Round 1
Questions:
Question 1: Ad Event ingestion
You are given a class called AdEvent, which contains attributes of uuid (id for ad), timestamp (represented in YYYY-MM-DD format), but you can make it some integer value for easier testing, and event_type (either impression or click).
Write the following functions:
consume_ad_event(ad_event): Take in an ad event and consume it.get_historical_metrics(id): Given an ad id, return an output that looks like the following (note that the 1, 2, 3 in the test example are timestamp)
1: impressions - 5, clicks - 5 2: impressions - 10, clicks - 2 3: impressions - 15, clicks - 20, ....
has_low_performance(id, timestamp): Given an ad id along with a timestamp, identify if there were at least X impressions without any click in the last Y days. This signifies that an ad is receiving views, but no conversion! X and Y are hardcoded constants.
Follow Ups
- Suppose we store ad log events for 10 million days for each ad. What would the size of the data be? How would you handle the data volume/cost concern?
- Suppose we have a big company that makes advertisement in US, CA, JP, etc. How would you structure the backend database to support querying the performance metric by each country?
Question 2: Evaluator
You are given the following input:
equations: an array with entry of format [ai, bi]. ai, bi are variables.values: an array where values[i] = ai / bi.queries: a list of lists represented in similar way toequations.
For each query, compute the result. If unable to, return -1. Return a list of the results of the queries.
Note that this question is apparently this problem on leetcode: https://leetcode.com/problems/evaluate-division/description/
Follow Ups
- What if you had 10 million queries to this service? What design choices would you make to speed up the search?
Candidate's Approach
No approach provided.
Interviewer's Feedback
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