"Tell me about a time you were with someone on your team who was struggling to meet objectives.
How did you address the situation?
What kind of feedback did you give the individual?
What was the outcome?"
Jawahir Y. - "Tell me about a time you were with someone on your team who was struggling to meet objectives.
How did you address the situation?
What kind of feedback did you give the individual?
What was the outcome?"See full answer
"I like chatgpt for the following users
Getting industry references are easy and time saving
Getting recommendations is very easy
Responses are accurate
I use chatgpt to get feedback on content and identify gaps in my documentation or thought process
I use chatgpt as a search engine and to get conscise situation based information.
Chatgpt offers varierty of other tools in the explore version where similar users can create different content
PRD template
Chatgpt users
1."
Shraddha D. - "I like chatgpt for the following users
Getting industry references are easy and time saving
Getting recommendations is very easy
Responses are accurate
I use chatgpt to get feedback on content and identify gaps in my documentation or thought process
I use chatgpt as a search engine and to get conscise situation based information.
Chatgpt offers varierty of other tools in the explore version where similar users can create different content
PRD template
Chatgpt users
1."See full answer
Product Manager
Product Design
+3 more
🧠Want an expert answer to a question? Saving questions lets us know what content to make next.
"DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."
Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer
"Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"
Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer
"We can use dictionary to store cache items so that our read / write operations will be O(1).
Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1)
Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"
Alfred O. - "We can use dictionary to store cache items so that our read / write operations will be O(1).
Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1)
Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"See full answer
"Let me try to explain it with simple life analogy
You're cooking dinner in the kitchen. Multithreading is when you've got a bunch of friends helping out. Each friend does a different job—like one chops veggies while another stirs a sauce. Everyone focuses on their task, and together, you all make the meal faster.
In a computer, it's like different jobs happening all at once, making stuff happen quicker, just like having lots of friends helping makes dinner ready faster."
Praveen D. - "Let me try to explain it with simple life analogy
You're cooking dinner in the kitchen. Multithreading is when you've got a bunch of friends helping out. Each friend does a different job—like one chops veggies while another stirs a sauce. Everyone focuses on their task, and together, you all make the meal faster.
In a computer, it's like different jobs happening all at once, making stuff happen quicker, just like having lots of friends helping makes dinner ready faster."See full answer
"Reversing a linked list is a very popular question. We have two approaches to reverse the linked list: Iterative approach and recursion approach.
Iterative approach (JavaScript)
function reverseLL(head){
if(head === null) return head;
let prv = null;
let next = null;
let cur = head;
while(cur){
next = cur.next; //backup
cur.next = prv;
prv = cur;
cur = next;
}
head = prv;
return head;
}
Recursion Approach (JS)
function reverseLLByRecursion("
Satyam S. - "Reversing a linked list is a very popular question. We have two approaches to reverse the linked list: Iterative approach and recursion approach.
Iterative approach (JavaScript)
function reverseLL(head){
if(head === null) return head;
let prv = null;
let next = null;
let cur = head;
while(cur){
next = cur.next; //backup
cur.next = prv;
prv = cur;
cur = next;
}
head = prv;
return head;
}
Recursion Approach (JS)
function reverseLLByRecursion("See full answer
"Clarifying my assumptions first, i.e. "Technical contributions are not just writing or reviewing code".
Answer :
My contributions to any program I lead as a TPM are as follows :
Inputs towards design, architecture review, mapping requirements to the proposed design, reviewing the implementation strategy w.r.t scalable solution, Writing core user-centric test scenarios, Validating proposed design vs implementation estimates vs initial planning. etc...
Example :
Situation: We are a"
DM - "Clarifying my assumptions first, i.e. "Technical contributions are not just writing or reviewing code".
Answer :
My contributions to any program I lead as a TPM are as follows :
Inputs towards design, architecture review, mapping requirements to the proposed design, reviewing the implementation strategy w.r.t scalable solution, Writing core user-centric test scenarios, Validating proposed design vs implementation estimates vs initial planning. etc...
Example :
Situation: We are a"See full answer
"
Project Overview: Real-Time Risk Management System
Objective
The goal was to develop a real-time risk management system capable of processing and analyzing large volumes of trading data to provide near-instantaneous risk assessments. This system was crucial for enabling traders to make informed decisions while managing their exposure to various market risks in real-time.
Complexity Factors
1. \\Data Volume and Velocity\\
\\High Throughput:\\ The system needed to ha"
Scott S. - "
Project Overview: Real-Time Risk Management System
Objective
The goal was to develop a real-time risk management system capable of processing and analyzing large volumes of trading data to provide near-instantaneous risk assessments. This system was crucial for enabling traders to make informed decisions while managing their exposure to various market risks in real-time.
Complexity Factors
1. \\Data Volume and Velocity\\
\\High Throughput:\\ The system needed to ha"See full answer
"
from typing import List
def getnumberof_islands(binaryMatrix: List[List[int]]) -> int:
if not binaryMatrix: return 0
rows = len(binaryMatrix)
cols = len(binaryMatrix[0])
islands = 0
for r in range(rows):
for c in range(cols):
if binaryMatrixr == 1:
islands += 1
dfs(binaryMatrix, r, c)
return islands
def dfs(grid, r, c):
if (
r = len(grid)
"
Rick E. - "
from typing import List
def getnumberof_islands(binaryMatrix: List[List[int]]) -> int:
if not binaryMatrix: return 0
rows = len(binaryMatrix)
cols = len(binaryMatrix[0])
islands = 0
for r in range(rows):
for c in range(cols):
if binaryMatrixr == 1:
islands += 1
dfs(binaryMatrix, r, c)
return islands
def dfs(grid, r, c):
if (
r = len(grid)
"See full answer
"Maybe we can use this solution:
1, connect all the strings together, and add an integer value ahead each string.
2, use Huffmans algorithm to encode the step 1 result, to make the result size smaller.
3, return the root of Huffmans tree.
This solution man be slower than the common serialize method, but it can save a lot of memory, I think, at lease doing serialize is mainly for tranfering data or storing data."
Jordan Z. - "Maybe we can use this solution:
1, connect all the strings together, and add an integer value ahead each string.
2, use Huffmans algorithm to encode the step 1 result, to make the result size smaller.
3, return the root of Huffmans tree.
This solution man be slower than the common serialize method, but it can save a lot of memory, I think, at lease doing serialize is mainly for tranfering data or storing data."See full answer