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Start broad, then focus when you want structure.
Company labels help frame practice in a familiar business context. They are directional study aids, not official interview guidance from the companies listed. We keep all paths visible first so hard questions do not disappear.
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Databricks
Foundations Track
Most Frequent Item Counts
Databricks wants a scorecard for most frequent item counts. Return the item_count value or values with the highest order_occurrences. Sort by item_count.
Databricks
Foundations Track
Signup Confirmation Rate
At Databricks, analysts are preparing a scorecard focused on signup confirmation rate. Calculate the confirmation rate as the percentage of signups that were...
Databricks
Foundations Track
2024 App Click Volume
Use this Databricks planning note to work on 2024 app click volume. Return each app_id and the total number of click events recorded in 2024. Sort by app_id.
Databricks
Foundations Track
Late Shipment Rate By Carrier
Databricks wants a trend check for late shipment rate by carrier. Return the percentage of shipments that arrived late by carrier_name. Output carrier_name a...
Databricks
Foundations Track
Frequent Support Callers
At Databricks, analysts are preparing a tracking view focused on frequent support callers. Return the distinct policy_holder_id values for members who made a...
Databricks
Foundations Track
Creators With Multiple 2024 Posts
Databricks is reviewing creators with multiple 2024 posts through a weekly brief. Return each creator_id that posted at least two items in 2024. Sort by crea...
Databricks
Interview Core Track
3. How do you identify records in one table that aren't in another
To discover records in one table that are not present in another, you can utilize a LEFT JOIN and filter out any NULL values in the right-side table. For exa...
Databricks
Analytics Reporting Track
Second Customer Transaction
At Databricks, analysts are preparing a scorecard focused on second customer transaction. Return each user_id and transaction_date for the second transaction...
Databricks
Interview Core Track
1. Identifying Power Users for Databricks
As a Databricks data analyst, one of the most important tasks is identifying our power users. These are users who frequently use Databricks' tools and servic...
Databricks
Interview Core Track
Customers Buying Every Category
Databricks wants a scorecard for customers buying every category. Return each customer_id that purchased at least one product in every product_category. Sort...
Databricks
Interview Core Track
Rolling Three-day Earnings
Databricks is reviewing rolling three-day earnings through a readout. Return each user_id, transaction_date, and rolling_3_day_earnings based on the current ...