Cracking the Coding Interview 6th Edition

CRACKING
Get prepared for your next dev interview!
1
THE CODING INTERVIEW

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CRACKING THE
CODING INTERVIEW
6th Edition
185 Programming Questions & Solutions
Gayle Laakmann McDowell
© 2015 by CareerCup
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Interviews are hard!
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Prepare with real interview questions
It's about developing a fresh algorithm,
not memorizing existing problems

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OUTLINE 1. The Interview Process
2. Before the Interview
3. The Offer and Beyond
4. Technical Questions
5. Interview Questions
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THE INTERVIEW PROCESS
1/5

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At most of the top tech companies,
algorithm and coding problems
form the largest component of the interview process.
•••
The Interview Process

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1. Analytical skills
2. Coding skills
3. Technical knowledge / Computer Science fundamentals
4. Experience
5. Culture fit / Communication skills
Assessment of your performance:
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•••

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•••
1. False negatives are acceptable
2. Problem-solving skills are valuable
3. Basic data structure and algorithm knowledge is useful
4. Whiteboards let you focus on what matters
Why do things this way?
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Frequently asked questions
1. If there's no grading system, how are you evaluated?
How does an interviewer know what to expect of you?
2. I didn't hear back immediately after my interview. Am I rejected?
3. Can I re-apply to a company after getting rejected?
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Companies Interview
••
Overview:
1. Screening interview, typically conducted over the phone.
2. On-site interview.
3. After your interview, your interviewers will provide feedback in some
form.
4. Most companies get back after about a week with next steps. (offer,
rejection, further interviews, or just an update on the process).
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Companies Interview
••
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BEFORE THE INTERVIEW
2/5

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Great experience Great CV +
LinkedIn
Interview
Before The Interview

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Getting the Right Experience
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For current students:
● Take the big project classes
● Get an internship
● Become lab/teaching assistant
● Start something
Two big things that companies want to see:
that you're smart and that you can code

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●
●
● ● ● ● ●
Preparation Map
3 - 12 Months
●
●
1+ Years
1+ Years
- Build project outside of school.
- Expand network.
- Find internship and take classes
with large projects.
3-12 Months
- Continue to work on projects.
- Create draft of CV.
- Make list of prefered companies.
- Practice interview questions.

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●
●
● ● ● ● ●
Preparation Map
3 - 12 Months
●
●
1+ Years
●
1 - 3 Months
4 Weeks
●
1-3 Months
- Do mini-projects.
- Do mock interviews.
- Continue to practice interview
questions.
4 Weeks
- Review/update CV.
- Begin applying to companies.
- Do another mock interview and
continue to practice questions,
writing code on paper.

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●
●
● ● ● ● ●
Preparation Map
Day before
●
●
1 Week
1 Week
- Phone interview.
- Do a final mock interview.
- Continue to practice interview
questions.
Day before
- Continue to practice questions &
review your list of mistakes.
3 - 12 Months
●
●
1+ Years
●
1 - 3 Months
4 Weeks
●

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● ● ● ● ●
Day before
●
●
1 Week
3 - 12 Months
●
●
1+ Years
●
1 - 3 Months
4 Weeks
● ●
Day of
After
●
Preparation Map
Day Of
- Eat a good breakfast & be on
time.
- Be confident (not cocky).
- Remember to talk out loud.
After
- Write thank you note to recruiter.
- If you haven’t heard from recruiter,
check in after one week.
- If no offer, ask why and when you
can re-apply.

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Your hard work paid off!
Get an offer?
Celebrate!

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THE OFFER AND BEYOND
3/5

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Offer Deadlines and Extensions
● Usually these deadlines are couple days to couple weeks
out.
● If you're still waiting to hear back from other companies,
you can ask for an extension.
● Companies will usually try to accommodate this, if
possible.
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● It's in your best interest to decline the offer on good terms
and keep a line of communication open.
● When you decline an offer, provide a reason that is
non-offensive and inarguable.
● But, be honest and be professional!
Declining an Offer
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Handling Rejection
● Getting rejected is unfortunate, but it doesn't mean that you're not a great
engineer.
● Lots of great engineers do poorly, either because they don't "test well" on
these sort of interviewers, or they just had an "off" day.
● Companies are often eager to re-interview previously rejected candidate.
● You can also ask for feedback from the recruiter.
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Evaluating the Offer
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On the Job
1. Set a Timeline
Five years later, are you still in there?
2. Build Strong Relationships
At work, establish strong relationships with your manager and teammates. When
employees leave, keep in touch with them.
3. Ask for What You Want
Be (reasonably) frank about your goals with your manager, like “want to take on
more back-end coding projects”
4. Keep Interviewing
a. Set a goal of interviewing at least once a year, even if you aren’t actively
looking for a new job.
b. If you get an offer, you don’t have to take it. It will still build a connection
with that company in case you want to join at a later date.
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TECHNICAL QUESTIONS
4/5

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Complexity (Big O)
Big O time is the language and metric we use
to describe the efficiency of algorithms
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Not understanding it thoroughly
can really hurt you in developing an algorithm

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Complexity (Analogy)
Transfer file to your friend across the country
● Email?
● Plane? Car? Walk?
Time complexity
● Electronic transfer: O(s)
● Airplane transfer: O(1)
Note: s - size of the file
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O(1)
O(s)

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Big O, Big Theta, and Big Omega
Prints all the values in an array
● O (big O): upper bound on the time. O(n), O(n2), O(n3), O(2n)
● Ω (big omega): lower bound. Ω(n), Ω(log n), Ω(1)
● Θ (big theta): means both O and Ω. That is, an algorithm is Θ(n) if it is both
O(n) and Ω(n)
Industry's meaning of big O is closer to what academics mean by Θ.
Best Case, Worst Case, Expected (Average) Case.
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Complexity

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Space Complexity
Complexity
int pairSumSequence(int n) {
int sum = 0;
for (int i = 0; i < n; ++i) {
sum += (i + i + 1);
}
return sum;
}
Time is not the only thing that matters in an algorithm.
We might also care about the amount of memory - or space - required
by an algorithm.
int sum(int n) {
if (n <= 0) {
return 0;
}
return n + sum(n - 1);
}
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Complexity
Big O allows us to express how the runtime scales.
● Drop the constants
O(2N) becomes O(N)
● Drop the Non-Dominant Terms
O(N2 + N) becomes O(N2).
O(N + log N) becomes O(N).
O(5 * 2N + 1000 * N100) becomes O(2N).
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Sample Question
Complexity
int mod(int a, int b) {
if (b <= 0) {
return -1;
}
int div = a / b;
return a - div * b;
}
The following code computes a % b. What is its runtime?
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Walking through a problem
Technical Questions
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Pay very close attention to any
information in the problem
description.
Listen
Debug your example. Is there any
way it’s a special case? Is it big
enough?
Draw an Example
Get a brute force solution as soon
as possible. Don’t code yet
though!
Brute Force
1. Conceptual test
2. Unusual or non-standard code
3. Hot spots, like null nodes
4. Small test case.
Measure time complexity
5. Special cases and edge cases
Test
Walk through you brute force
with BUD (Bottlenecks,
Unnecessary, Duplicated)
optimization.
Optimize
Your goal is to write beautiful code.
Modularize your code from the
beginning and refactor to clean up
anything that isn’t beautiful.
Keep talking!
Implement

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Optimize & Solve Technique
Technical Questions
Look for BUD
The most common things that an algorithm can waste time doing.
Walk through your brute force looking for these things.
When you find one of them, focus on getting rid of it.
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Bottlenecks
Technical Questions
Suppose you have a two-steps algorithm where you first sort the array
O(n log n) and then find elements with a particular property O(n).
Perhaps you could reduce O(n) to O(1), but would it matter?
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Unnecessary work
Technical Questions
Print all positive integer solutions to the equation
a3 + b3 = c3 + d3, (1 ≤ a, b, c, d ≤ 1000)
n = 1000
for a from 1 to n
for b from 1 to n
for c from 1 to n
for d from 1 to n
if a3 + b3 == c3 + d3
print a, b, c, d
O(N4)
n = 1000
for a from 1 to n
for b from 1 to n
for c from 1 to n
for d from 1 to n
if a3 + b3 == c3 + d3
print a, b, c, d
break
O(N4)
n = 1000
for a from 1 to n
for b from 1 to n
for c from 1 to n
d = pow(a3 + b3 - c3, 1/3)
if a3 + b3 == c3 + d3
print a, b, c, d
O(N3)
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a3 + b3 - c3 = d3

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Duplicated work
Technical Questions
The algorithm operates by essentially iterating through all (a, b) pairs
and searching all (c, d) pairs to ﬁnd if there are any matches to that (a, b) pair
n = 1000
for c from 1 to n
for d from 1 to n
result = c3 + d3
append (c, d) to list map[result]
for a from 1 to n
for b from 1 to n
result = a3 + b3
list = map.get(result)
for each pair in list
print a, b, pair
n = 1000
for c from 1 to n
for d from 1 to n
result = c3 + d3
append (c, d) to list at map[result]
for each result, list in map
for each pair1 in list
for each pair2 in list
print pair1, pair2
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Technical Questions
● Data structure brainstorm
○ Number are randomly generated and stored into an (expanding)
array. How would you keep track of the median?
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○ Probably not! Tend not to do very well with
accessing and sorting numbers
○ Maybe, but you already have an array.
Could you somehow keep the
elements sorted?
○ This is possible, but how?
○ A heap is really good at basic ordering
and keeping track of max and mins.

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Technical Questions
● Do It Yourself
● Simplify and generalize
● Base case and build
○ Often lead to natural recursive algorithms
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Handling Incorrect Answers
1. Responses to interview questions shouldn't be thought of as correct or
incorrect.
2. Your performance is evaluated in comparison to other candidates.
3. Many-possibly most-questions are too difficult to expect even a strong
candidate to immediately spit out the optimal algorithm.
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When You’ve Heard a Question Before
● If you've heard a question before, admit this to your interviewer.
● Your interviewer is asking you these questions in order to evaluate your
problem-solving skills.
● Additionally, your interviewer may find it highly dishonest if you don't
reveal that you know the question.
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What Good Coding Looks Like
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Simple
Readable Maintainable
Correct Efficient

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Don’t give up!
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INTERVIEW QUESTIONS
5/5

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1. Arrays and Strings
2. Linked Lists
3. Stacks and Queues
4. Tree and Graphs
5. Bit Manipulation
6. Math and Logic Puzzles
7. Object Oriented Design
8. Recursion and Dynamic Programming
9. System Design and Scalability
10. Sorting and Searching
11. Testing
12. C and C++ (Optional)
13. Java (Optional)
14. Databases
15. Threads and Locks
16. Moderate (Optional)
17. Hard (Optional)
What you need to know!

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● Hash Tables
○ Hash table is a data structure that maps keys to values for highly efﬁcient lookup
● ArrayList
○ Fixed length
● Resizable Arrays
○ Dynamically resizable
● StringBuilder
○ StringBuilder simply creates a resizable array of the strings
A good exercise to practice strings, arrays, and general data structures is to
implement your own version of StringBuilder, HashTable and
ArrayList.
1. Arrays and Strings (Overview)
•••

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1. Arrays and Strings (Example)
•••
53
Palindrome Permutation: Given a string,
write a function to check if it is a
permutation of a palindrome.
A permutation is a rearrangement of
letters. The palindrome does not need
to be limited to just dictionary words.
is a word or phrase that is the
same forwards and backwards
gigi: ggii, gigi, giig, igig, iggi, iigg

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1. Arrays and Strings (Solution)
•••
54
boolean isPermutationOfPalindrome(String phrase) {
int countOdd = 0;
int[] table = new int[Character.getNumericValue('z') -
Character.getNumericValue('a') + 1];
for (char c : phrase.toCharArray()) {
int x = getCharNumber(c);
if (x != -1) {
table[x]++;
if (table[x] % 2 == 1) {
countOdd++;
} else {
countOdd--;
}
}
}
return countOdd <= 1;
}
g 1 1
i 1 2
g 2 1
i 2 0
s 1 1
a 1 2
r 1 3
a 2 2
s 2 1

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2. Linked Lists (Overview)
••••••
55
Unlike an array, a linked list does not provide constant time access to a
particular index within the list. This means that if you'd like to find the K-th
element in the list, you will need to iterate through K elements.
The benefit of a linked list is that you can add and remove items from the
beginning of the list in constant time.

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2. Linked Lists (Overview)
••••••
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● Single Linked List
● Double Linked List
A good exercise to practice is to implement your own version Linked
List (get, insert, delete)

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2. Linked Lists (Example)
••••••
57
Remove Dups: Write code to remove duplicates from an unsorted linked list.
1 5 3
10
1 5 5
10 3 1 5

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2. Linked Lists (Solution)
••••••
58
void deleteDups(LinkedListNode n) {
HashSet set = new HashSet();
LinkedListNode previous = null;
while (n != null) {
if (set.contains(n.data)) {
previous.next = n.next;
} else {
set.add(n.data);
previous = n;
}
n = n.next;
}
}

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2. Linked Lists (Example)
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Follow Up
How would you solve this problem if a temporary buffer is not allowed?
••••••

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2. Linked Lists (Solution)
••••••
60
void deleteDups(LinkedListNode head) {
LinkedListNode current = head;
while (current != null) {
/* remove all future nodes that have the same value */
LinkedListNode runner = current;
while (runner.next != null) {
if (runner.next.data == current.data) {
runner.next = runner.next.next;
} else {
runner = runner.next;
}
}
current = current.next;
}
}

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Stack / Tumpukan: LIFO (Last in first out)
It uses the operations:
● pop()
● push(item)
● peek()
● isEmpty()
3. Stacks and Queues (Overview)
••••

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Queue / Antrian: FIFO (First in first out)
It uses the operations:
● add(item)
● remove()
● peek()
● isEmpty()
3. Stacks and Queues (Overview)
••••

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3. Stacks and Queues (Example)
63
Three in one: Describe how you could use a single array to implement three
stacks?
••••

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1 1 1 2 2 2 3 3 3
1 1 2 2 3 3 3
3 1 1 2 2 3 3 3
3 3 1 1 2 2 3 3 3
3. Stacks and Queues (Solution)
••••

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4. Trees and Graphs (Overview)
•••••
65
● Binary Tree
● Binary Search Tree
● Balanced & Unbalanced Tree
● Complete Binary Tree
● Full Binary Tree
● Perfect Binary Tree
● Binary Heaps
● Tries (Prefix Tree)
● Traversal
○ In-Order
○ Pre-order
○ Post-Order

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4. Trees and Graphs (Overview)
•••••
66
● Represent a Graph
○ Adjacency List
○ Adjacency Matrices
○ Edge List
● Graph search
○ Depth-First Search
○ Breadth-First Search
○ Bidirectional Search

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4. Trees and Graphs (Example)
•••••
67
Validate BST: Implement a function to check if a binary tree is a binary
search tree

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4. Trees and Graphs (Solution)
•••••
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10
5
3
0 15
Min: -∞
Max: +∞
Min: -∞
Max: 5
Min: 5
Max: +∞
Min: 5
Max: 10
Min: 10
Max: +∞

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4. Trees and Graphs (Solution)
•••••
69
boolean checkBST(TreeNode n) {
return checkBST(n, null, null);
}
boolean checkBST(TreeNode n, Integer min, Integer max) {
if (n == null) {
return true;
}
if ((min != null && n.data <= min) || (max != null && n.data > max)) {
return false;
}
if (!checkBST(n.left, min, n.data) || !checkBST(n.right, n.data, max)) {
return false;
}
return true;
}

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5. Bit Manipulation (Overview)
•••
70
Operators:
● & (AND)
● | (OR)
● ~ (NOT)
● ^ (XOR)
● >> (SHIFT RIGHT)
● << (SHIFT LEFT)
10 AND 6 = 1010 & 0110 = 2
1010
0110
---- &
0010 = 2

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5. Bit Manipulation (Example)
•••
71
Debugger: Explain what the following code does:
((n & (n - 1)) == 0)

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5. Bit Manipulation (Solution)
•••
72
((n & (n - 1)) == 0)
1 & 0 = 0
2 & 1 = 0
3 & 2 = 2
4 & 3 = 0
5 & 4 = 4
6 & 5 = 4
7 & 6 = 6
8 & 7 = 0
1000000000
0111111111

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6. Math and Logic Puzzles (Overview)
•••
73
● Logical observation
● GCD, LCM
● Prime Numbers
○ Sieve Eratosthenes
● Probability
○ and, P(A and B) = P(B | A) P(A)
○ or, P(A or B) = P(A) + P(B) - P(A and B)
○ independence, P(A and B) = P(A) P(B)
○ mutual exclusivity, P(A or B) = P(A) + P(B)

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6. Math and Logic Puzzles (Example)
•••
74
Dominos: There is an 8x8 chessboard in which two diagonally opposite
corners have been cut off. You are given 31 dominos, and a single domino can
cover exactly two squares. Can you use the 31 dominos to cover the entire
board?
Prove your answer.

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6. Math and Logic Puzzles (Solution)
•••
75
30
32
=
=

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7. Object-Oriented Design (Overview)
••••
76
Object oriented design questions require a candidate to sketch out the classes
and methods to implement technical problems or real-life objects.
You have to create elegant, maintainable object-oriented code.
How to approach:
1. Handle ambiguity (Ask questions to clarify)
2. Define the core object like Table, Employee, Host, etc
3. Analyze relationships
4. Investigate actions

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7. Object-Oriented Design (Overview)
••••
77
● Design Patterns
○ Singleton class
○ Factory method
○ Abstract factory
● Concepts
○ Object/Class
○ Inheritance
○ Interface
○ Polymorphism
● Outputs
○ Sequence diagram
○ Class diagram

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Parking Lot: Design a parking lot using object-oriented principles.
7. Object-Oriented Design (Example)
••••

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● Has multiple levels. Each level has multiple rows
of spots.
● motorcycles, cars, and buses.
● motorcycle spots, compact spots, and large spots.
● A motorcycle can park in any spot.
● A car can park in either a single compact spot or a
single large spot.
● A bus can park in five large spots that are
consecutive and within the same row. It cannot
park in small spots.
7. Object-Oriented Design (Solution)
••••

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8. Recursion and Dynamic Programming (Overview)
•••••
80
● How to approach:
○ Bottom-Up
■ Solve simple case, like a list with only one element, then solve
the problem with two elements, three, and so on.
○ Top-Down
■ Divide the problem for case N into subproblems.
○ Half-and-Half
■ Divide data set in half, and merge together.
● Recursive vs Iterative
● Memoization
○ Example problem: Fibonacci numbers

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8. Recursion and Dynamic Programming (Example)
•••••
81
Triple Step: A child is running up a staircase with n steps and can hop either 1
step, 2 steps, or 3 steps at a time. Implement a method to count how many
possible ways the child can run up the stairs.

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8. Recursion and Dynamic Programming (Solution)
•••••
82
82
Illustration
n
n-1
n-2
n-3
Step Combinations
1 1
2 11, 2
3 111, 12, 21, 3
4 1111
121, 211, 112
31, 22, 13

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8. Recursion and Dynamic Programming (Solution)
•••••
83
int countWays(int n) {
if (n < 0) return 0;
if (n == 0) return 1;
return countWays(n-1) + countWays(n-2) + countWays(n-3);
}
Works, but not efficient

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8. Recursion and Dynamic Programming (Overview)
•••••
84
int countWays(int n) {
int[] memo = new int[n + 1];
Arrays.fill(memo, -1);
return countWays(n, memo);
}
int countWays(int n, int[] memo) {
if (n < 0) return 0;
if (n == 0) return 1;
if (memo[n] > -1) return memo[n];
memo[n] = countWays(n-1, memo) +
countWays(n-2, memo) +
countWays(n-3, memo);
return memo[n];
}

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85
● Horizontal vs Vertical Scaling
Vertical: Increasing resources of a specific node (add additional memory)
Horizontal: Increasing the number of nodes (add additional server)
● Load Balancer
● Database Denormalization and NoSQL
Denormalization: Adding redundant information
NoSQL: Not support joins
● Database Partitioning (Sharding) - Vertical, Key-Based, Directory-Based
● Caching
● Asynchronous Processing & Queues
● Networking Metrics (Bandwidth, Throughput, Latency)
● MapReduce
9. System Design and Scalability (Overview)
•••

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86
Web Crawler: If you were designing a web crawler, how would you avoid
getting into infinite loops?
9. System Design and Scalability (Example)
•••

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87
www.careercup.com/page?pid=microsoft-interview -questions vs
www.careercup.com/page?pid=google-interview -questions
www.careercup.com?foobar=hello vs www.careercup.com
Estimation for degree of similarity
9. System Design and Scalability (Solution)
•••

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10. Sorting and Searching (Overview)
••••
88
● Sorting
○ Bubble Sort
■ Runtime: O(N2), memory: O(1)
○ Selection Sort
■ Runtime: O(N2), memory: O(1)
○ Merge Sort
■ Runtime: O(N log N), memory: Depends
○ Quick Sort
■ Runtime: O(N log N), memory: O(log N)
○ Radix Sort
■ Runtime O(KxN)

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10. Sorting and Searching (Overview)
••••
89
● Searching
○ Linear Search
■ Runtime: O(N)
○ Binary search
■ Precondition : sorted array
■ Runtime: O(N log N)

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10. Sorting and Searching (Example)
••••
90
Sorted Merge:
You are given two sorted arrays, A and B, where A has a large enough buffer at
the end to hold B. Write a method to merge B into A in sorted order.
1 2 3 4 5 6
2 3 4 6

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10. Sorting and Searching (Solution)
••••
91
void merge(int[] a, int[] b, int lastA, int lastB) {
int indexA = lastA - 1; // Index of last element in array a
int indexB = lastB - 1; // Index of last element in array b
int indexMerged = lastB + lastA - 1; // end of merged array
while (indexB >= 0) {
if (indexA >= 0 && a[indexA] > b[indexB]) {
a[indexMerged] = a[indexA]; // copy element
indexA--;
} else {
a[indexMerged] = b[indexB]; // copy element
indexB--;
}
indexMerged--; // move indices
}
}

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● Big picture understanding
○ Are you a person who understands what the software is really about?
● Knowing how the pieces fit together
○ Do you understand how software works, and how it might fit into a greater ecosystem?
● Organization
○ Do you approach the problem in a structured manner, or do you just spout off anything
that comes to your head?
● Practicality
○ Can you actually create reasonable testing plans?
11. Testing (Overview)
•••

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11. Testing (Example)
•••
93
Mistake: Find the mistake(s) in the following code:
unsigned int i;
for (i = 100; i >= 0; --i)
printf("%d\n", i);

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11. Testing (Solution)
•••
94
unsigned int i;
for (i = 100; i > 0; --i) {
printf("%u\n", i);
}
printf("0\n");

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14. Databases (Overview)
•••
95
● SQL Syntax and Variations
○ Explicit Join vs Implicit Join
● Denormalized vs Normalized Databases
● SQL Statements
○ Join vs Inner Join vs Outer Join
○ Group By
● Small Database Design
○ Handle ambiguity
○ Define the core objects
○ Analyze relationships
○ Investigate actions
● Large Database Design
○ Denormalize

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14. Databases (Example)
•••
96
Multiple Apartments: Write a SQL query to get a list of tenants who are
renting more than one apartment.
Apartments
AptID int
UnitNumber varchar(10)
BuildingID int
AptTenants
TenantID int
AptID int
Tenants
TenantID int
TenantName varchar(100)

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14. Databases (Solution)
•••
97
SELECT TenantName
FROM Tenants
INNER JOIN
(SELECT TenantID FROM AptTenants GROUP BY TenantID HAVING count(*) > 1) C
ON Tenants.TenantID = C.TenantID

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98
It is, however, relatively common for interviewers at any company to assess
your general understanding of threads, particularly your understanding of
deadlocks.
● Thread vs process
● Synchronization methods
● Locks
● Deadlocks and Deadlock Prevention
15. Threads and Locks (Overview)
•••

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15. Threads and Locks (Example)
•••
99
Thread vs. Process: What's the difference between a thread and a process?

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15. Threads and Locks (Solution)
•••
100
A process can be thought of as an instance of a program in execution. A process is
an independent entity to which system resources (e.g., CPU time and memory) are
allocated.
A thread exists within a process and shares the process resources (including its heap
space) .
A thread is a particular execution path of a process. When one thread modifies a
process resource, the change is immediately visible to sibling threads.

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101
Factorial Zeros: Write an algorithm which computes the number of trailing
zeros in N factorial (N!).
3! = 6 → 0 trailing zero
6! = 720 → 1 trailing zero
12! = 479001600 → 2 trailing zeros
16. Moderate (Example)
••

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102
To count the number of zeros, we only need to count the pairs of multiples of
5 and 2. There will always be more multiples of 2 than 5, though, so simply
counting the number of multiples of 5 is sufficient.
16. Moderate (Solution)
••

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103
Add Without Plus: Write a function that adds two numbers.
You must not use + or any arithmetic operators.
17. Hard (Example)
••

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104
For example to add 759 and 674:
1. Add 759 + 674, but "forget" to carry. Get 323.
2. Add 759 + 674 but only do the carrying, rather than the addition of each
digit. Get 1110.
3. Add the result of the first two operations (recursively, using the same
process described in step 1 and 2): 323 + 1110 = 1433
Step 1 can be done using bitwise xor operator.
Step 2 can be done using bitwise and operator.
Recurse until there's nothing to carry.
17. Hard (Solution)
••

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105
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106
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107
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Cracking the Coding Interview 6th Edition

Cracking the Coding Interview 6th Edition

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