Python Data Science Jobs & Interviews
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Your go-to hub for Python and Data Science—featuring questions, answers, quizzes, and interview tips to sharpen your skills and boost your career in the data-driven world.

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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 12:
What is an autoencoder?

1. A type of decision tree algorithm

2. A neural network architecture that is used for dimensionality reduction

3. A method for unsupervised learning

4. A type of clustering algorithm

Correct Response: 2

Explanation: An autoencoder is a neural network architecture that is used for dimensionality reduction, and is particularly useful for reducing the dimensionality of high-dimensional datasets.

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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.

Op
tion 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.

Op
tion 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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Question 21: #CPP
To access elements in a structure in C++, you use the structure variable name followed by ___.

1. A dot operator (.)

2. An arrow operator (->)

3. A hash sign (#)

4. A dollar sign ($)

Correct Response: 1

Explanation: In C++, to access elements in a structure, you use the structure variable name followed by a dot operator (.) and the name of the structure member. For example, if you have a structure 'Person' with a member 'name', you can access it using 'person.name', assuming 'person' is an instance of the 'Person' structure. The dot operator is used to access individual members of the structure.

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Question 22: #python
What does the 'map' function in Python do?

Option 1: Applies a function to each element of an iterable and returns a map object.
Option 2: Filters elements from an iterable based on a given function.
Option 3: Combines multiple iterables into a single iterable.
Option 4: Sorts the elements of an iterable in ascending order.

Correct Response: 1

Explanation: The 'map' function in Python applies a function to each element of an iterable and returns a map object containing the results. This is commonly used to apply the same operation to every item in a list or other iterable data structure.

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