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Dev Interview Training презентация
Содержание
- 1. Dev Interview Training
- 2. Hi! I’m Gayle Laakmann McDowell Author Interview Coach Interview Consulting (CS) (MBA)
- 3. Core Beliefs Philosophies around hiring 01
- 4. #1: Interviews don’t need to mirror the real world
- 5. #2: Good candidate experiences matter
- 6. #3: You’ll never have a perfect process
- 7. #4: You need to THINK about your process
- 8. The Interview Goals & Questions 02
- 9. Goals Great employees, not great interviewees Good
- 10. Core Question Types Experience/Behavioral Questions Knowledge Design/Architecture Problem Solving/Algorithms Coding Can be mixed and matched!
- 11. Behavioral/Experience What do you ask and why? 03
- 12. 1. Why? Is this a person you
- 13. 2. Bad Practices & False Negatives Undefined
- 14. 3. Best Practices Probe deeper Be nice
- 15. 4. Evaluation A factor in ALL interviews
- 16. 5. Examples Dive into a technical project
- 17. Knowledge What do you really need? 04
- 18. 1. Why? Do they know the stuff
- 19. 2. Bad Practices & False Negatives Only
- 20. 3. Best Practices Hard to acquire OR
- 21. 4. Examples How does ____ work? How do you think it’s implemented?
- 22. Design Big, meaty problems 05
- 23. 1. Why? Tests: Ability to tackle open-ended
- 24. 2. Bad Practices & False Negatives Unreasonable
- 25. 3. Best Practices Ask open-ended problems Encourage
- 26. 4. Evaluation Ability to make tradeoffs Ability
- 27. 5. Examples Design API for… System for
- 28. Algorithms Make ‘em think 06
- 29. 1. Why? Smart people do good work
- 30. 2. Bad Practices & False Negatives Easy
- 31. 3. Best Practices Ask the right questions Be nice and friendly Coach MAKE THEM THINK
- 32. 3a. The Right Questions Medium-to-hard questions Multiple hurdles Unusual questions Avoid obscure knowledge
- 33. 3a. Reasonable Knowledge
- 34. 3b. Be Nice and Friendly Intimidated candidates
- 35. 3c. Coach Give hints as necessary Encourage
- 36. 3d. Phone Interviews vs. Onsite Don’t “go
- 37. 4. Evaluation Not just correct vs. incorrect
- 38. Rand7: Given rand5(), implement rand7() Has “a ha” moment 5. Bad Questions
- 39. Sub Permutations: Given two strings,
- 40. Coding Practical stuff 07
- 41. 1. Why? Code quality matters Not everyone can translate algorithm into code
- 42. 2. Bad Practices & False Negatives Requiring
- 43. Goal: “Seemingly compilable” code. Don’t waste time
- 44. 4. Whiteboard vs. Computer More communication More
- 45. 5. Evaluation Look at structure and style
- 46. Final Thoughts Things to remember 07
- 47. Remember: It’s on YOU to get the
- 48. Remember: Err towards noting it on feedback Be nice and friendly MAKE THEM THINK
Hi! I’m Gayle Laakmann McDowell Author Interview Coach Interview Consulting (CS) (MBA)
Слайд 9Goals
Great employees, not great interviewees
Good in 6 months, not 6 days
Reduce
false negatives
Keep candidates happy ☺
Keep candidates happy ☺
Слайд 10Core Question Types
Experience/Behavioral Questions
Knowledge
Design/Architecture
Problem Solving/Algorithms
Coding
Can be mixed and matched!
Слайд 121. Why?
Is this a person you want to work with?
Technical expertise
Has
made good, interesting technical decisions
Culture fit/personality
Not arrogant, curious, initiative, etc
Communication
Can they articulate impact?
Culture fit/personality
Not arrogant, curious, initiative, etc
Communication
Can they articulate impact?
Слайд 132. Bad Practices & False Negatives
Undefined “cultural fit”
Interviewer assumptions
Overly specific questions
Not
focusing on self
“We” not “I”
“We” not “I”
Слайд 143. Best Practices
Probe deeper
Be nice and friendly
(Even if you feel differently)
Stick
to more technical discussions
Challenge YOUR assumptions
In every interview
Challenge YOUR assumptions
In every interview
Слайд 154. Evaluation
A factor in ALL interviews
Err towards listing minor concerns
Even
if it’s just a “feeling”
Challenge your assumptions
Challenge your assumptions
Слайд 165. Examples
Dive into a technical project
Walk through design on whiteboard
Discuss tradeoffs,
key decisions, etc
Extensions to project (scaling, etc)
Focus on personal impact
Extensions to project (scaling, etc)
Focus on personal impact
Слайд 192. Bad Practices & False Negatives
Only basic knowledge
Requiring stuff you don’t
really need
Too many factual questions
Too many factual questions
Слайд 203. Best Practices
Hard to acquire OR a red flag
Relevant to job
Discussions
> fact grilling
Evaluation should be mostly qualitative
OR: Questions easily Googled are bad
Evaluation should be mostly qualitative
OR: Questions easily Googled are bad
Слайд 231. Why?
Tests:
Ability to tackle open-ended problems
Communication/teamwork skills
A different side of
problem-solving
Respects experience of senior candidates
Respects experience of senior candidates
Слайд 242. Bad Practices & False Negatives
Unreasonable knowledge expectations
Some candidates don’t know
“the flow”
Can’t ask questions
More of a factual answer
Don’t drive the process
Can’t ask questions
More of a factual answer
Don’t drive the process
Слайд 253. Best Practices
Ask open-ended problems
Encourage follow up questions
Have candidate walk through
Let candidate drive
Слайд 264. Evaluation
Ability to make tradeoffs
Ability to identify issues
Separate knowledge from attributes
Response
to feedback
Слайд 275. Examples
Design API for…
System for Amazon book rank
System for TinyURL
OOD for
a music library
Слайд 302. Bad Practices & False Negatives
Easy questions
Questions with “a ha” moments
Well
known problems (or patterns)
Слайд 323a. The Right Questions
Medium-to-hard questions
Multiple hurdles
Unusual questions
Avoid obscure knowledge
Слайд 343b. Be Nice and Friendly
Intimidated candidates do poorly
Candidates cling to every
word
Use this!
“Good job”, “great point”, etc.
Especially if they’re struggling or nervous
Use this!
“Good job”, “great point”, etc.
Especially if they’re struggling or nervous
Слайд 353c. Coach
Give hints as necessary
Encourage examples (input/output)
Remind them of key details
Stop
them from writing code too early
Слайд 363d. Phone Interviews vs. Onsite
Don’t “go easy” on the phone
But avoid
problems needing diagrams
Strings, hash tables, linked lists are easy to draw
Trees and graphs are hard
Strings, hash tables, linked lists are easy to draw
Trees and graphs are hard
Слайд 374. Evaluation
Not just correct vs. incorrect
How optimal? How quickly? How many
hints?
Compare to other candidates
Early on you won’t be calibrated
More of a “gut feel” than a metric
Compare to other candidates
Early on you won’t be calibrated
More of a “gut feel” than a metric
Слайд 39
Sub Permutations: Given two strings, s and t, find all permutations
of s within t.
5. Good Question
Слайд 422. Bad Practices & False Negatives
Requiring every detail
Tedious questions
Taking over the
testing
Letting the candidate code too early
Letting the candidate code too early
Слайд 43Goal: “Seemingly compilable” code.
Don’t waste time
Do you really need that Node
class?
Encourage abbreviations, skipping uninteresting parts, etc.
Make it clear when they should/shouldn’t code
Encourage testing, refactoring, etc
Encourage abbreviations, skipping uninteresting parts, etc.
Make it clear when they should/shouldn’t code
Encourage testing, refactoring, etc
3. Best Practices
Слайд 444. Whiteboard vs. Computer
More communication
More thought
More focus on essentials
BUT: slow &
tedious
Can be more comfortable
Can write faster
BUT: compiling can be distracting
Слайд 455. Evaluation
Look at structure and style
But differentiate what’s trainable
Not about complete
vs. incomplete
Let the candidate test
Let the candidate test
Слайд 47Remember:
It’s on YOU to get the info you want
Challenge your assumptions
Separate
“did they do X?” from “can they do X?”
