Pilot近期OA简介这次面试分为两个部分，总共时长为100分钟，使用了一个叫做Woven的在线工具。下面我会详细介绍每个部分的内容和我的感受，希望对正在准备类似面试的小伙伴有所帮助。
1. 数据提取（40分钟）
这个部分的重点是SQL能力。你会得到一个数据库的schema，以及一个需要解决的分析性问题。面试要求你编写SQL查询，不仅要正确地回答问题，还需要展示你对基本和高级SQL语法的掌握。这40分钟的时间其实比较紧张，所以建议大家提前多做一些SQL练习，尤其是在多表联接和复杂查询上。
2. 数据探索（60分钟）
接下来的60分钟，你将获得一份数据集，并需要通过探索性分析来挖掘出有价值的见解。这个部分考验的是你的数据分析直觉和从数据中发现问题、总结规律的能力。除了分析数据外，你还需要将发现的内容清晰地表达出来，所以数据可视化和报告撰写也是非常重要的技能。
整个面试过程对时间管理要求较高，所以在正式开始之前，建议大家先熟悉一下Woven这个工具，并且做好充足的准备工作。这次面试不仅考察了技术能力，还考察了如何在有限时间内高效地处理任务。
Hi everyone! Today, I’d like to share my recent experience with a technical interview I had with Pilot. This interview was structured into two parts, totaling 100 minutes, and was conducted using an online tool called Woven. I’ll walk you through each section and share my thoughts on how to prepare effectively, hoping this will be useful for anyone facing a similar challenge.
1. Data Extraction (40 minutes):
The first part of the assessment focuses on your SQL skills. You’re given a database schema along with a specific analytical question that you need to answer by writing SQL queries. The challenge is to not only get the correct result but also to demonstrate your understanding of both basic and advanced SQL concepts. The 40-minute timeframe is tight, so I recommend brushing up on your SQL, especially on topics like multi-table joins and complex queries, before taking this assessment.
2. Data Exploration (60 minutes):
The second part is all about exploring a dataset to extract meaningful insights. You’re provided with a dataset and are expected to analyze it thoroughly, identifying trends, patterns, or any significant findings. This section tests your data intuition as well as your ability to communicate your findings clearly. Skills in data visualization and report writing are crucial here to present your analysis effectively.
Time management is key throughout this interview process, so I suggest getting familiar with the Woven tool ahead of time and making sure you’re well-prepared before diving in. This interview not only assesses your technical abilities but also how efficiently you can tackle tasks within a limited timeframe.
I hope this insight helps those preparing for similar interviews. Good luck, and I hope you ace it!
Q1: Problem Statement
Given the schema presented below, find the two lawyers who worked in the most trials together. List the lawyers' full names with the titles of the trials they participated in together. Order the results alphabetically by trial title.
Desired output
Your query should get results in the following format:
first_lawyer | second_lawyer | title
----------------+-----------------+-------------------
John Doe | Jane Doe | Johnson v Adams
John Doe | Jane Doe | Smith v Davis
...
- first_lawyer - Their first name and last name, separated by a space
- second_lawyer - Their first and last name, separated by a space
- title - Trial title
Schema
Table lawyer
| Column | Type | Modifiers |
|---|---|---|
| lawyer_id | integer | not null |
| first_name | character varying(45) | not null |
| last_name | character varying(45) | not null |
| ... |
Table trial
| Column | Type | Modifiers |
|---|---|---|
| trial_id | integer | not null |
| title | character varying(255) | not null |
| ... |
Table attorney
| Column | Type | Modifiers |
|---|---|---|
| lawyer_id | smallint | not null |
| trial_id | smallint | not null |
| ... |
Q2: Analyzing Sales Data Scenario
Analyzing Sales Data
You open up your email client and see this email from Casey Sells, a Director of Sales at your company:
From: Casey Sells (csells@seashell-appliances.com) To: You (your_name@seashell-appliances.com) Subject: January 2022 Sales
Hey there. We’ve gotten reports from the team that sales were down in January 2022. Our total sales came out to $25,049.00, which doesn’t match our projections. We need your help figuring out whether something changed and what exactly is going on.
I’ve attached a data export with about 5 years of sales figures. All currency is in US dollars ($). Can you dig into the data and let me know if you notice any recent trends? It would be helpful to have:
- Some kind of visualization to make it easy to see what’s going on
- A written analysis I can share with the team
Also, to ensure this dropoff doesn't happen again, can you please suggest a couple of metrics the team can use to monitor sales performance?
Thanks for your help! Casey
Instructions for the Scenario
Instructions
Write your email response to Casey in the email.md file.
The sales data is in a CSV file at data/sales_data.csv.
Details
This Qualified environment has Python 3.8 with Pandas, NumPy, and Matplotlib imported. If you’re using the environment provided here in Qualified, you can run the code in notebook.py by pressing the "Run Tests" button. There are no actual automated tests—this is just a way to run your code in the environment.
As an alternative, you may download the sales data CSV and use your own tools to do the data analysis. Candidates have used Microsoft Excel, PowerBI, Jupyter Notebook, R, or other tools to complete this analysis.
To download the data file, copy/paste the contents of data/sales_data.csv, or use the "Save Locally" button in the top-right corner of the editor with data/sales_data.csv open. It looks like this: ![Save Locally]
Please use the tools that allow you to make the most progress on this scenario.
Guidance
We will only evaluate the contents of the email.md file. We will not run your code, so be sure to put any visualizations you want to share into the email file. If you generate visualizations in Qualified or your own environment, either drag and drop them in or screenshot and paste them in.
We're more interested in your research and communication skills than the code that you write. We won’t be evaluating your code for this, only what you write in your email. Since this is how we will evaluate your response, err on the side of over-communicating the analysis you did in your email.
If you run low on time, document any analysis that you did in email.md for partial credit.
CSV Data Snapshot
Transaction #,Date,Salesperson,Product,Units,Unit price,Total
7588571682,2016-06-01,Tanisha Fadel,Air purifier,3,92,276
1524592357,2016-06-01,Forest Nicolas,Blender,3,29,87
5792641262,2016-06-01,Tanisha Fadel,Electric kettle,4,59,236
1245606118,2016-06-01,Amee Green,Electric kettle,3,55,165
9455600825,2016-06-01,Lennie Kunze,Futon,3,349,1047
3831822961,2016-06-02,Lisabeth Cummings,Coffee machine,3,31,93
7863060404,2016-06-02,Lisabeth Cummings,Electric kettle,3,51,153
4408950716,2016-06-02,Tanisha Fadel,Futon,4,351,702
4771323146,2016-06-03,Nichole Considine,Electric kettle,3,41,123
7197352188,2016-06-03,Forest Nicolas,Futon,4,349,1396
6615366161,2016-06-04,Tanisha Fadel,Air purifier,3,110,330
9005283939,2016-06-04,Amee Green,Coffee machine,4,40,160
2777311123,2016-06-04,Tanisha Fadel,Electric kettle,3,58,174
8793807982,2016-06-05,Myesha Ritchie,Air purifier,4,102,408
6947265166,2016-06-06,Tanisha Fadel,Air purifier,4,96,384
6466558139,2016-06-06,Lennie Kunze,Blender,4,25,100
5123750950,2016-06-07,Diane Ritchie,Blender,3,32,96
2767245573,2016-06-07,Ginny Daugherty,Coffee machine,4,39,156
3604805396,2016-06-07,Myesha Ritchie,Coffee machine,2,34,68
4500255652,2016-06-07,Nichole Considine,Futon,4,358,1432
3932587759,2016-06-08,Myesha Ritchie,Blender,2,39,78
4651754767,2016-06-08,Tanisha Fadel,Blender,2,33,66
1872454642,2016-06-08,Lennie Kunze,Dishwasher,2,206,412
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