Kalyan Penta kalyanp

##Google Interview Questions: Product Marketing Manager

Why do you want to join Google? -- Because I want to create tools for others to learn, for free. I didn't have a lot of money when growing up so I didn't get access to the same books, computers and resources that others had which caused money, I want to help ensure that others can learn on the same playing field regardless of their families wealth status or location.
What do you know about Google’s product and technology? -- A lot actually, I am a beta tester for numerous products, I use most of the Google tools such as: Search, Gmaill, Drive, Reader, Calendar, G+, YouTube, Web Master Tools, Keyword tools, Analytics etc.
If you are Product Manager for Google’s Adwords, how do you plan to market this?
What would you say during an AdWords or AdSense product seminar?
Who are Google’s competitors, and how does Google compete with them? -- Google competes on numerous fields: --- Search: Baidu, Bing, Duck Duck Go

	# -- coding: utf-8 --
	"""
	common-crawl-cdx.py

	A simple example program to analyze the Common Crawl index.

	This is implemented as a single stream job which accesses S3 via HTTP,
	so that it can be easily be run from any laptop, but it could easily be
	converted to an EMR job which processed the 300 index files in parallel.

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

	import play.api.libs.iteratee._
	import scala.concurrent.ExecutionContext.Implicits.global
	import scala.concurrent._
	import scala.concurrent.duration._
	import play.api.libs.concurrent.Promise

	object iteratees {

	// Implicit conversion to add 'await' to a Future
	implicit class WFuture[A](val inner: Future[A]) extends AnyVal {

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	#! /usr/bin/env python

	import redis
	import random
	import pylibmc
	import sys

	r = redis.Redis(host = 'localhost', port = 6389)
	mc = pylibmc.Client(['localhost:11222'])

	license: gpl-3.0
	redirect: https://observablehq.com/@d3/selection-join