart

b) Pie chart

c) Scatter plot

d) Line chart

8 / 45

8) Which chart is best to show correlation between two variables?

a) Scatter plot

b) Bar chart

c) Line chart

d) Pie chart

9 / 45

9) Which function in Power BI is used to create calculated columns?

a) NumPy

b) Python

c) SQL

d) DAX

10 / 45

10) Which SQL command is used to combine rows from two tables?

a) SELECT

b) DELETE

c) UPDATE

d) JOIN

11 / 45

11) Cleaning data involves:

a) Handling missing values

b) All of the above

c) Correcting errors

d) Removing duplicates

12 / 45

12) A slicer in Power BI is used for:

a) Filtering data interactively

b) Formatting reports

c) Creating charts

d) Joining tables

13 / 45

13) What is Big Data mainly characterized by?

a) Size only

b) Volume, Velocity, Variety

c) Value, Vision, Validation

d) SQL queries

14 / 45

14) Which of the following is the first step in Data Analytics?

a) Data Collection

b) Data Cleaning

c) Model Deployment

d) Data Visualization

15 / 45

15) In ETL, the "Transform" step includes:

a) Creating dashboards

b) Storing raw data

c) Data cleaning and formatting

d) Importing charts

16 / 45

16) What is the main benefit of using dashboards?

a) Storing raw files

b) Replacing databases

c) Interactive and real-time insights

d) Writing long reports

17 / 45

17) Which analytical method helps to find hidden patterns in data?

a) Data Mining

b) Data Cleaning

c) Data Loading

d) Data Reporting

18 / 45

18) What is the purpose of data transformation?

a) Store data in raw form

b) Erase unnecessary data permanently

c) Convert data into usable format

d) Visualize the data

19 / 45

19) A foreign key is used to:

a) Visualize dashboards

b) Store passwords

c) Define a unique record

d) Link two tables together

20 / 45

20) A bar chart is best suited for:

a) Showing trends over time

b) Comparing categories

c) Displaying proportions

d) Showing correlations

21 / 45

21) Which of these is an open-source tool for data visualization?

a) Power BI

b) All of the above

c) Google Data Studio

d) Tableau

22 / 45

22) . What does a Data Warehouse store?

a) Historical and structured data

b) Live gaming data

c) Emails only

d) Music files

23 / 45

23) What does ETL stand for?

a) Execute, Transfer, Log

b) Encrypt, Translate, Locate

c) Extract, Transform, Load

d) Enter, Test, Learn

24 / 45

24) Which tool is most widely used for business data visualization?

a) MS Word

b) Notepad

c) Power BI

d) Photoshop

25 / 45

25) In Power BI, relationships are built between:

a) DAX formulas

b) Reports

c) Tables

d) Charts

26 / 45

26) Which function in Power BI creates aggregate measures?

a) COUNT()

b) SUM()

c) All of the above

d) AVERAGE()

27 / 45

27) A primary key in a database is:

a) A column allowing duplicates

b) A chart type

c) A foreign key

d) A unique identifier for each record

28 / 45

28) DAX in Power BI stands for:

a) Database Access XML

b) Data Automated Execution

c) Data Analysis Expressions

d) Data Array Exchange

29 / 45

29) Data storytelling in analytics refers to:

a) Coding in SQL

b) Writing novels

c) Using data to entertain

d) Presenting data with narratives and visuals

30 / 45

30) In Power BI, data can be imported from:

a) All of the above

b) Excel

c) Cloud services

d) SQL databases

31 / 45

31) Power BI dashboards can be shared through:

a) Cloud storage

b) Power BI Service

c) All of the above

d) Emails

32 / 45

32) Which of these is NOT a data type in Power BI?

a) Music File

b) Whole Number

c) Decimal Number

d) Date/Time

33 / 45

33) Which chart is best for showing trends over time?

a) Line chart

b) Pie chart

c) Histogram

d) Scatter plot

34 / 45

34) Which type of join keeps only the matching records from two tables?

a) Inner Join

b) Left Join

c) Outer Join

d) Cross Join

35 / 45

35) In SQL, which clause is used to filter records?

a) SELECT

b) FROM

c) WHERE

d) JOIN

36 / 45

36) Predictive analytics is used to:

a) Describe the past

b) Clean datasets

c) Create pie charts

d) Forecast future outcomes

37 / 45

37) Which tool can be integrated with Power BI for advanced analytics?

a) R

b) Both a and b

c) Python

d) Photoshop

38 / 45

38) Which visualization is best for frequency distribution?

a) Pie chart

b) Line graph

c) Histogram

d) Area chart

39 / 45

39) Which of these is NOT a stage in the analytics process?

a) Data Visualization

b) Space Exploration

c) Data Collection

d) Data Cleaning

40 / 45

40) Prescriptive analytics helps to:

a) Suggest actions to optimize results

b) Delete unwanted data

c) Identify what happened

d) Visualize data

41 / 45

41) Which component of Power BI is used for creating reports?

a) Power BI Service

b) Power Query

c) Power BI Desktop

d) Power BI Mobile

42 / 45

42) Which cloud platform is popular for analytics?

a) All of the above

b) Google Cloud

c) AWS

d) Azure

43 / 45

43) The ultimate goal of Data Analytics is to:

a) Build hardware devices

b) Write computer programs

c) Generate insights for better decisions

d) Collect large amounts of raw data

44 / 45

44) Which of these is an example of descriptive analytics?

a) Optimizing supply chain

b) Explaining past sales trends

c) Predicting sales

d) Automating workflows

45 / 45

45) What is the main purpose of a dashboard in analytics?

a) Collect social media data

b) Train machine learning models

c) Present key insights visually

d) Store raw data

Your score is

The average score is 79%

Exit

Important Notice:
Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Science D3

1 / 45

1) What does R² (R-squared) measure in regression?

a) Proportion of variance explained

b) Precision

c) Accuracy

d) Error rate

2 / 45

2) Which algorithm is used for clustering in Data Science?

a) Logistic Regression

b) Linear Regression

c) Decision Trees

d) K-Means

3 / 45

3) Which operation in Pandas merges datasets based on a common column?

a) append()

b) concat()

c) merge()

d) join()

4 / 45

4) Which sampling method ensures all groups are represented?

a) Systematic Sampling

b) Cluster Sampling

c) Stratified Sampling

d) Random Sampling

5 / 45

5) Which of the following reduces the number of dimensions in data?

a) Classification

b) Decision Tree

c) PCA

d) Regression

6 / 45

6) The process of filling missing values in a dataset is called:

a) Encoding

b) Sampling

c) Imputation

d) Normalization

7 / 45

7) What is the main disadvantage of decision trees?

a) Cannot handle categorical variables

b) Prone to overfitting

c) Do not support regression tasks

d) High computational cost

8 / 45

8) Which of these is an example of unstructured data?

a) Text documents

b) Excel sheet

c) SQL table

d) CSV file

9 / 45

9) Which Python library is most widely used for handling structured data?

a) Scikit-learn

b) Matplotlib

c) Numpy

d) Pandas

10 / 45

10) One-hot encoding is used for:

a) Handling missing data

b) Scaling continuous features

c) Encoding categorical variables

d) Reducing dimensionality

11 / 45

11) Bagging in machine learning stands for:

a) Boosted Aggregation

b) Bootstrap Aggregation

c) Binary Aggregation

d) Bayesian Aggregation

12 / 45

12) Which of the following is the first step in the Data Science process?

a) Problem Definition

b) Model Evaluation

c) Data Visualization

d) Data Cleaning

13 / 45

13) What does “overfitting” mean in machine learning models?

a) Model ignores training data

b) Model has too few parameters

c) Model performs well on training but poorly on new data

d) Model performs poorly on both training and test data

14 / 45

14) What does normalization do to data?

a) Converts data into categories

b) Scales data to a fixed range

c) Adds noise to data

d) Removes missing values

15 / 45

15) Which is a key challenge in Data Science?

a) Simple model selection

b) Writing C++ code

c) Handling large and messy datasets

d) Lack of data

16 / 45

16) ROC curve is used to measure:

a) Clustering efficiency

b) Classification performance

c) Data preprocessing

d) Regression accuracy

17 / 45

17) Which of the following is NOT a Data Science task?

a) Data Collection

b) Game Design

c) Data Cleaning

d) Model Deployment

18 / 45

18) What is the purpose of Exploratory Data Analysis (EDA)?

a) To train machine learning models

b) To visualize patterns and insights in data

c) To deploy applications

d) To optimize algorithms

19 / 45

19) Which of these is an unsupervised learning algorithm?

a) Logistic Regression

b) Linear Regression

c) Decision Tree

d) K-Means Clustering

20 / 45

20) In Python, which function gives the first five rows of a DataFrame?

a) df.head()

b) df.top()

c) df.sample()

d) df.first()

21 / 45

21) Which distribution is used for binary classification problems?

a) Poisson distribution

b) Bernoulli distribution

c) Uniform distribution

d) Normal distribution

22 / 45

22) Which programming language is most popular in Data Science?

a) Ruby

b) Java

c) C++

d) Python

23 / 45

23) Which step comes last in a Data Science workflow?

a) Data Collection

b) Deployment & Monitoring

c) Model Building

d) Data Cleaning

24 / 45

24) Which metric is most appropriate for evaluating a regression model?

a) F1 Score

b) Precision

c) Mean Squared Error

d) Accuracy

25 / 45

25) A confusion matrix is mainly used for:

a) Regression tasks

b) Data Cleaning

c) Classification tasks

d) Clustering tasks

26 / 45

26) Which evaluation metric is better for imbalanced classification datasets?

a) R-squared

b) Accuracy

c) Recall

d) Mean Squared Error

27 / 45

27) Which of the following is an ensemble method?

a) Logistic Regression

b) Random Forest

c) Linear Regression

d) PCA

28 / 45

28) In supervised learning, the dataset must contain:

a) Random data

b) Only input features

c) Input features and labeled outputs

d) Only categorical variables

29 / 45

29) In Python, what does df.describe() do?

a) Displays null values

b) Provides summary statistics

c) Shows column names

d) Shows first few rows

30 / 45

30) What does feature engineering involve?

a) Training models

b) Creating new features from raw data

c) Deleting features

d) Encoding missing data

31 / 45

31) Which Pandas method is used to check missing values?

a) df.isna()

b) df.isnull()

c) df.hasnull()

d) df.clean()

32 / 45

32) What is the main purpose of feature scaling?

a) To add categorical variables

b) To bring all features to the same range

c) To increase missing values

d) To improve visualization

33 / 45

33) What is the purpose of data wrangling?

a) Deploying models

b) Visualizing data

c) Creating models

d) Cleaning and preparing data

34 / 45

34) Which method is used to handle multicollinearity in regression models?

a) Bagging

b) Clustering

c) Logistic Regression

d) PCA

35 / 45

35) Which library in Python is used for machine learning algorithms?

a) Matplotlib

b) Scikit-learn

c) Seaborn

d) Pandas

36 / 45

36) Which technique helps reduce variance in machine learning models?

a) Bagging

b) Feature scaling

c) Normalization

d) Boosting

37 / 45

37) Which Python function removes duplicates from a DataFrame?

a) df.remove()

b) df.unique()

c) df.drop_duplicates()

d) df.clean()

38 / 45

38) Which data type is best to represent categorical variables in Pandas?

a) int

b) bool

c) float

d) object

39 / 45

39) Which type of graph best shows the distribution of a numeric variable?

a) Bar chart

b) Scatter plot

c) Pie chart

d) Histogram

40 / 45

40) Which visualization technique is most suitable for checking correlation?

a) Pie chart

b) Heatmap

c) Bar chart

d) Line chart

41 / 45

41) Which Python library is best for creating interactive visualizations?

a) Matplotlib

b) Numpy

c) Plotly

d) Seaborn

42 / 45

42) Which SQL clause is used to group rows for aggregation in Data Science workflows?

a) GROUP BY

b) HAVING

c) WHERE

d) ORDER BY

43 / 45

43) The term “Big Data” usually refers to:

a) Labeled data

b) Balanced datasets

c) Small datasets

d) Unstructured and large datasets

44 / 45

44) What does cross-validation help with?

a) Reducing features

b) Checking model generalization

c) Preventing data cleaning

d) Increasing dataset size

45 / 45

45) Which plot is used to visualize the relationship between two continuous variables?

a) Pie chart

b) Scatter plot

c) Heatmap

d) Histogram

Your score is

The average score is 91%

Exit

Important Notice:
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Data Science D4

1 / 43

1) Bagging technique helps in:

a) Reducing variance

b) Data cleaning

c) Visualization

d) Increasing bias

2 / 43

2) Which of these is a classification algorithm?

a) Linear Regression

b) PCA

c) Gradient Descent

d) KNN

3 / 43

3) Which ML technique is inspired by human brain neurons?

a) Neural Networks

b) Clustering

c) Regression

d) Decision Trees

4 / 43

4) ROC curve is used for:

a) Data Visualization

b) Data Cleaning

c) Regression model evaluation

d) Classification model evaluation

5 / 43

5) Which of the following is used for topic modeling in NLP?

a) PCA

b) RNN

c) CNN

d) LDA

6 / 43

6) Which of these is a data visualization library in Python?

a) Matplotlib

b) TensorFlow

c) NumPy

d) Pandas

7 / 43

7) Tokenization in NLP means:

a) Encrypting text

b) Removing duplicate

c) Combining words

d) Splitting text into smaller units

8 / 43

8) Which of the following is NOT supervised learning?

a) Linear Regression

b) Logistic Regression

c) K-Means Clustering

d) Decision Trees

9 / 43

9) Which library is used for numerical computing in Python?

a) Matplotlib

b) Sklearn

c) NumPy

d) Seaborn

10 / 43

10) Which of the following evaluates regression models?

a) Precision

b) RMSE

c) F1-score

d) Recall

11 / 43

11) Which of these is an unsupervised learning algorithm?

a) Logistic Regression

b) Decision Trees

c) K-Means

d) Linear Regression

12 / 43

12) Which clustering algorithm is density-based?

a) K-Means

b) Hierarchical

c) PCA

d) DBSCAN

13 / 43

13) Which cloud service provides ML tools?

a) MS Word

b) GitHub

c) Photoshop

d) AWS SageMaker

14 / 43

14) Which of these languages is most popular for Data Science?

a) CSS

b) HTML

c) C#

d) Python

15 / 43

15) Which is an activation function in Neural Networks?

a) All of the above

b) Sigmoid

c) Tanh

d) ReLU

16 / 43

16) Deep learning is mainly based on:

a) Neural Networks

b) SVM

c) Decision Trees

d) Regression

17 / 43

17) Cross-validation is used to:

a) Reduce dataset size

b) Remove missing values

c) Test model generalization

d) Increase model complexity

18 / 43

18) What is the full form of NLP?

a) Neural Learning Process

b) Natural Language Processing

c) National Learning Protocol

d) Network Language Programming

19 / 43

19) Which ML algorithm is most interpretable?

a) Random Forest

b) Gradient Boosting

c) Neural Networks

d) Decision Trees

20 / 43

20) In NLP, removing stop words is part of:

a) None

b) Preprocessing

c) Model Training

d) Visualization

21 / 43

21) Which library is most used for deep learning?

a) TensorFlow

b) Matplotlib

c) Seaborn

d) Pandas

22 / 43

22) Which of the following is NOT a type of bias in data science?

a) Musical bias

b) Confirmation bias

c) Sampling bias

d) Algorithmic bias

23 / 43

23) Which concept allows machines to learn from data?

a) Web Development

b) Networking

c) Compilers

d) Machine Learning

24 / 43

24) Which method handles class imbalance?

a) SMOTE

b) DBSCAN

c) K-Means

d) PCA

25 / 43

25) F1-score is the harmonic mean of:

a) Precision and RMSE

b) Precision and Recall

c) Accuracy and Recall

d) Recall and RMSE

26 / 43

26) Which is an ensemble method?

a) Logistic Regression

b) K-Means

c) PCA

d) Random Forest

27 / 43

27) Gradient Descent is used for:

a) Data Collection

b) Data Cleaning

c) Visualization

d) Optimization

28 / 43

28) Which of these is a data preprocessing step?

a) Model deployment

b) Data cleaning

c) Data visualization

d) None

29 / 43

29) Which of the following is NOT supervised learning?

a) Decision Trees

b) K-Means Clustering

c) Logistic Regression

d) Linear Regression

30 / 43

30) Which algorithm is best for spam email classification?

a) Linear Regression

b) K-Means

c) Naïve Bayes

d) PCA

31 / 43

31) Which ML model is good for image classification?

a) Logistic Regression

b) CNN

c) Naïve Bayes

d) Decision Trees

32 / 43

32) Which metric is used for classification models?

a) RMSE

b) MSE

c) MAE

d) Accuracy

33 / 43

33) The ultimate goal of Data Science is:

a) Develop operating systems

b) Build applications

c) Extract knowledge and insights from data

d) Create databases

34 / 43

34) Which of these is NOT a step in CRISP-DM framework?

a) Deployment

b) Game Development

c) Data Understanding

d) Data Preparation

35 / 43

35) Data Science lifecycle ends with:

a) Deployment

b) Cleaning

c) Visualization

d) Model Building

36 / 43

36) Which algorithm is called "lazy learner"?

a) Decision Tree

b) SVM

c) Naïve Bayes

d) KNN

37 / 43

37) Backpropagation is used in:

a) Decision Trees

b) Neural Networks

c) Random Forest

d) Clustering

38 / 43

38) Which ML algorithm is most interpretable?

a) Neural Networks

b) Decision Trees

c) Random Forest

d) Gradient Boosting

39 / 43

39) Which algorithm is widely used for recommendation systems?

a) Collaborative Filtering

b) Decision Trees

c) Linear Regression

d) K-Means

40 / 43

40) Confusion matrix is used in:

a) PCA

b) Regression

c) Clustering

d) Classification

41 / 43

41) Which type of variable has numeric values?

a) Ordinal

b) Categorical

c) Continuous

d) Nominal

42 / 43

42) What is Data Science primarily concerned with?

a) Extracting insights from data

b) Building websites

c) Creating animations

d) Designing circuits

43 / 43

43) Hyperparameters are:

a) Predictions

b) Learned during training

c) Set before training

d) Model outputs

Your score is

The average score is 95%

Exit

Important Notice:
Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Science D6

1 / 42

1) In NLP, TF-IDF is used for:

a) Feature extraction

b) Visualization

c) Model training

d) Data cleaning

2 / 42

2) Which of these is a supervised algorithm?

a) Apriori

b) K-Means

c) PCA

d) Linear Regression

3 / 42

3) In statistics, variance measures:

a) Spread of data

b) Frequency

c) Central value

d) Count of data

4 / 42

4) What is the purpose of a confusion matrix?

a) Plot graphs

b) Show correlations

c) Collect raw data

d) Summarize classification results

5 / 42

5) Feature scaling techniques include:

a) Normalization

b) Standardization

c) Both

d) None

6 / 42

6) In machine learning, classification problems predict:

a) Random numbers

b) Continuous values

c) Missing values

d) Categories

7 / 42

7) Which of these is an ensemble method?

a) Decision Tree

b) KNN

c) Random Forest

d) Linear Regression

8 / 42

8) Which type of learning uses labeled datasets?

a) Semi-supervised

b) Reinforcement

c) Unsupervised

d) Supervised

9 / 42

9) Logistic regression is mainly used for:

a) Regression tasks

b) Time-series

c) Classification tasks

d) Clustering

10 / 42

10) Which plot is best for visualizing correlation?

a) Scatter plot

b) Histogram

c) Pie chart

d) Bar chart

11 / 42

11) Which library is commonly used for machine learning in Python?

a) Scikit-learn

b) Matplotlib

c) NumPy

d) Pandas

12 / 42

12) Bagging improves model:

a) None

b) Variance

c) Noise

d) Bias

13 / 42

13) Which Python library is most commonly used for numerical computations?

a) Seaborn

b) Pandas

c) NumPy

d) SciPy

14 / 42

14) Which algorithm is used in recommendation systems?

a) Linear regression

b) PCA

c) Collaborative filtering

d) K-Means

15 / 42

15) Which evaluation metric is NOT for regression?

a) Accuracy

b) R²

c) RMSE

d) MAE

16 / 42

16) Which is an unsupervised technique?

a) Logistic Regression

b) Naive Bayes

c) K-Means

d) Random Forest

17 / 42

17) Which step comes first in a Data Science project?

a) Data collection

b) Visualization

c) Model deployment

d) Feature engineering

18 / 42

18) Which time-series model is commonly used?

a) PCA

b) ARIMA

c) Decision Tree

d) K-Means

19 / 42

19) Which type of bias occurs when training data isn’t representative?

a) Confirmation bias

b) Survivorship bias

c) Selection bias

d) Measurement bias

20 / 42

20) K in KNN refers to:

a) Number of features

b) Number of neighbors

c) Number of clusters

d) Number of records

21 / 42

21) Cross-validation is used to:

a) Reduce training data

b) Increase noise

c) Improve generalization

d) Merge datasets

22 / 42

22) Which of these is a cloud platform for Data Science?

a) All of the above

b) Azure ML

c) Google Colab

d) AWS SageMaker

23 / 42

23) Which visualization helps detect skewness?

a) Pie chart

b) Histogram

c) Bar chart

d) Scatter plot

24 / 42

24) Overfitting occurs when a model:

a) Performs well on test data

b) Has low complexity

c) Ignores training data

d) Learns noise in data

25 / 42

25) Gradient Descent is an algorithm for:

a) Visualization

b) Optimization

c) Data cleaning

d) Data collection

26 / 42

26) Which library is used for deep learning in Python?

a) TensorFlow

b) NumPy

c) Matplotlib

d) Pandas

27 / 42

27) Which of the following is the primary goal of Data Science?

a) Collect hardware

b) Extract insights

c) Store data

d) Delete data

28 / 42

28) Which of these is a dimensionality reduction method?

a) t-SNE

b) PCA

c) None

d) Both

29 / 42

29) In decision trees, leaf nodes represent:

a) Root nodes

b) Splits

c) Final outcomes

d) Features

30 / 42

30) Which of the following detects overfitting?

a) High accuracy in both sets

b) High test accuracy only

c) Low training accuracy

d) High training accuracy, low test accuracy

31 / 42

31) Which process splits data into training and test sets?

a) Bootstrapping

b) Sampling

c) Data splitting

d) Cross-validation

32 / 42

32) Which algorithm is suitable for market basket analysis?

a) Linear Regression

b) KNN

c) Apriori

d) SVM

33 / 42

33) The F1-score is the harmonic mean of:

a) Sensitivity and Accuracy

b) Accuracy and Precision

c) Precision and Recall

d) Recall and Specificity

34 / 42

34) Which of the following measures similarity between vectors?

a) All of the above

b) Manhattan distance

c) Cosine similarity

d) Euclidean distance

35 / 42

35) Which metric evaluates classification models?

a) All of the above

b) Accuracy

c) Precision

d) Recall

36 / 42

36) Which of the following models handles non-linear data well?

a) Decision Tree

b) Linear Regression

c) PCA

d) Logistic Regression

37 / 42

37) Which evaluation metric is best for imbalanced datasets?

a) Accuracy

b) Mean Squared Error

c) R-Squared

d) F1 Score

38 / 42

38) Data Science is an interdisciplinary field combining?

a) All of the above

b) Statistics

c) Domain Knowledge

d) Computer Science

39 / 42

39) In NLP, stop words are:

a) Mathematical symbols

b) Words filtered out

c) Very important words

d) Numbers

40 / 42

40) Which regularization technique adds absolute value penalties?

a) None

b) ElasticNet

c) L1 (Lasso)

d) L2 (Ridge)

41 / 42

41) In statistics, p-value is used for:

a) Regression

b) Clustering

c) Classification

d) Hypothesis testing

42 / 42

42) Which of these is a regression evaluation metric?

a) MAE

b) All of the above

c) RMSE

d) R²

Your score is

The average score is 0%

Exit

Important Notice:
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Data Science D5

1 / 43

1) A feature in Data Science means:

a) A variable or input column

b) A model output

c) An error measure

d) A predictione

2 / 43

2) Which metric is used in regression problems?

a) Recall

b) Accuracy

c) Precision

d) RMSE (Root Mean Square Error)

3 / 43

3) Which of the following is a key step in Data Science workflow?

a) Data collection

b) Model building

c) Data cleaning

d) All of the above

4 / 43

4) Which metric is used for clustering quality?

a) silhouette score

b) Accuracy

c) RMSE

d) R-squared

5 / 43

5) In clustering, the “elbow method” is used to:

a) Handle missing data

b) Train the model faster

c) Choose the number of clusters

d) Reduce dimensions

6 / 43

6) Which dataset is commonly used as a beginner dataset in Data Science?

a) Iris

b) All of the above

c) Titanic

d) MNIST

7 / 43

7) Which of the following is a deep learning library?

a) TensorFlow

b) All of the above

c) Keras

d) PyTorch

8 / 43

8) Principal Component Analysis (PCA) is used for:

a) Dimensionality reduction

b) Clustering

c) Classification

d) Regression

9 / 43

9) Which measure is best for classification accuracy?

a) Mean Squared Error

b) R-squared

c) Correlation coefficient

d) Precision and Recall

10 / 43

10) Which evaluation method is best for time-series forecasting?

a) Cross-validation with random splits

b) Time-based split

c) Random subsampling

d) Bootstrap sampling

11 / 43

11) Which visualization is best for correlation analysis?

a) Pie chart

b) Line chart

c) Histogram

d) Heatmap

12 / 43

12) Which evaluation metric is best for imbalanced data classification?

a) Mean Absolute Error

b) Accuracy

c) Precision, Recall, F1-score

d) RMSE

13 / 43

13) Which model is best for sequence data like time series?

a) RNN (Recurrent Neural Network)

b) Random Forest

c) CNN

d) Decision Trees

14 / 43

14) NLP stands for:

a) Natural Language Processing

b) None

c) Non-linear Programming

d) Neural Layer Prediction

15 / 43

15) Which distance measure is often used in KNN algorithm?

a) All of the above

b) Cosine similarity

c) Euclidean distance

d) Manhattan distance

16 / 43

16) The ROC curve shows:

a) Relationship between True Positive Rate and False Positive Rate

b) Distribution of errors

c) Model accuracy

d) Relationship between Precision and Recall

17 / 43

17) Hyperparameter tuning can be done using:

a) All of the above

b) Bayesian Optimization

c) Grid Search

d) Random Search

18 / 43

18) Which library is mainly used for linear algebra in Python?

a) NumPy

b) flask

c) Matplotlib

d) Pandas

19 / 43

19) Supervised learning is based on:

a) No data

b) Labeled data

c) Random data

d) Unlabeled data

20 / 43

20) Logistic regression is used for:

a) Binary classification

b) Predicting continuous values

c) Clustering

d) Visualization

21 / 43

21) Which is an example of unsupervised learning?

a) K-Means clustering

b) Linear regression

c) Decision trees

d) Logistic regression

22 / 43

22) Confusion matrix is used in:

a) Clustering

b) Visualization

c) Regression

d) Classification

23 / 43

23) L1 and L2 penalties are used in:

a) PCA

b) Regularization

c) Clustering

d) Normalization

24 / 43

24) Which of the following is NOT a machine learning algorithm?

a) Naïve Bayes

b) SQL Join

c) Random Forest

d) SVM

25 / 43

25) Random Forest is based on:

a) Regression only

b) Bagging technique

c) boosting technique

d) Single decision tree

26 / 43

26) Which technique is used to handle class imbalance?

a) SMOTE

b) Oversampling

c) All of the above

d) Undersampling

27 / 43

27) Ensemble learning means:

a) Reducing dataset size

b) Using deep learning

c) Using one strong model

d) Combining multiple models for better accuracy

28 / 43

28) What is the primary goal of Data Science?

a) Making animations

b) Building websites

c) Storing large datasets

d) Extracting insights and knowledge from data

29 / 43

29) Bag of Words is a technique used in:

a) Image processing

b) Regression

c) Clustering

d) NLP

30 / 43

30) ReLU is a type of:

a) Loss function

b) Optimizer

c) Regularizer

d) Activation function

31 / 43

31) Which technique converts categorical data into numerical?

a) One-hot encoding

b) Standardization

c) PCA

d) Normalization

32 / 43

32) Which method reduces overfitting in decision trees?

a) All of the above

b) Boosting

c) Pruning

d) Bagging

33 / 43

33) Gradient Boosting builds models:

a) Randomly

b) Sequentially

c) Independently

d) Without trees

34 / 43

34) Which type of learning requires reward-based training?

a) Unsupervised learning

b) Semi-supervised learning

c) Supervised Learning

d) Reinforcement learning

35 / 43

35) Which plot shows model accuracy over training epochs?

a) Histogram

b) Scatter plot

c) Learning curve

d) Pie chart

36 / 43

36) Which algorithm is best for image recognition tasks?

a) Linear regression

b) Decision tree

c) CNN (Convolutional Neural Network)

d) Logistic regression

37 / 43

37) In machine learning, “overfitting” means:

a) Model generalizes well

b) Model is too simple

c) Model performs badly on both datasets

d) Model performs well on training data but poorly on new data

38 / 43

38) In Python, which library is commonly used for machine learning?

a) BeautifulSoup

b) Django

c) ) Flask

d) Scikit-learn

39 / 43

39) Which step ensures model generalization?

a) Cross-validation

b) All of the above

c) Regularization

d) Using test data

40 / 43

40) Which programming languages are most used in Data Science?

a) Swift and Kotlin

b) JavaScript and PHP

c) C and C++

d) Python and R

41 / 43

41) Which method is used to split data into training and testing sets?

a) train_test_split()

b) data_partition()

c) random_split()

d) divide_data()

42 / 43

42) Which optimizer is widely used in deep learning?

a) SGD

b) RMSProp

c) Adam

d) All of the above

43 / 43

43) Which algorithm is NOT supervised learning?

a) K-Means clustering

b) Linear regression

c) Decision trees

d) Logistic regression

Your score is

The average score is 0%

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Once you start the quiz, you will not be able to pause, exit, or restart it. Please ensure you are ready before beginning.

Data Science D7

1 / 45

1) Which of these is a Python library for machine learning?

a) Scikit-learn

b) BeautifulSoup

c) Flask

d) Matplotlib

2 / 45

2) which machine learning task does clustering belong to?

a) Semi-supervised learning

b) Supervised learning

c) Reinforcement learning

d) Unsupervised learning

3 / 45

3) Which method is used for splitting datasets in machine learning?

a) divide_data()

b) train_test_split()

c) partition()

d) split_data()

4 / 45

4) Which of these is a feature scaling method?

a) Robust Scaler

b) All of the above

c) Min-Max Normalization

d) Standardization

5 / 45

5) Which data visualization is best for correlation matrices?

a) Pie chart

b) Heatmap

c) Histogram

d) Bar chart

6 / 45

6) What does PCA (Principal Component Analysis) do?

a) Reduces dimensionality

b) Builds decision trees

c) Increases dataset size

d) Creates clusters

7 / 45

7) Which evaluation metric is useful for imbalanced classification?

a) R² Score

b) Precision and Recall

c) RMSE

d) Accuracy

8 / 45

8) Which step comes after data cleaning in the DS process?

a) data Reporting

b) Data Archiving

c) Data Modeling

d) Data Collection

9 / 45

9) What does “bias” in a machine learning model indicate?

a) Error from incorrect assumptions

b) Overfitting

c) Random fluctuations

d) Error due to variance

10 / 45

10) Which measure of central tendency is affected most by outliers?

a) None

b) Mode

c) Median

d) Mean

11 / 45

11) Which of these is NOT a supervised learning algorithm?

a) Random Forest

b) Decision Tree

c) Linear Regression

d) K-means

12 / 45

12) Which of the following is NOT a step in the Data Science lifecycle?

a) Hardware Manufacturing

b) Data Cleaning

c) Data Visualization

d) Data Collection

13 / 45

13) Which algorithm is best suited for grouping customers by behavior?

a) Logistic Regression

b) K-means Clustering

c) Naive Bayes

d) Decision Trees

14 / 45

14) Which library in Python is used for deep learning?

a) Seaborn

b) NumPy

c) Pandas

d) TensorFlow

15 / 45

15) Which algorithm is commonly used for anomaly detection?

a) Logistic Regression

b) Linear Regression

c) Isolation Forest

d) Decision Trees

16 / 45

16) What is the main use of cross-validation?

a) Estimate model performance on unseen dat

b) Reduce dataset size

c) Improve accuracy on training data

d) Increase dataset variance

17 / 45

17) Which technique reduces multicollinearity in regression?

a) Cross-validation

b) Regularization

c) Overfitting

d) Normalization

18 / 45

18) Which model is commonly used for binary classification?

a) Logistic Regression

b) PCA

c) K-means

d) Linear Regression

19 / 45

19) Which ML algorithm is inspired by the biological nervous system?

a) Regression

b) Decision Trees

c) Neural Networks

d) KNN

20 / 45

20) Which metric is commonly used for regression tasks?

a) Recall

b) Accuracy

c) R² Score

d) Precision

21 / 45

21) Which is a supervised learning task?

a) Classification

b) Association rule mining

c) Anomaly detection

d) Clustering

22 / 45

22) Which of these is an unsupervised learning algorithm?

a) K-means

b) Naive Bayes

c) Random Forest

d) Logistic Regression

23 / 45

23) Which type of variable has no natural order

a) Continuous

b) Categorical (Nominal)

c) Interval

d) Ordinal

24 / 45

24) Which type of learning uses both labeled and unlabeled data?

a) Reinforcement

b) Supervised

c) Unsupervised

d) Semi-supervised

25 / 45

25) Which dataset is commonly used for image recognition experiments?

a) Titanic

b) FIFA 21 Dataset

c) MNIST

d) Boston Housing

26 / 45

26) Which Python library is used for statistical modeling?

a) Statsmodels

b) TensorFlow

c) Scikit-learn

d) Matplotlib

27 / 45

27) Which technique prevents overfitting in decision trees?

a) Pruning

b) Standardization

c) Normalization

d) Splitting

28 / 45

28) Which activation function is commonly used in deep learning?

a) Sigmoid

b) Tanh

c) All of the above

d) ReLU

29 / 45

29) Which distribution is often assumed in statistics?

a) Poisson distribution

b) Uniform distribution

c) Normal distribution

d) Binomial distribution

30 / 45

30) which of the following is a feature selection method?

a) Logistic regression

b) Decision tree pruning

c) Chi-square test

d) Gradient descent

31 / 45

31) What is overfitting in machine learning?

a) Model performs poorly on both train and test data

b) Model ignores irrelevant features

c) Model fits all test data perfectly

d) Model works well on training but poorly on test data

32 / 45

32) Which data type is continuous?

a) Gender

b) Temperature

c) Country

d) Eye color

33 / 45

33) Which of the following is an example of classification?

a) Predicting house prices

b) Predicting rainfall amount

c) Predicting stock prices

d) Predicting email as spam or not spam

34 / 45

34) Which of the following is an example of regression?

a) Predicting spam emails

b) Predicting movie ratings

c) Predicting house prices

d) Classifying animals

35 / 45

35) Which type of learning involves agents interacting with an environment?

a) Supervised Learning

b) Unsupervised Learning

c) Reinforcement Learning

d) Semi-supervised Learning

36 / 45

36) Which technique combines bootstrapping with decision trees?

a) Bagging

b) Random Forest

c) All of the above

d) Boosting

37 / 45

37) Which evaluation metric is used for regression models?

a) Precision

b) RMSE (Root Mean Square Error)

c) Accuracy

d) F1-score

38 / 45

38) Which Python library is most commonly used for data manipulation?

a) NumPy

b) Pandas

c) Seaborn

d) TensorFlow

39 / 45

39) Which model combines predictions from multiple models?

a) Neural network

b) Regression model

c) Ensemble model

d) Decision tree

40 / 45

40) Which is a key difference between Data Science and Data Analytics?

a) Data Analytics always uses AI

b) Data Science focuses more on predictive modeling

c) Data Analytics focuses only on visualization

d) Data Science ignores machine learning

41 / 45

41) Which algorithm is suitable for market basket analysis?

a) Apriori

b) SVM

c) Random Forest

d) KNN

42 / 45

42) Which ML algorithm is known as a lazy learner?

a) Naive Bayes

b) KNN (K-Nearest Neighbors)

c) Decision Trees

d) SVM

43 / 45

43) Which term refers to selecting the best parameters for a model?

a) Cross-validation

b) Data cleaning

c) Model tuning

d) Scaling

44 / 45

44) Which optimization technique is used in training neural networks?

a) PCA

b) Sampling

c) Clustering

d) Gradient Descent

45 / 45

45) Which of the following best defines Data Science?

a) Managing Excel sheets

b) Study of databases only

c) Extracting knowledge and insights from data

d) Building websites with data

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