The full problem statement is available to Pro members.
preloaded fixtures
import pandas as pd
import numpy as np
meters = pd.DataFrame({
"tenant": ["Acme", "Acme", "Globex", "Initech", "Initech", "Hooli", "Hooli", "Globex"],
"service": ["Compute", "Storage", "Compute", "Compute", "Storage", "Compute", "Bandwidth", "Bandwidth"],
"day": ["2024-03-01", "2024-03-01", "2024-03-01", "2024-03-02", "2024-03-04", "2024-03-06", "2024-03-07", "2024-03-03"],
"quantity": [120.0, 5000.0, 30.0, 260.0, 12000.0, 410.0, 2200.0, 800.0],
"unit_cost": [0.12, 0.002, 0.12, 0.12, 0.002, 0.12, 0.008, 0.008],
})
meters["day"] = pd.to_datetime(meters["day"])
seats = pd.DataFrame({
"tenant": ["Acme", "Acme", "Globex", "Initech", "Hooli", "Hooli"],
"product": ["M365 E5", "Power BI", "M365 E3", "M365 E5", "M365 E5", "Power BI"],
"seats": [700, 200, 120, 450, 1000, 300],
"status": ["active", "active", "churned", "active", "active", "active"],
})
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“Cost per tenant”
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Pro problem
“Cost per tenant”
This is one of the real interview questions in Pro. Try Pro free for 7 days to unlock it and everything below:
- 293 interview-grade problems across SQL, Python & pandas
- 104+ real-world SQL problems: SaaS retention, fintech ledgers, supply chain, recursive CTEs
- 18 company interview packs (Amazon, Meta, Google, Uber and more)
- AI "explain my mistake" tutor and mock interviews
- Use QueryLab inside Claude, Cursor & other AI assistants
// built for real interview rounds
Practice the patterns 18 top companies test
329 problems · 36 free forever · 293 unlocked with Pro
Card required. Then $12/month. Cancel before your trial ends and you pay nothing.
or keep practicing free problems →Secure checkout by Paddle · instant access · cancel anytime from your account