高频题（0）
1. Marketing
| marketing promotion measure/experiments 0.75hr
ramping a new feature to help merchants grow called 'marketing program' .This feature
will allow merchants to set promotions (like free delivery, discount) at certain period of
time.Like ‘happy hour'
1.any questions？any limitations on participants？ who is paying for the discount benefit？ - no limitation
- merchants are paying cost themselves
2.how to understand if merchants are interests in this?
3.How to define ROI return on investment for merchants
4.What negative impact can you think of with the feature
5. how to you quantify the customer side returns.
6. How to do A/B test for this - select metrics; what to split, why, why not consider a,b,c;
Why t-test
7. Once we’ve done the experiment, how to translate that to side wide impact for ramp
up decisions - asked in details why l made some assumptions and how l project it, why l
can hold true.Start from small markets, monitor, improve.可以在同类型的市场ship这个
experience，然后1）选择不同类型的市场reiterate，或2）继续explore有没有更好的radius
(15 miles, etc.)
ramping a new feature to help merchants grow called 'marketing program' .This feature
will allow merchants to set promotions (like free delivery, discount) at certain period of
time.Like ‘happy hour'
1.any questions？any limitations on participants？ who is paying for the discount benefit？ - no limitation
- merchants are paying cost themselves
2.how to understand if merchants are interests in this?
3.How to define ROI return on investment for merchants
4.What negative impact can you think of with the feature
5. how to you quantify the customer side returns.
6. How to do A/B test for this - select metrics; what to split, why, why not consider a,b,c;
Why t-test
7. Once we’ve done the experiment, how to translate that to side wide impact for ramp
up decisions - asked in details why l made some assumptions and how l project it, why l
can hold true.Start from small markets, monitor, improve.可以在同类型的市场ship这个
experience，然后1）选择不同类型的市场reiterate，或2）继续explore有没有更好的radius
(15 miles, etc.)
2. Predict delivery time
| Before a consumer places an order on DoorDash, we show the expected delivery time. It
is very important for DoorDash to get this right, as it has a big impact on consumer experience. Order lateness /underprediction of delivery time is of particular concern as past experiments suggest that underestimating delivery time is roughly twice as costly as overestimating it .Orders that are very early / late are also much worse than those that are only slightly early / late. In this exercise ,you will build a model to exercise, you will build a model to predict the estimated time taken for a delivery.
Concretely. for a given delivery you must predict the total delivery duration seconds, i.e.,the time from
Start: the time consumer submits the order (created at) TO
End: when the order will be delivered to the consumer (actual_delivery_time)
To help with this,we have provided
Historical¬_data.csv: table of historical deliveries (your training set)
Data_to_predict.csv: data for deliveries that you must predict (label-free test set we will
use for evaluation)
Data_description.txt: description of all columns in historical_data.csv and details of
Data_to_ predict.csv
is very important for DoorDash to get this right, as it has a big impact on consumer experience. Order lateness /underprediction of delivery time is of particular concern as past experiments suggest that underestimating delivery time is roughly twice as costly as overestimating it .Orders that are very early / late are also much worse than those that are only slightly early / late. In this exercise ,you will build a model to exercise, you will build a model to predict the estimated time taken for a delivery.
Concretely. for a given delivery you must predict the total delivery duration seconds, i.e.,the time from
Start: the time consumer submits the order (created at) TO
End: when the order will be delivered to the consumer (actual_delivery_time)
To help with this,we have provided
Historical¬_data.csv: table of historical deliveries (your training set)
Data_to_predict.csv: data for deliveries that you must predict (label-free test set we will
use for evaluation)
Data_description.txt: description of all columns in historical_data.csv and details of
Data_to_ predict.csv
3. Remove Duplicates from a List of Lists
During the third round of an onsite interview, you are given a list of lists and asked to remove duplicates from it.
4. Probability of Shared Birthdays
In an onsite interview, you are asked a probability question: What is the probability that in a room of three people, two of them share the same birthday?
5. Product Defects Analysis
A quality agent inspects samples of finished products represented in a two-dimensional square matrix where defective products are represented by 1 and non-defective products by 0. Develop a method to find the largest square area of defective products within the matrix. For example, given a 5x5 matrix of product samples, identify the largest sub-matrix that contains only defective products and return its size.
