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10+ Eversendai Interview Questions and Answers

Updated 19 Oct 2024

Q1. How do you think aircraft engine works ?

Ans.

Aircraft engines work by sucking in air, compressing it, adding fuel, igniting it, and expelling the exhaust gases to create thrust.

  • Air is sucked in through the front of the engine by the fan blades

  • The compressor squeezes the air to increase its pressure

  • Fuel is added and ignited in the combustion chamber

  • The hot exhaust gases are expelled out the back of the engine, creating thrust

  • Different types of engines include turbojet, turboprop, and turbofan

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Q2. What sort of quantitative experience you have

Ans.

I have experience in conducting statistical analysis, data modeling, and forecasting.

  • Proficient in statistical software such as R, Python, and SPSS

  • Experience in analyzing large datasets and identifying trends

  • Skilled in creating data visualizations to communicate findings

  • Knowledge of regression analysis, hypothesis testing, and predictive modeling

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Q3. How does IC engines work ?

Ans.

IC engines work by converting fuel into mechanical energy through combustion within the engine.

  • Fuel is mixed with air and ignited in the combustion chamber

  • The resulting explosion pushes a piston, which turns a crankshaft

  • The crankshaft converts the linear motion of the piston into rotational motion

  • The rotational motion is used to power the vehicle or machinery

  • Examples include gasoline engines, diesel engines, and natural gas engines

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Q4. Java performance management techniques

Ans.

Java performance management techniques involve optimizing code, memory usage, and resource allocation.

  • Use efficient data structures and algorithms to improve performance.

  • Optimize code by reducing unnecessary loops, avoiding excessive object creation, and minimizing memory usage.

  • Utilize profiling tools like JVisualVM or YourKit to identify performance bottlenecks.

  • Implement caching mechanisms to reduce redundant computations and database queries.

  • Consider using multithreading an...read more

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Q5. Difference Between List and Touple in python

Ans.

List is mutable, ordered collection of items while tuple is immutable, ordered collection of items in Python.

  • List is defined using square brackets [] while tuple is defined using parentheses ().

  • Elements in a list can be changed or modified while elements in a tuple cannot be changed.

  • Lists are typically used for collections of similar items while tuples are used for fixed collections of items.

  • Example: list_example = [1, 2, 3] and tuple_example = (4, 5, 6)

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Q6. Tuple is immutable, while list is mutable.

Ans.

Tuple is immutable, list is mutable in Python.

  • Tuple elements cannot be changed once assigned, while list elements can be modified.

  • Tuple uses parentheses () and list uses square brackets [] for declaration.

  • Example: tuple_example = (1, 2, 3) vs list_example = [1, 2, 3]

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Q7. how back propagation in NN work

Ans.

Back propagation is a method used to train neural networks by adjusting the weights based on the error calculated during the forward pass.

  • Back propagation involves calculating the error between the predicted output and the actual output.

  • The error is then propagated backwards through the network to adjust the weights using gradient descent.

  • This process is repeated iteratively until the network's performance improves.

  • Example: If a neural network is trained to recognize handwrit...read more

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Q8. How custom Middleware is created with code example?

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Q9. How regularisation works in random forest

Ans.

Regularisation in random forest helps prevent overfitting by controlling the complexity of the model.

  • Regularisation in random forest is achieved by limiting the depth of the trees in the forest.

  • It helps prevent overfitting by reducing the complexity of the model and improving generalization.

  • Regularisation parameters like max_depth, min_samples_split, and min_samples_leaf can be tuned to control the complexity of the model.

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Q10. how LLM use neural network

Ans.

LLM can use neural networks for tasks such as natural language processing, image recognition, and predictive analytics.

  • Neural networks can be used in LLM for natural language processing tasks such as sentiment analysis, text generation, and language translation.

  • LLM can utilize neural networks for image recognition tasks like object detection, facial recognition, and image classification.

  • Neural networks can also be applied in LLM for predictive analytics tasks such as forecast...read more

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