Fullstack Developer

The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects. This means that lists can be modified, appended or sliced on the go but tuples remain constant and cannot be modified in any manner.

"__init__" is a contructor method in Python and is automatically called to allocate memory when a new object/instance is created. All classes have a "__init__" method associated with them. It helps in distinguishing methods and attributes of a class from local variables.

Lambda is an anonymous function in Python, that can accept any number of arguments, but can only have a single expression. It is generally used in situations requiring an anonymous function for a short time period. Lambda functions can be used in either of the two ways:

Assigning lambda functions to a variable :

mul = lambda a, b : a * b
print(mul(4, 6)) # output => 24

Wrapping lambda functions inside another function :

def myWrapper(n):
return lambda a : a * n
mulFive = myWrapper(4)
print(mulFive(6)) # output => 24

1) .py files contain the source code of a program. Whereas, .pyc file contains the bytecode of your program. We get bytecode after compilation of .py file (source code). .pyc files are not created for all the files that you run. It is only created for the files that you import.

2) Before executing a python program python interpreter checks for the compiled files. If the file is present, the virtual machine executes it. If not found, it checks for .py file. If found, compiles it to .pyc file and then python virtual machine executes it.

3) Having .pyc file saves you the compilation time.

1) Python as a language is not interpreted or compiled. Interpreted or compiled is the property of the implementation. Python is a bytecode(set of interpreter readable instructions) interpreted generally.

2) Source code is a file with .py extension.

3) Python compiles the source code to a set of instructions for a virtual machine. The Python interpreter is an implementation of that virtual machine. This intermediate format is called “bytecode”.

4) .py source code is first compiled to give .pyc which is bytecode. This bytecode can be then interpreted by the official CPython or JIT(Just in Time compiler) compiled by PyPy.

1) The list data structure of python is very highly efficient and is capable of performing various functions. But, they have severe limitations when it comes to the computation of vectorized operations which deals with element-wise multiplication and addition. 

2) The python lists also require the information regarding the type of every element which results in overhead as type dispatching code gets executes every time any operation is performed on any element. This is where the NumPy arrays come into the picture as all the limitations of python lists are handled in NumPy arrays.

3) Additionally, as the size of the NumPy arrays increases, NumPy becomes around 30x times faster than the Python List. This is because the Numpy arrays are densely packed in the memory due to their homogenous nature. This ensures the memory free up is also faster.

GIL stands for Global Interpreter Lock. This is a mutex used for limiting access to python objects and aids in effective thread synchronization by avoiding deadlocks. GIL helps in achieving multitasking (and not parallel computing).

There are three threads in GIL representation. First Thread acquires the GIL first and starts the I/O execution. When the I/O operations are done, thread 1 releases the acquired GIL which is then taken up by the second thread. The process repeats and the GIL are used by different threads alternatively until the threads have completed their execution. The threads not having the GIL lock goes into the waiting state and resumes execution only when it acquires the lock.

Django follows the MVT (Model View Template) pattern which is based on the Model View Controller architecture. It’s slightly different from the MVC pattern as it maintains its own conventions, so, the controller is handled by the framework itself. The template is a presentation layer. It is an HTML file mixed with Django Template Language (DTL). The developer provides the model, the view, and the template then maps it to a URL, and finally, Django serves it to the user.

This ORM (an acronym for Object Relational Mapper) enables us to interact with databases in a more pythonic way like we can avoid writing raw queries, it is possible to retrieve, save, delete and perform other operations over the database without ever writing any SQL query. It works as an abstraction layer between the models and the database.

1) Abstract Base Class Inheritance: Used when you only need the parent class to hold information that you don’t want to write for each child model.

2) Multi-Table Model Inheritance: Used when you are subclassing an existing model and need each model to have its own table in the database.

3) Proxy Model Inheritance: Used when you want to retain the model's field while altering the python level functioning of the model.

1) Memory management in Python is handled by the Python Memory Manager. 

2) The memory allocated by the manager is in form of a private heap space dedicated to Python. 

3) All Python objects are stored in this heap and being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space.

4) Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.

Pickling:

Pickling is the name of the serialization process in Python. Any object in Python can be serialized into a byte stream and dumped as a file in the memory. The process of pickling is compact but pickle objects can be compressed further. Moreover, pickle keeps track of the objects it has serialized and the serialization is portable across versions.
The function used for the above process is pickle.dump().



Unpickling:

Unpickling is the complete inverse of pickling. It deserializes the byte stream to recreate the objects stored in the file and loads the object to memory.
The function used for the above process is pickle.load().

Decorator is any function that takes in another function and adds functionality to it without manipulating the function itself. It does this by using a wrapper function.


def decorator(func):
def wrapper(*args):
print("Logging the parameter of ", func, " is ", args)
return func
return wrapper

@decorator
def operation(x, y):
return x+y

operation(5,20)


@decorator is just a syntactic sugar, it is equivalent to
res = decorator(operation(5,20))

A collection in MongoDB is a group of documents. If a document is the MongoDB analog of a row in a relational database, then a collection can be thought of as the analog to a table.
Documents within a single collection can have any number of different “shapes.”, i.e. collections have dynamic schemas. 
For example, both of the following documents could be stored in a single collection:

{"greeting" : "Hello Ninjas!", "views": 5}
{"signoff": "Have a nice day"}

1) Sharding is the process of splitting data up across machines. 
2) We also use the term “partitioning” sometimes to describe this concept. 
3) We can store more data and handle more load without requiring larger or more powerful machines, by putting a subset of data on each machine.
4) MongoDB’s sharding allows you to create a cluster of many machines (shards) and break up a collection across them, putting a subset of data on each shard. 
5) This allows your application to grow beyond the resource limits of a standalone server or replica set.

Tip 1 : The cross questioning can go intense some time, think before you speak.
Tip 2 : Be open minded and answer whatever you are thinking, in these rounds I feel it is important to have opinion.
Tip 3 : Context of questions can be switched, pay attention to the details. It is okay to ask questions in these round, like what are the projects currently the company is investing, which team you are mentoring. How all is the work environment etc.
Tip 4 : Since everybody in the interview panel is from tech background, here too you can expect some technical questions. No coding in most of the cases but some discussions over the design can surely happen.

Tip : For an experienced professional seeking a change, this is a common question. The easiest method to respond to this question is to state that you are leaving your current work in order to advance your career. Make sure you don't criticize or speak poorly about the company where you now work.