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Alex 👨🏼‍💻FullStack.Cafe — Sun, 22 Nov 2020 06:41:41 +0000

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New System Design and Architecture Interview Questions section - CAP Theorem, DDD, Caching, Concurrency, NoSQL and etc.
Improved, updated and refined Full-Stack Interview Questions section - including React, Angular, Vue, JS , .NET + 36 other topics
Plus codebase has been completely rewritten from Angular to React (-56% bundle size, 98/100 Google Page speed score) so that's kinda cool

Why Bother?

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12 Common Dynamic Programming Interview Questions and Problems (SOLVED)

Alex 👨🏼‍💻FullStack.Cafe — Fri, 23 Oct 2020 07:53:15 +0000

Dynamic programming is all about ordering your computations in a way that avoids recalculating duplicate work. More specifically, Dynamic Programming is a technique used to avoid computing multiple times the same subproblem in a recursive algorithm. DP algorithms could be implemented with recursion, but they don't have to be. Follow along and learn 12 Most Common Dynamic Programming Interview Questions and Answers to nail your next coding interview.

🔴 Originally published on FullStack.Cafe - Kill Your Next Tech Interview

Q1: What is Dynamic Programming? ☆

Topics: Dynamic Programming

Answer:

Dynamic programming is all about ordering your computations in a way that avoids recalculating duplicate work. More specifically, Dynamic Programming is a technique used to avoid computing multiple times the same subproblem in a recursive algorithm. DP algorithms could be implemented with recursion, but they don't have to be.

With dynamic programming, you store your results in some sort of table generally. When you need the answer to a problem, you reference the table and see if you already know what it is. If not, you use the data in your table to give yourself a stepping stone towards the answer.

There are two approaches to apply Dynamic Programming:

The top-down or memoization. When the recursion does a lot of unecessary calculation, an easy way to solve this is to cache the results and to check before executing the call if the result is already in the cache.
The bottom-up or tabulation approach. A better way to do this is to get rid of the recursion all-together by evaluating the results in the right order and building the array as we iterate. The partial results are available when needed if the iteration is done in the right order.

TOP of the tree
fib(4)
 fib(3)...................... + fib(2)
  fib(2)......... + fib(1)       fib(1)........... + fib(0)
   fib(1) + fib(0)   fib(1)       fib(1)              fib(0)
    fib(1)   fib(0)
BOTTOM of the tree

Greedy	Divide & Conquer	Dynamic Programming
Optimises by making the best choice at the moment	Optimises by breaking down a subproblem into simpler versions of itself and using multi-threading & recursion to solve	Same as Divide and Conquer, but optimises by caching the answers to each subproblem as not to repeat the calculation twice.
Doesn't always find the optimal solution, but is very fast	Always finds the optimal solution, but is slower than Greedy	Always finds the optimal solution, but could be pointless on small datasets.
Requires almost no memory	Requires some memory to remember recursive calls	Requires a lot of memory for memoisation / tabulation

Scenario	Main Focus	Description
No partition	CA	The system is available
		and provides strong consistency
---------------------------	------------	------------------------------------
partition,	AP	Not synchronized writes
majority connected		from the old primary are ignored
---------------------------	------------	------------------------------------
partition,	CP	only read access is provided
majority not connected		to avoid separated and inconsistent systems


lateinit var	by lazy
Can be initialized from anywhere the object seen from.	Can only be initialized from the initializer lambda.
Multiple initialization possible.	Only initialize single time.
Non-thread safe. It’s up to user to initialize correctly in a multi-threaded environment.	Thread-safety by default and guarntees that the initializer is invoked by once.
Can only be used for var.	Can only be used for val.
Not eligible for nonnull properties.	Not eligible for nonnull properties.
An isInitialized method added to check whether the value has been initialized before.	Property never able to un-initialized.
Not allowed on properties of primitive types.	Allowed on properties of primitive types.

Forem: Alex 👨🏼‍💻FullStack.Cafe

🔴👨‍💻 FullStack.Café - 🪓 Kill Your Tech & Coding Interview + BLKFRD 50% OFF DEAL

12 Common Dynamic Programming Interview Questions and Problems (SOLVED)

Q1: What is Dynamic Programming? ☆

Answer:

Q2: How Dynamic Programming is different from Recursion and Memoization? ☆☆

Answer:

Q3: What are pros and cons of Memoization or Top-Down approach? ☆☆

Answer:

Q4: What are some characteristics of Dynamic Programming? ☆☆

Answer:

Q5: LIS: Find length of the longest increasing subsequence (LIS) in the array. Solve using DP. ☆☆☆

Problem:

Solution:

Complexity Analysis:

Java

PY

Q6: Provide an example of Dynamic Program but without Recursion ☆☆☆

Answer:

Q7: What are some pros and cons of Tabulation or Bottom-Up approach? ☆☆☆

Answer:

Q8: What is the difference between Divide and Conquer and Dynamic Programming Algorithms? ☆☆☆

Answer:

Q9: What should you consider when choosing between Top-Down vs Bottom-Up solutions for the same problem? ☆☆☆

Answer:

Q10: Compare Greedy vs Divide & Conquer vs Dynamic Programming Algorithms ☆☆☆☆

Answer:

Q11: How to use Memoization for N-th Fibonacci number? ☆☆☆☆

Problem:

Solution:

Complexity Analysis:

Implementation:

JS

Java

Q12: Is Dijkstra's algorithm a Greedy or Dynamic Programming algorithm? ☆☆☆☆

Answer:

25 NoSQL Interview Questions (ANSWERED) You Must Know In 2020

Q1: What are NoSQL databases? What are the different types of NoSQL databases? ☆

Answer:

Q2: What do you understand by NoSQL databases? Explain. ☆

Answer:

Q3: Explain difference between scaling horizontally and vertically for databases ☆☆

Answer:

Q4: What are the advantages of NoSQL over traditional RDBMS? ☆☆

Answer:

Q5: When should we embed one document within another in MongoDB? ☆☆

Answer:

Q6: Define ACID Properties ☆☆☆

Answer:

Q7: Does MongoDB support ACID transaction management and locking functionalities? ☆☆☆

Answer:

Q8: Explain advantages of BSON over JSON in MongoDB? ☆☆☆

Answer:

Q9: How can you achieve primary key - foreign key relationships in MongoDB? ☆☆☆

Answer:

Q10: How do I perform the SQL JOIN equivalent in MongoDB? ☆☆☆

Answer:

Q11: How does column-oriented NoSQL differ from document-oriented? ☆☆☆

Answer:

Q12: What does Document-oriented vs. Key-Value mean in context of NoSQL? ☆☆☆

Answer:

Q13: What is Denormalization? ☆☆☆

Answer:

Q14: What is Sharding in MongoDB? ☆☆☆

Answer:

Q15: When should I use a NoSQL database instead of a relational database? ☆☆☆

Answer:

Q16: When would you use NoSQL? ☆☆☆

Answer:

Q17: Explain BASE terminology in a context of NoSQL ☆☆☆☆

Answer:

Q18: Explain eventual consistency in context of NoSQL ☆☆☆☆

Answer:

Q19: Explain how would you keep document change history in NoSQL DB? ☆☆☆☆

Answer:

Q20: Explain use of transactions in NoSQL ☆☆☆☆

Answer:

Q21: How do you track record relations in NoSQL? ☆☆☆☆

Answer:

Q22: How does MongoDB ensure high availability? ☆☆☆☆

Q10: What is complexity of `push` and `pop` for a Stack implemented using a LinkedList? ☆☆

Q12: Split the Linked List into `k` consecutive linked list "parts" ☆☆☆

Q18: How to find `Nth` element from the end of a singly Linked List? ☆☆☆