Technical Interview Cheat Sheet

My technical interview cheat sheet, modified from this reference: https://gist.github.com/TSiege/cbb0507082bb18ff7e4b

• Javascript Technical Interview Cheat Sheet
• Technical Interview Cheat Sheet Printable
• Technical Interview Cheat Sheet 2020

Studying for a Tech Interview Sucks, so here’s a Cheat Sheet to Help

The Technical Interview Cheat Sheet Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help. This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. 1) The interview cheat sheet is key (I actually called mine the exact same thing). Get your answers down and consistent. My goal was to know the answer to every single question I got before it was asked. 2) Differentiation is huge. Craft yourself a story, make people think you're interesting even if you're not. For me, creating this cheat sheet was like creating a cheat sheet before an exam. The process of creating it and checking the steps over and over solidified the steps in my mind. I believe anyone can pass interviews at the big tech companies, you just have to spend time learning about the interview process and preparing. INTERVIEW CHEAT SHEET THE ULTIMATE TOP TIPS & TRICKS TO IMPRESSING YOUR FUTURE EMPLOYER I am the worst at being interviewed but am the easiest person in the world to talk to. I am confident with.

This list is meant to be a both a quick guide and reference for further research into these topics. It’s basically a summary of that comp sci course you never took or forgot about, so there’s no way it can cover everything in depth.

Data Structure Basics

Note: The following data structures are called “Dynamic Sets” which basically means that you can change the value of a member.

Array

Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

What you need to know:

• Optimal for indexing; bad at searching, inserting, and deleting (except at the end).
• Linear arrays, or one dimensional arrays, are the most basic.
  • Are static in size, meaning that they are declared with a fixed size.
• Dynamic arrays are like one dimensional arrays, but have reserved space for additional elements.
  • If a dynamic array is full, it copies it’s contents to a larger array.
• Two dimensional arrays have x and y indices like a grid or nested arrays.

Big O efficiency:

• Indexing: Linear array: O(1), Dynamic array: O(1)
• Search:
  • If unsorted: Linear array: O(n), Dynamic array: O(n)
  • If sorted: Linear array: O(log n), Dynamic array: O(log n)
• Optimized Search: Linear array: O(log n), Dynamic array: O(log n)
• Insertion: Linear array: n/a Dynamic array: O(n)
• Set, Check element at a particular index: O(1)
• Deletion: Not available. One can symbolically delete an element by setting it to a value like -1 or None.

Linked List

Definition:

• Stores data with nodes that point to other nodes.
  • Nodes, at its most basic it has one datum and one reference (another node).
  • A linked list chains nodes together by pointing one node’s reference towards another node.

What you need to know:

• Designed to optimize insertion and deletion, slow at indexing and searching.
• Doubly linked list has nodes that reference the previous node.
• Circularly linked list is simple linked list whose tail, the last node, references the head, the first node.
• Stack, commonly implemented with linked lists but can be made from arrays too.
  • Stacks are last in, first out (LIFO) data structures.
  • Made with a linked list by having the head be the only place for insertion and removal.
• Queues, too can be implemented with a linked list or an array.
  • Queues are a first in, first out (FIFO) data structure.
  • Made with a doubly linked list that only removes from head and adds to tail.

Big O efficiency:

• Unordered Singly-Linked List:
  • Insert: O(1)
  • Delete: O(1)
  • Search: O(n)
  • Successor: O(n)
  • Predecessor: O(n)
  • Min: O(n)
  • Max: O(n)
• Ordered Singly-Linked List:
  • Insert: O(n)
  • Delete: O(1)
  • Search: O(n)
  • Successor: O(1)
  • Predecessor: O(n)
  • Min: O(1)
  • Max: O(1) if tail is available, O(n) otherwise
• Unordered Doubly-Linked List:
  • Insert: O(1)
  • Delete: O(1)
  • Search: O(n)
  • Successor: O(n)
  • Predecessor: O(n)
  • Min: O(n)
  • Max: O(n)
• Ordered Doubly-Linked List:
  • Insert: O(n)
  • Delete: O(1)
  • Search: O(n)
  • Successor: O(1)
  • Predecessor: O(1)
  • Min: O(1)
  • Max: O(1) if tail is available, O(n) otherwise
• ArrayList (Java has the data structure ArrayList which refers to array with dynamic size)
  • Add: Amortized O(1)
  • Remove: O(n)
  • Contains: O(n)
  • Size: O(1)
• Stack:
  • Push: O(1)
  • Pop: O(1)
  • Top: O(1)
  • Search: O(n)
• Queue/Deque/Circular Queue:
  • Enqueue: O(1)
  • Dequeue: O(1)
  • Search: O(n)

Hash Table or Hash Map or Hash Set

Definition:

• Stores data with key value pairs.
• Hash functions accept a key and return an output unique only to that specific key.
  • This is known as hashing, which is the concept that an input and an output have a one-to-one correspondence to map information.
  • Hash functions return a unique address in memory for that data.

What you need to know:

• Designed to optimize searching, insertion, and deletion.
• Hash collisions are when a hash function returns the same output for two distinct outputs.
  • All hash functions have this problem.
  • This is often accommodated for by having the hash tables be very large.
• Hashes are important for associative arrays and database indexing.

Big O efficiency:

• Indexing: O(1)
• Search: O(1)
• Insertion: O(1)
• Deletion: O(1)
• Re-size/hash: O(n)
• Min: O(1)
• Max: O(1)
• Successor: O(1)
• Predecessor: O(n)

Heap/PriorityQueue (min/max)

Big O efficiency:

• Find Min/Find Max: O(1)
• Insert: O(log n)
• Delete: O(log n)
• Extract Min/Extract Max: O(log n)
• Search : O(n), look through each node

What you need to know:

How to delete node x from a min-heap:

1. save the value V in the rightmost node in the bottom level then delete this node.
2. Put V into the node containing x and then SIFT V UP, if it is smaller than its parent, or SIFT it DOWN if it is larger than either of its children.Summary: delete the last node and move the value to the node to be deleted

Binary Tree

Definition:

• Is a tree like data structure where every node has at most two children.
  • There is one left and right child node.
• Each node may have: key, satellite data, left ptr, right ptr, parent ptr

What you need to know:

• Designed to optimize searching and sorting.
• A degenerate tree is an unbalanced tree, which if entirely one-sided is a essentially a linked list.
• They are comparably simple to implement than other data structures.
• Used to make binary search trees.
  • A binary tree that uses comparable keys to assign which direction a child is.
  • Left child has a key smaller than it’s parent node.
  • Right child has a key greater than it’s parent node.
  • There can be no duplicate node.
  • Because of the above it is more likely to be used as a data structure than a binary tree.
• Three types of tree walks:
  • Inorder: print out all keys following this order: left subtree, root, right subtree
  • Postorder: roots after left and right subtree
  • Preorder: root before left and right subtree

Big O efficiency:

• Indexing: Average O(log n), Worst Case: O(n)
• Search: Average O(log n), Worst Case: O(n)
• Insertion: Average O(log n), Worst Case: O(n)
• Deletion: Average O(log n), Worst Case: O(n)
• Min: Average case: O(log n), Worst Case: O(n)
• Max: Average case: O(log n), Worst Case: O(n)
• Successor: Average case: O(log n), Worst Case: O(n)
• Predecessor: Average case: O(log n), Worst Case: O(n)
• Inorder-walk: O(n)

How to solve the worst case issue with Binary Tree: Use Balanced Tree

Red-Black Tree( Other examples are splay tree, AVL tree)

What you need to know:

https://www.cs.auckland.ac.nz/software/AlgAnim/red_black.html

Guarantee O(logn) search times - in a dynamic environment. Can be re-balanced in O(logn) time.

Big O efficiency:

Insert, delete and search: Average case: O(log n), Worst Case: O(log n)

Search Basics

Breadth First Search

Definition:

• An algorithm that searches a tree (or graph) by searching levels of the tree first, starting at the root.
  • It finds every node on the same level, most often moving left to right.
  • While doing this it tracks the children nodes of the nodes on the current level.
  • When finished examining a level it moves to the left most node on the next level.
  • The bottom-right most node is evaluated last (the node that is deepest and is farthest right of it’s level).

What you need to know:

• Optimal for searching a tree that is wider than it is deep.
• Uses a queue to store information about the tree while it traverses a tree.
  • Because it uses a queue it is more memory intensive than depth first search.
  • The queue uses more memory because it needs to stores pointers

Big O efficiency:

• Search: Breadth First Search: O(

  E

  +

  V

  )

• E is number of edges
• V is number of vertices

Depth First Search

Definition:

• An algorithm that searches a tree (or graph) by searching depth of the tree first, starting at the root.
  • It traverses left down a tree until it cannot go further.
  • Once it reaches the end of a branch it traverses back up trying the right child of nodes on that branch, and if possible left from the right children.
  • When finished examining a branch it moves to the node right of the root then tries to go left on all it’s children until it reaches the bottom.
  • The right most node is evaluated last (the node that is right of all it’s ancestors).

What you need to know:

• Optimal for searching a tree that is deeper than it is wide.
• Uses a stack to push nodes onto.
  • Because a stack is LIFO it does not need to keep track of the nodes pointers and is therefore less memory intensive than breadth first search.
  • Once it cannot go further left it begins evaluating the stack.

Big O efficiency:

• Search: Depth First Search: O(

  E

  +

  V

  )

• E is number of edges
• V is number of vertices

Breadth First Search Vs. Depth First Search

• The simple answer to this question is that it depends on the size and shape of the tree.
  • For wide, shallow trees use Breadth First Search
  • For deep, narrow trees use Depth First Search

Nuances:

• Because BFS uses queues to store information about the nodes and its children, it could use more memory than is available on your computer. (But you probably won’t have to worry about this.)
• If using a DFS on a tree that is very deep you might go unnecessarily deep in the search. See xkcd for more information.
• Breadth First Search tends to be a looping algorithm.
• Depth First Search tends to be a recursive algorithm.

Efficient Sorting Basics

When to choose what sorting algorithm

Reference: http://web.mit.edu/1.124/LectureNotes/sorting.html

• Merge Sort is usually preferred for sorting linked list
• Quick Sort is O(n^2) in already sorted list

Merge Sort

Definition:

• A comparison based sorting algorithm
  • Divides entire dataset into groups of at most two.
  • Compares each number one at a time, moving the smallest number to left of the pair.
  • Once all pairs sorted it then compares left most elements of the two leftmost pairs creating a sorted group of four with the smallest numbers on the left and the largest ones on the right.
  • This process is repeated until there is only one set.

What you need to know:

• This is one of the most basic sorting algorithms.
• Know that it divides all the data into as small possible sets then compares them.

Big O efficiency:

• Best Case Sort: Merge Sort: O(n)
• Average Case Sort: Merge Sort: O(n log n)
• Worst Case Sort: Merge Sort: O(n log n)

Quicksort

Definition:

• A comparison based sorting algorithm
  • Divides entire dataset in half by selecting the average element and putting all smaller elements to the left of the average.
  • It repeats this process on the left side until it is comparing only two elements at which point the left side is sorted.
  • When the left side is finished sorting it performs the same operation on the right side.
• Computer architecture favors the quicksort process.

What you need to know:

• While it has the same Big O as (or worse in some cases) many other sorting algorithms it is often faster in practice than many other sorting algorithms, such as merge sort.
• Know that it halves the data set by the average continuously until all the information is sorted.

Big O efficiency:

• Best Case Sort: Merge Sort: O(n)
• Average Case Sort: Merge Sort: O(n log n)
• Worst Case Sort: Merge Sort: O(n^2)

Bubble Sort

Definition:

• A comparison based sorting algorithm
  • It iterates left to right comparing every couplet, moving the smaller element to the left.
  • It repeats this process until it no longer moves and element to the left.

What you need to know:

• While it is very simple to implement, it is the least efficient of these three sorting methods.
• Know that it moves one space to the right comparing two elements at a time and moving the smaller on to left.

Big O efficiency:

• Best Case Sort: Merge Sort: O(n)
• Average Case Sort: Merge Sort: O(n^2)
• Worst Case Sort: Merge Sort: O(n^2)

Merge Sort Vs. Quicksort

• Quicksort is likely faster in practice.
• Merge Sort divides the set into the smallest possible groups immediately then reconstructs the incrementally as it sorts the groupings.
• Quicksort continually divides the set by the average, until the set is recursively sorted.

Others

• Insertion Sort: traverse the list, put each entry in its position
• Selection sort: select the smallest entry and swap it to the 1st position, then the 2nd smallest entry into 2nd position.
• Bucket sort: linear time, assuming input from uniform distribution
• Radix Sort: linear time, assuming the input are integers
• Heap sort: O(n) + O(nlogn) = O(nlogn)
  • build a max-heap O(n), from n/2 to 1(all non-leaf nodes), max-heapify each element
  • i= n to 2, swap a[i] with root a[1], s.t. a[i] becomes the largest, remove it from the heap, max-heapify(a, 1). O(n*lgn)
  • Misc: Build a max-heap in linear time (for the first-half/non-leaf nodes, run max_heapify which itself takes O(lgn))

Basic Types of Algorithms

Recursive Algorithms

Definition:

• An algorithm that calls itself in its definition.
  • Recursive case a conditional statement that is used to trigger the recursion.
  • Base case a conditional statement that is used to break the recursion.

What you need to know:

• Stack level too deep and stack overflow.
  • If you’ve seen either of these from a recursive algorithm, you messed up.
  • It means that your base case was never triggered because it was faulty or the problem was so massive you ran out of RAM before reaching it.
  • Knowing whether or not you will reach a base case is integral to correctly using recursion.
  • Often used in Depth First Search

Iterative Algorithms

Definition:

• An algorithm that is called repeatedly but for a finite number of times, each time being a single iteration.
  • Often used to move incrementally through a data set.

What you need to know:

• Generally you will see iteration as loops, for, while, and until statements.
• Think of iteration as moving one at a time through a set.
• Often used to move through an array.

Recursion Vs. Iteration

• The differences between recursion and iteration can be confusing to distinguish since both can be used to implement the other. But know that,
  • Recursion is, usually, more expressive and easier to implement.
  • Iteration uses less memory.
• Functional languages tend to use recursion. (i.e. Haskell)
• Imperative languages tend to use iteration. (i.e. Ruby)
• Check out this Stack Overflow post for more info.

Pseudo Code of Moving Through an Array (this is why iteration is used for this)

Greedy Algorithm

Definition:

• An algorithm that, while executing, selects only the information that meets a certain criteria.
• The general five components, taken from Wikipedia:
  • A candidate set, from which a solution is created.
  • A selection function, which chooses the best candidate to be added to the solution.
  • A feasibility function, that is used to determine if a candidate can be used to contribute to a solution.
  • An objective function, which assigns a value to a solution, or a partial solution.
  • A solution function, which will indicate when we have discovered a complete solution.

What you need to know:

• Used to find the optimal solution for a given problem.
• Generally used on sets of data where only a small proportion of the information evaluated meets the desired result.
• Often a greedy algorithm can help reduce the Big O of an algorithm.

Pseudo Code of a Greedy Algorithm to Find Largest Difference of any Two Numbers in an Array.

This algorithm never needed to compare all the differences to one another, saving it an entire iteration.

Dynamic Programming

Definition:

• Programming here means planning.
• This is an algorithm that caches previous results for later use in the case where the subproblems overlap.

What you need to know:

• Difference with Recursion: In recursion, subproblems do not overlap.
• Often very efficient at the cost of extra space.

NP-complete problems

Classic examples:

Operating System:

Key concepts

• processes
• threads
• concurrency issues
• locks, mutexes, semaphores, monitors, deadlock, livelock
• context switching works
• scheduling ( Problems in this category can be formulated with priority queue )

INTERVIEW CHEAT SHEET

THE ULTIMATE TOP TIPS & TRICKS TO IMPRESSING YOUR FUTURE EMPLOYER

I am the worst at being interviewed but am the easiest person in the world to talk to. I am confident with holding intelligent conversation with anyone but ask me personal questions within an interview setting and I turn in to a gibberish mess.

I am sure a lot of people can relate to the above statement. I did a long time ago but not anymore since I became more aware and more prepared. The below is, in my mind, one of the best guides for interviewing for your next job and how to be prepared in the first instance.

BE PREPARED

It is often the simple and most obvious things, which can make the difference between securing or losing your ideal job offer. Before the interview there are several things that you can do to give you that leading edge over other applicants. A positive attitude is essential to convince the employer to offer you that lucrative position.

DO SOME RESEARCH

Research the company in as much detail as you can. You can visit the company websites for more detailed information and request a copy of their Annual Report. You can also contact your consultant who will have expert knowledge of the client, the environment you'll be working in, the company culture as well as the employer's expectations and candidate requirements. Take advantage of their experience and they will help you secure your ideal role

It is important to find out specific facts about the company:

• What are their products and services?
• What is their growth potential for the future?
• Who are their main competitors?
• How are they viewed in the market place?

There are a number of research applications providing this information. Among the most helpful are:

• The company's annual reports.
• Kompass.
• Textline.
• The Stock Exchange Research handbook.
• The Internet/Company websites.

Ensure that you are also up to speed with the facts and figures of your present/former employer. You will be expected to know a lot of information about the company you have previously worked for.

FIRST IMPRESSIONS COUNT

Ensure that you wear your smartest suit and act in a professional manner throughout the interview. A company is more likely to hire somebody who is well presented as they will be future representatives of their company

BE ON TIME

Ensure that you know the exact time and location of the interview. Your recruitment consultant will provide you with a contact name and also a map of how to get there if required. Allow plenty of time in case of travel delays

INTERVIEW ULTIMATE TIPS

INTERVIEW DO'S

• Switch your mobile off.
• Introduce yourself courteously.
• Arrive on time or earlier if possible (but not too early, 5-10 minutes is acceptable).
• Express yourself clearly.
• Smile during the interview.
• Show how your experience can benefit the company.
• Ask questions concerning the company.
• Construct your answers carefully.
• Show willingness to learn and progress.
• Be assertive without being aggressive.
• Prepare 10 relevant questions, you'll probably cover 5 in the interview.

INTERVIEW DON'TS

• Don't be late for the interview.
• Don't be unprepared for the interview.
• Don't answer questions with a 'yes' or 'no'. Expand whenever possible.
• Don't lie. Answer all questions truthfully and honestly
• Don't overemphasise money. Do not discuss salary in the 1st interview unless they do; getting the job at this stage is the main priority - salary negotiations will follow.
• Don't say negative things about previous employers.
• Don't show lack of career planning or aspirations.
• For every responsibility/requirement on the job specification, ensure that you have at least one example of an experience or a transferable skill that covers that requirement for the interview.

TYPICAL EMPLOYER QUESTIONS

Q: Tell me about yourself (Interviewer thinks: I want to hear you talk)

A: This is a conversation starter and is nearly always asked. Talk about your qualifications, career history and range of skills. Particularly emphasise those skills that are most relevant to the position on offer.

Q: Describe your achievements to date: (What they want to know: Are you successful?)

A: Another common question, so be prepared beforehand. Select an achievement that is career related. Identify the skills you used in this situation and quantify the benefit.

Q: Has your career met your expectations? (What they want to know: Are you confident, happy, positive, and ambitious?)

A: Answer must be a resounding 'Yes' however if you feel you are moving too slowly, then give reasons for this. Qualify your answer.

Q: Tell me the most challenging situation you have faced recently and how you dealt with it? (What they want to know: Are you logical? Do you show initiative? What's your definition of difficult?)

A: This is a trap question. To avoid it select a difficult work situation that was not caused by you, the options available, how you selected the appropriate one and why and how you resolved it and what the outcome was. Ensure that it is positive.

Q: What are your strengths? (Interviewer thinks: I hope you're honest, what have you got that's different? How can I use you in the team? What value will you add to the company?)

You are going to get asked this question, so there is no excuse for not being prepared. Discuss your main strengths. List three or four ways they could benefit your employer. Strengths to consider include technical proficiency, ability to learn quickly, determination, positive attitude and your ability to relate to people and work as a team. Provide examples and be prepared to back them up.

Q: What are your major weaknesses? (Interviewer thinks: I hope you're honest, what aren't you interested in? What will you need help with? What's your self-awareness like)

A: Don't say 'none' - we all have some! There are two options available when asked such a question - use a professional weakness such as lack of experience on your part in an area that is not essential to the job on offer. The second option is to describe a personal or professional weakness that could also be considered a strength and the steps that you have taken to combat this.

Q: What decisions do you find difficult to make? (What they want to know: Are you decisive? Do you have a human side?)

Javascript Technical Interview Cheat Sheet

A: Your answer must not display weakness. Focus on decisions that have to be made without sufficient information. This will show your positive side.

Q: Why are you leaving your current employer?

A: Should be a straightforward answer - looking for more challenge, responsibility and experience. DO NOT be negative in your reasons for leaving, positive reasons are better.

Q: How do you deal with confrontation? (What they want to know: Are you strong? Can you admit you're wrong?)

Technical Interview Cheat Sheet Printable

A: Again - this is a trap question. Demonstrate that you're willing to listen, implement changes where necessary, but you have the courage of your convictions and will be firm when necessary.

Show you have researched the firm's position in the market, what the company's strategy is, how long the particular department you are interviewing for has been around and what their corporate image is looking to project.

I would always commit to memory at least 5 facts that are achievements you have had within your career. Link to them problems you have overcome to reach those achievements etc. Once you have them committed to memory then you should never falter when presented with a question that usually makes you go.... errrrr.......ummmm. Why should we employ you? You have 5 loaded bullets in your memory to fire at this question. What makes you go the extra mile? Bang, forth committed answer comes to play, and so on.

OTHER LIKELY QUESTIONS

INDUSTRY/ROLE

• What do you enjoy about the industry? Why do you want to work in this industry?
• What kinds of people do you like working with?
• How does your job fit into your department and company? (Gives an idea of level of responsibility)
• What are you looking for in a company?
• What changes in the workplace have caused you difficulty and why?
• How do you feel about working long hours and/or weekends?
• Which part of this role is least attractive to you?
• How do you see this job developing your skills and experience?
• Why do you want to work in this area of this company?
• What qualifies you for this job?
• Where do you see this job going?
• Why do you think you would like this role?

YOUR SKILLS

• How do you respond to working under pressure? Provide examples.
• How have you coped when your work has been criticised? (Give an example including the outcome).
• What is the worst situation you have faced outside work? (Give an example including the outcome).
• How do you measure your own performance?
• What motivates you?
• Why do you think you would be good at this job?
• What example can you give me of when you have been out of your depth?
• What have you failed to achieve to date?
• What can you bring to this organisation?
• What area of your skills do you want to improve? (Try to relate this to the role on offer).

DEALING WITH OTHER PEOPLE

• What kind of people do you find difficult to work with? (Be extremely careful when answering this question).
• Tell me about the last time you disagreed with your boss. How did you resolve this?
• Where have you been unable to get on with others? (Give an example and how you resolved/overcome the situation).
• What are your preferred working conditions, working alone or in a group and why?
• How do you think you are going to fit in here especially as this organisation is very different to your current employer? (You may not be able to answer until you have established what he/she perceives as the differences).

ABOUT YOU AND THE FUTURE

Technical Interview Cheat Sheet 2020

• Where do you see yourself in five years time?
• Why should I give this position to you instead of the other people on the shortlist? (Strengths).
• What reservations should I have about you as an employee? (Weaknesses).
• What do you do in your spare time?
• What will you do if you don't get the job?

QUESTIONS TO EMPLOYERS

The interview is a two-way process. As well as the employer interviewing you, you are also interviewing your prospective employer. Remember, employers ask questions to get information out of you - but it is a two way process - make sure you tell them the information you want them to hear. Prepare questions prior to the interview:

• How has this position become vacant?
• What will my role entail?
• What will my daily routine involve?
• How does my role fit into the structure of the overall department?
• How will my performance be monitored?
• Who will I report to?
• Who will report to me?
• What are the opportunities for further training?
• Where is your company going? Expansion plans?
• Will this position involve travelling?
• What is the objective of this organisation/department/team?
• How does the culture of this team, this organisation compare to others?
• What sort of person does well here?
• How might I influence my own future in the company?
• Which of my skills are required to do this job?
• How will this role satisfy my drives for success/progression/travel?
• What is it about this department and organisation that you (the interviewer) enjoy?
• What is the next step?

CLOSING THE INTERVIEW

If you are interested in the role, ask about the next interview stage if appropriate. If the interviewer offers you the job on the spot and you want it, accept it there and then. If you require further time to think it over, be tactful in saying so and qualify your reasons. Try and provide a definite date as to when you can provide an answer.

Do not be disappointed if no definite job offer is made at the interview stage. The interviewer will in most cases need to consult ccolleagues first or interview other suitable candidates.

If you feel that the interview is not going well, do not be discouraged. Sometimes this is part of an interviewer's technique to see how you perform under pressure - and may have no bearing on whether you will/will not get the job. Display a positive attitude at all times.

Ensure that you THANK the interviewer.

AFTER THE INTERVIEW:

After the interview it is essential that you call your recruitment consultant and provide feedback. In most situations the consultant will not be able to get feedback from the client without speaking to you first. Any delay in providing this feedback can slow down the whole process. One of the most important learning aspects of interviewing is the feedback that you'll receive from the recruitment consultants after they've spoken to your potential employer. Whether it is positive or negative, it is essential that you take it on board and use it for future interviews. Feedback is a great learning opportunity for you.

FINAL THOUGHTS

Hopefully this has been of interest to somebody out there and more so, of benefit to someone in a better interviewing technique. Please share and like this post so others will also benefit from the information.