❓ Question 2:
What is a z-score?

1. A standardized value that indicates the number of standard deviations an observation is from the mean.
2. The range between the highest and lowest values in a set of data.
3. A measure of the spread of a set of data.
4. A measure of central tendency of a set of data.

✅ Correct Response: 1

Explanation: In statistics, a z-score is a standardized value that indicates the number of standard deviations an observation is from the mean of a set of datIt is used to compare values from different normal distributions and to calculate probabilities.

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❓ Question 3:
The function ____ is used to load a CSV file into a DataFrame in Pandas.

Option 1: read_csv()
Option 2: to_csv()
Option 3: read_excel()
Option 4: load_csv()

✅ Correct Response: 1

Explanation: The read_csv() function is used to load a CSV file into a DataFrame in Pandas. It provides many parameters to read CSV data in a flexible way.

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❓ Question 4:
When creating a DataFrame in Pandas, which data structures can be used as input?

Option 1: Dictionaries
Option 2: NumPy ndarrays
Option 3: Series
Option 4: Python Lists

✅ Correct Response: 1, 2, 3, 4

Explanation: When creating a DataFrame in Pandas, various data structures can be used as input. These include Dictionaries, NumPy ndarrays, Pandas Series, and Python Lists. Each of these can be converted into a DataFrame, which then allows for flexible data manipulations in the form of a structured grid.

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❓Question 5:
The NumPy library is often used for performing ____ operations on large arrays and matrices.

Option 1: Mathematical
Option 2: Statistical
Option 3: Linear algebra
Option 4: All of the above
 
✅ Correct Response: 4

Explanation: The NumPy library is often used for performing mathematical, statistical, and linear algebra operations on large arrays and matrices. It provides efficient functions and operations to manipulate and analyze numerical data. NumPy is widely used in scientific computing and data analysis tasks

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❓ Question 6:
What is a decision tree regression?

1. A type of regression that uses multiple decision trees to make predictions.

2. A method for clustering data points into groups based on their similarity.

3. A technique for dimensionality reduction by projecting the data onto a lower-dimensional space.

4. A method for estimating the causal effects of interventions using graphical models.

✅ Correct Response: 1

Explanation: Decision tree regression is a type of regression that uses a decision tree to make predictions. The decision tree is constructed by recursively partitioning the input space into regions based on the values of the input variables, and then fitting a simple model, such as a constant or a linear function, in each region. Decision tree regression is a simple and interpretable model that can capture nonlinear relationships between the input variables and the response variable.

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❓ Question 7:
____ is a process of creating abstract classes and methods in Python.

Option 1: Inheritance
Option 2: Polymorphism
Option 3: Encapsulation
Option 4: Abstraction
 
✅ Correct Response: 4

Explanation: Abstraction is the process of creating abstract classes and methods in Python. Abstraction allows you to define the interface or contract for derived classes to follow, without providing the implementation details. This concept helps in achieving code modularity and creating more maintainable and flexible code.

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❓ Question 8:
What is a support vector regression (SVR)?

1. A technique for identifying the most important features in a dataset.

2. A regression method that finds the hyperplane that maximizes the margin between the data points and the hyperplane.

3. A clustering algorithm for grouping similar data points together.

4. A method for estimating the causal effects of interventions using graphical models.

✅ Correct Response: 2

Explanation: Support vector regression (SVR) is a regression method that finds the hyperplane that maximizes the margin between the data points and the hyperplane. SVR is commonly used for regression problems in which the data points have many irrelevant features and only a few relevant features. It is a type of kernel method that can be used with nonlinear data.

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❓ Question 9:
What is Explainable Artificial Intelligence (XAI)?

1. A type of artificial intelligence that is transparent and interpretable to humans.

2. A type of artificial intelligence that is designed to mimic human intelligence.

3. A type of artificial intelligence that can learn and adapt from experience.

4. A type of artificial intelligence that is optimized for a specific task or objective.

✅ Correct Response: 1

Explanation: Explainable Artificial Intelligence (XAI) is a type of artificial intelligence that is transparent and interpretable to humans. It aims to make machine learning models and decision-making processes more transparent and understandable to users and stakeholders. XAI techniques include feature importance analysis, saliency maps, decision trees, and other methods for visualizing and explaining the internal workings of AI models. XAI is an important research area in AI ethics and regulation.

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❓Question 10:
What is the difference between precision and recall?

1. Precision measures how many of the predicted positive cases are actually positive, while recall measures how many of the actual positive cases were correctly identified by the model

2. Precision measures how many of the actual positive cases were correctly identified by the model, while recall measures how many of the predicted positive cases are actually positive

3. Precision and recall are the same thing

4. Precision and recall are both measures of how well a model is able to identify positive cases

✅ Correct Response: 1

Explanation: Precision and recall are both measures of how well a model is able to identify positive cases, but they focus on different aspects of this task. Precision measures how many of the predicted positive cases are actually positive, while recall measures how many of the actual positive cases were correctly identified by the model.

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❓ Question 11:
What is the difference between a random forest and a gradient boosting machine?

1. Random forest is an ensemble of decision trees while gradient boosting is a single decision tree

2. Random forest combines decision trees using boosting while gradient boosting combines decision trees using bagging

3. Random forest uses bagging while gradient boosting uses boosting

4. Random forest is used for regression while gradient boosting is used for classification

✅ Correct Response: 3

Explanation: Random forest is an ensemble of decision trees that combines the results of multiple decision trees using bagging. Gradient boosting is also an ensemble of decision trees, but it combines the results of multiple decision trees using boosting.

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❓ Question 13:
What is the purpose of the iter() function when used with an iterable?

Option 1: It returns an iterator object that can be iterated over

Option 2: It checks if an object is iterable

Option 3: It converts an iterable into a generator

Option 4: It raises a StopIteration exception
 

✅ Correct Response: 1

Explanation: The purpose of the iter() function when used with an iterable is to return an iterator object that can be iterated over. In Python, the iter() function takes an iterable as an argument and returns an iterator object that can be used to traverse the elements of the iterable. The iterator object implements the iter() and next() methods required by the iterator protocol. This allows for sequential iteration over the elements of the iterable using the next() function or a for loop.

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❓ Question 14: #python
The find() method returns -1 if a substring is not found in a string.

Option 1: TRUE

Option 2: FALSE

Option 3: nan

Option 4: nan
 
✔️ Correct Response: 1

Explanation: The statement is true. The find() method in Python returns the index of the first occurrence of a substring in a string. If the substring is not found, it returns -1. This method can be used to check if a substring exists in a string and to find its position within the string.

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❓ Question 15: #python
What is the result of the expression "10 % 3" in Python?

Option 1: 1

Option 2: 3

Option 3: 0

Option 4: 3.333

✅ Correct Response: 1

Explanation: The expression "10 % 3" in Python results in 1. The % operator represents the modulus operator and returns the remainder of the division.

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❓ Question 16: #python
The expression "x in [1, 2, 3]" is True when:

Option 1: x is equal to 1 or 2 or 3

Option 2: x is equal to any value in the list

Option 3: x is not equal to any value in the list

Option 4: x is equal to the length of the list
 
✔️ Correct Response: 2

Explanation: The expression "x in [1, 2, 3]" is True when x is equal to any value in the list [1, 2, 3]. The in operator checks if the value exists in the sequence.

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❔Question 17: #Pandas
The ____ method in Pandas is used to replace specific values in a DataFrame.

Option 1: replace()

Option 2: exchange()

Option 3: transform()

Option 4: change()

✅ Correct Response: 1

Explanation: The replace() method in Pandas is used to replace specific values in a DataFrame.

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❔ Question 18: #pandas
To drop rows with a specific value in a DataFrame column, we can use the ____ method in Pandas.

Option 1: remove()

Option 2: delete()

Option 3: drop()

Option 4: dismiss()

✅ Correct Response: 3

Explanation: The drop() method in Pandas can be used to drop rows with a specific value in a DataFrame column.

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❔ Question 19: #scipy
Which of the following methods are commonly used for derivative-based optimization in SciPy?

Option 1: fmin_bfgs

Option 2: minimize with method='BFGS'

Option 3: minimize with method='Newton-CG'

Option 4: fmin

✅ Correct Response: 1, 2, 3

Explanation: Methods like fmin_bfgs and minimize with method='BFGS' or 'Newton-CG' are commonly used for derivative-based optimization in SciPy. fmin, on the other hand, implements the Nelder-Mead algorithm, which is a derivative-free method

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❓️ Question 20: #CPP
The length of an array in C++ is significant because it determines ____.

1. the number of elements that can be stored in the array

2. the memory allocated for the array

3. the maximum size of the array

4. All of the above

✅ Correct Response: 1

Explanation: The length of an array in C++ is significant because it determines the number of elements that can be stored in the array. It affects the memory allocation for the array and defines the maximum size of the array. Understanding the length of an array is important for proper memory management, accessing array elements, and avoiding potential issues such as accessing out-of-bounds indices.

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