Pinterest's OA is the most "realistic" set of questions I've done in a while. The questions themselves are like the questions you'd encounter at work when you're writing scripts, processing data, and optimizing logic.
The whole OA is divided into three coding questions + two SQL multiple choice questions, no hackerrank but using self-developed platform, the interface is very clean.
I. Coding Topics
1. Top K Most Frequent Hashtags
You're given a list of hashtags. Return the top K most frequent hashtags sorted by frequency and then lexicographically.
Input: hashtags = ["#cat", "#fun", "#cat", "#life", "#fun"], k = 2
Output: ["#cat", "#fun"]
Constraints.
- 1 <= len(hashtags) <= 10^5
- 1 <= k <= 100
- Hashtags are lowercase strings starting with '#'
2. Detect Continuous Growth Days
Given a list of user daily engagement counts, return the length of the longest continuous strictly increasing subsequence.
Input: [10, 15, 20, 10, 12, 14, 9]
Output: 3 # (10, 12, 14)
Constraints.
- 1 <= len(array) <= 10^5
- Values are non-negative integers
3. Flatten Nested Categories
Pinterest categories can be nested in JSON-like structures. You're given a nested dictionary and asked to flatten it into dot-separated You're given a nested dictionary and asked to flatten it into dot-separated keys.
Input.
{
"fashion": {
"men": {
"shoes": "leather"
{ "women": "dress}
"women": "dress"
}
}
Output.
{
"fashion.men.shoes": "leather", "fashion.
"fashion.women": "dress"
}
Hint. Use recursion.
II. Frequently Asked Questions
Q1: How many questions are there in Pinterest's OA?
- 3 Python programming questions (data processing oriented)
- 2 SQL multiple choice questions (for basic semantic judgment)
Q2: What type of company does the topic style favor?
More like Stripe, Datadog, Redpanda and other business-oriented data programming questions, the topic design is very close to the real work, do a sense of accomplishment.
Q3: How difficult are the debug / algorithm questions?
There is no debug in this set, but the questions need clean code + clear structure, and the boundary conditions must be stable. The algorithm is not too difficult, but the amount of data is large, so you need to pay attention to optimization.
Q4: Is the use of standard libraries supported?
Yes, the standard Python3 libraries support it, and even json and collections will work fine.
Q5: How long does it take to hear from me after I submit?
My experience was that I received an interview invitation (VO) 4 days after submitting, and the follow-up process went quickly.
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