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The 'new user' definition is what gets you.
Start by clarifying the definitions of 'new user', 'click-through rate', and 'pin format', then outline the necessary tables and joins. Write the query in steps: first identify new US users at event time, then aggregate impressions and clicks by pin format and event date, and finally compute CTR as clicks divided by impressions.
Pro tip: Mention that you would validate the query by checking edge cases, such as users with no impressions or clicks, and ensure that the date filtering for 'new' users is correctly applied relative to the event date. Also, consider performance implications and suggest indexing or partitioning strategies.
Define what 'new user' means (e.g., first event within 7 days), what constitutes a click and an impression, and how pin formats are categorized. Confirm the time frame for 'new' status and the event date granularity.
Determine which tables contain user events (impressions, clicks), user attributes (location, signup date), and pin metadata (format). Plan joins on user_id and pin_id, ensuring proper filtering for US users and new users at event time.
Use a subquery or join to select users who were new when the event occurred (e.g., event_date between signup_date and signup_date + 7 days) and whose location is US. Apply this filter before aggregation to avoid unnecessary computation.
Group by pin format and event date, counting clicks and impressions separately. Ensure that clicks are only counted for events that are clicks, and impressions for all relevant events.
Calculate CTR as clicks divided by impressions, using NULLIF to avoid division by zero. Consider rounding or formatting the result. Optionally, include a check for statistical significance or minimum volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two-stage logic and I fumbled the first stage a bit.
Break the problem into two parts: first identify 'fresh' pins by checking if they received at least 2 impressions within 7 days of creation, then find users who saw at least one such pin during the reporting window. Finally, compute the percentage by dividing the count of distinct users who saw a fresh pin by the total distinct users active in the window, multiplying by 100.
Pro tip: Clarify the definition of 'saw'—it could mean impression or engagement—and confirm the reporting window boundaries. Also, consider whether to include users with zero impressions in the denominator; typically, the denominator is all users active in the window, not just those who saw any pin.
Confirm what 'saw' means (e.g., impression event), the exact reporting window, and whether 'fresh' pins are determined globally or per user. State assumptions clearly.
Write a subquery to find pins that received at least 2 impressions within 7 days of their creation timestamp. This may involve joining pin creation data with impression events and filtering by time difference.
Join the fresh pins list with impression events during the reporting window to get distinct users who saw at least one fresh pin.
Determine the total number of distinct users who were active (e.g., had any impression) during the reporting window.
Divide the count of users who saw a fresh pin by the total active users, multiply by 100, and round as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty clean problem once you see it as a co-occurrence count.
First, clarify the problem: the input is a dictionary mapping board IDs to lists of pins, and we need to rank other pins by the number of boards they share with the target pin. Then, design an efficient algorithm using an inverted index from pin to boards, compute intersections, and sort by shared board count, with an optional top-N parameter.
Pro tip: Mention that in a real Pinterest-scale system, you'd precompute pin co-occurrence or use approximate methods like MinHash/LSH, but for this problem, an exact solution with an inverted index is expected. Also, discuss handling ties and the time/space trade-offs.
Ask about input size, whether boards can have duplicate pins, and if the target pin is guaranteed to exist. Clarify that 'other pins' excludes the target pin itself and that ranking is by descending shared board count.
Create a mapping from each pin to the set of boards it appears on. This allows O(1) lookup of boards for any pin and efficient intersection.
For the target pin, get its board set. For each other pin, compute the size of the intersection between its board set and the target's board set. Use a dictionary to accumulate counts.
Sort the pins by shared board count in descending order. If the optional top-N parameter is provided, return only the first N results; otherwise, return all.
Discuss time complexity: O(P * B) where P is number of pins and B is average boards per pin, but can be optimized by iterating over boards of the target pin and incrementing counts for other pins on those boards. Space complexity O(P + B).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mostly a pandas groupby question but the null handling is where people slip.
First, filter the DataFrame to include only video pins, then map category IDs to names using the external dictionary, filling missing or null IDs with 'unknown'. Group by category name, compute the average time spent, and identify the category with the highest average. For ties, return all tied categories or specify a tie-breaking rule such as alphabetical order.
Pro tip: Mention that you would validate the mapping dictionary for missing keys and consider using vectorized operations for efficiency, especially with large datasets. Also, discuss how you would handle ties in a way that aligns with business needs, such as reporting all ties or selecting the one with the most data points.
Subset the DataFrame to include only rows where the pin type is 'video' to focus the analysis on video pins.
Use the external mapping dictionary to convert category IDs to category names, and replace missing or null IDs with 'unknown'.
Group the filtered DataFrame by category name and calculate the mean of the time spent column for each category.
Find the category with the highest average time spent. If there are ties, decide whether to return all tied categories or apply a tie-breaking rule.
Articulate how you would handle ties, such as returning all tied categories, selecting the one with the most observations, or using alphabetical order, and justify your choice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.