Product Analyst Interview Questions

4,617 product analyst interview questions shared by candidates

They provided some CSV data and asked me question about it. Describing the data a. How many customers do we have ATO product data for? b. Over what time period does the ATO data exist? c. How many ATO detectors are there? d. What is the range of customer sizes by number of users? 2. Calculating metrics a. What is the average monthly false negative rate of the ATO product? b. What is the average monthly false positive rate of the ATO product? c. What fraction of user events are flagged by a detector at any confidence? At high confidence? d. Calculate the precision of each detector based on cases surfaced to customers. Which detector has the highest precision? Which detector has the lowest precision? e. Which detector flags the highest number of customer-confirmed true positive cases? 3. Building a data pipeline a. Calculating each metric above individually only requires a small subset of tables joined per metric. However, we want to enable all our downstream consumers, including Product and Engineering stakeholders, to have one table act as a source of truth for all analyses. We also want this same table to be upstream of all metrics calculations. Write a query which joins together all necessary information into a single table that can be used to answer all of the metrics questions above.
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Product Analyst

Interviewed at Abnormal AI

3.7
May 9, 2025

They provided some CSV data and asked me question about it. Describing the data a. How many customers do we have ATO product data for? b. Over what time period does the ATO data exist? c. How many ATO detectors are there? d. What is the range of customer sizes by number of users? 2. Calculating metrics a. What is the average monthly false negative rate of the ATO product? b. What is the average monthly false positive rate of the ATO product? c. What fraction of user events are flagged by a detector at any confidence? At high confidence? d. Calculate the precision of each detector based on cases surfaced to customers. Which detector has the highest precision? Which detector has the lowest precision? e. Which detector flags the highest number of customer-confirmed true positive cases? 3. Building a data pipeline a. Calculating each metric above individually only requires a small subset of tables joined per metric. However, we want to enable all our downstream consumers, including Product and Engineering stakeholders, to have one table act as a source of truth for all analyses. We also want this same table to be upstream of all metrics calculations. Write a query which joins together all necessary information into a single table that can be used to answer all of the metrics questions above.

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