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10+ Eversendai Interview Questions and Answers
Q1. How do you think aircraft engine works ?
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
Q2. What sort of quantitative experience you have
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
Q3. How does IC engines work ?
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
Q4. Java performance management techniques
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
Q5. Difference Between List and Touple in python
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)
Q6. Tuple is immutable, while list is mutable.
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]
Q7. how back propagation in NN work
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
Q8. How custom Middleware is created with code example?
Q9. How regularisation works in random forest
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.
Q10. how LLM use neural network
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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