IBM Data Science Professional Exam Verified
Questions, Correct Answers, and Detailed
Explanations for Computer Science Students||Already
Graded A+
1. Which of the following is the correct order of steps in the Data
Science Life Cycle?
A) Model → Data → Evaluate → Deploy → Prepare
B) Ask → Prepare → Analyze → Share → Act
C) Collect → Clean → Visualize → Predict → Evaluate
D) Clean → Analyze → Collect → Model → Deploy
The Data Science Life Cycle begins with understanding the problem
(Ask), then gathering and cleaning data (Prepare), analyzing the data
(Analyze), sharing results (Share), and taking action based on insights
(Act).
2. In Python, which library is commonly used for numerical
computing with arrays?
A) matplotlib
B) NumPy
C) pandas
D) seaborn
NumPy provides efficient array operations and mathematical
functions, forming the foundation for many scientific computing tasks
in Python.
3. Which of the following is NOT a supervised learning algorithm?
A) Linear Regression
B) Decision Trees
,C) K-Means Clustering
D) Logistic Regression
K-Means Clustering is an unsupervised algorithm used to group data
without labeled outcomes.
4. What is the main purpose of data visualization?
A) To store data
B) To clean data
C) To create models
D) To communicate insights from data
Visualization helps stakeholders understand patterns, trends, and
outliers in data.
5. In statistics, the mean, median, and mode are measures of:
A) Variability
B) Central Tendency
C) Correlation
D) Distribution
Central tendency measures describe the center point of a dataset.
6. Which IBM tool is commonly used for building machine learning
models without coding?
A) Jupyter Notebook
B) IBM Watson Studio
C) Python IDLE
D) RStudio
IBM Watson Studio provides a drag-and-drop interface for building
and deploying models easily.
,7. What does CSV stand for in data formats?
A) Comma Sequential Values
B) Column Separated Values
C) Comma Separated Values
D) Character Separated Variables
CSV files store tabular data with values separated by commas.
8. What type of chart is best for showing the relationship between
two numeric variables?
A) Pie chart
B) Histogram
C) Scatter plot
D) Bar chart
Scatter plots display the correlation or relationship between two
continuous variables.
9. In Python, which library is commonly used for data manipulation
and analysis?
A) NumPy
B) matplotlib
C) pandas
D) seaborn
Pandas provides data structures like DataFrames, ideal for handling
tabular data.
10. Which of the following is an example of a classification
problem?
, A) Predicting house prices
B) Predicting if an email is spam or not
C) Predicting stock price
D) Predicting temperature
Classification problems predict categorical outcomes, like spam vs.
not spam.
11. Which metric is commonly used to evaluate regression models?
A) Accuracy
B) Mean Squared Error (MSE)
C) Confusion Matrix
D) ROC Curve
MSE measures the average squared difference between predicted
and actual values.
12. What is the key difference between supervised and
unsupervised learning?
A) Supervised learning uses only numbers
B) Unsupervised learning predicts the future
C) Supervised learning uses labeled data; unsupervised learning
uses unlabeled data
D) Unsupervised learning is faster
Supervised learning requires labeled outcomes to train models,
whereas unsupervised does not.
13. Which of the following best describes overfitting?
A) Model performs poorly on training data
B) Model performs well on unseen data
Questions, Correct Answers, and Detailed
Explanations for Computer Science Students||Already
Graded A+
1. Which of the following is the correct order of steps in the Data
Science Life Cycle?
A) Model → Data → Evaluate → Deploy → Prepare
B) Ask → Prepare → Analyze → Share → Act
C) Collect → Clean → Visualize → Predict → Evaluate
D) Clean → Analyze → Collect → Model → Deploy
The Data Science Life Cycle begins with understanding the problem
(Ask), then gathering and cleaning data (Prepare), analyzing the data
(Analyze), sharing results (Share), and taking action based on insights
(Act).
2. In Python, which library is commonly used for numerical
computing with arrays?
A) matplotlib
B) NumPy
C) pandas
D) seaborn
NumPy provides efficient array operations and mathematical
functions, forming the foundation for many scientific computing tasks
in Python.
3. Which of the following is NOT a supervised learning algorithm?
A) Linear Regression
B) Decision Trees
,C) K-Means Clustering
D) Logistic Regression
K-Means Clustering is an unsupervised algorithm used to group data
without labeled outcomes.
4. What is the main purpose of data visualization?
A) To store data
B) To clean data
C) To create models
D) To communicate insights from data
Visualization helps stakeholders understand patterns, trends, and
outliers in data.
5. In statistics, the mean, median, and mode are measures of:
A) Variability
B) Central Tendency
C) Correlation
D) Distribution
Central tendency measures describe the center point of a dataset.
6. Which IBM tool is commonly used for building machine learning
models without coding?
A) Jupyter Notebook
B) IBM Watson Studio
C) Python IDLE
D) RStudio
IBM Watson Studio provides a drag-and-drop interface for building
and deploying models easily.
,7. What does CSV stand for in data formats?
A) Comma Sequential Values
B) Column Separated Values
C) Comma Separated Values
D) Character Separated Variables
CSV files store tabular data with values separated by commas.
8. What type of chart is best for showing the relationship between
two numeric variables?
A) Pie chart
B) Histogram
C) Scatter plot
D) Bar chart
Scatter plots display the correlation or relationship between two
continuous variables.
9. In Python, which library is commonly used for data manipulation
and analysis?
A) NumPy
B) matplotlib
C) pandas
D) seaborn
Pandas provides data structures like DataFrames, ideal for handling
tabular data.
10. Which of the following is an example of a classification
problem?
, A) Predicting house prices
B) Predicting if an email is spam or not
C) Predicting stock price
D) Predicting temperature
Classification problems predict categorical outcomes, like spam vs.
not spam.
11. Which metric is commonly used to evaluate regression models?
A) Accuracy
B) Mean Squared Error (MSE)
C) Confusion Matrix
D) ROC Curve
MSE measures the average squared difference between predicted
and actual values.
12. What is the key difference between supervised and
unsupervised learning?
A) Supervised learning uses only numbers
B) Unsupervised learning predicts the future
C) Supervised learning uses labeled data; unsupervised learning
uses unlabeled data
D) Unsupervised learning is faster
Supervised learning requires labeled outcomes to train models,
whereas unsupervised does not.
13. Which of the following best describes overfitting?
A) Model performs poorly on training data
B) Model performs well on unseen data
