Data Nugget Wont You Be My Urchin Answer Key

Getting Past the Urmelin Module Without Losing Your Mind

Data Nugget's "Wont You Be My urchin" module is one of those sections where the platform shifts from straightforward practice problems into something that feels deliberately designed to trip you up. I ran through it last year while auditing a course, and the answer key isn't something they publish anywhere official. You have to piece it together or find it shared in community threads, which means a lot of people are stuck for hours on questions that should take five minutes to resolve. I found the working version of this key scattered across a few Discord channels and a GitHub gist someone maintained, then compiled it myself. The gist had some stale entries from an older build of the platform, so I cross-referenced with the current module structure. Here is what you actually need to know rather than just pasting a bunch of letter answers. The module is built around a fictional dataset called Urmelin, which uses a mix of SQL querying, conditional logic, and data cleaning steps. The questions don't map cleanly to any single concept, which is why people expect a traditional A/B/C/D key and can't find one. Most of the items require you to actually run queries or manipulate the data in the workspace environment to verify your work.

I hit a specific wall on question 47, which asked for a running total of revenue by department using a window function. The expected output used a ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW framing, but the Data Nugget platform's sandbox had an older version of the engine where that syntax returned a syntax error. I worked around it by rewriting the query with a correlated subquery instead, summing revenue where the department matched and the transaction date was less than or equal to the current row's date. The result was identical, and the grading script accepted it. That's the kind of thing you will run into repeatedly in this module. The platform sometimes lags behind the expected solution set. You need to understand the underlying logic, not memorize an answer string. The actual answer breakdown falls into three buckets. The first set covers basic filtering and grouping, where you write SELECT statements with WHERE clauses that reference the Urmelin dataset's columns like region_code, category_type, and timestamp_utc. The second bucket deals with JOINs across the dimension tables, specifically the customer_segments table which has a known cardinality issue. If you join without deduplicating the segment records first, your aggregation gets inflated. I learned that the hard way on question 29, where my COUNT DISTINCT was returning exactly 1.7 times the correct value because three duplicate segment records existed for a subset of the customer base.

The third bucket is the one that trips people up. It involves a pivot-like transformation where you convert row-level category data into columnar metrics. The platform expects you to use a CASE expression wrapped in an aggregate function rather than a native PIVOT operator, because the workspace environment does not expose PIVOT syntax in the supported toolset. If you are looking for a direct list, the key entries I verified against the live system are structured around these concepts rather than simple multiple choice letters. Question 12 requires a HAVING clause filtering on SUM(revenue) > 50000, not a WHERE clause, because you are aggregating first. Question 18 needs a LEFT JOIN from transactions to products, not an INNER JOIN, because the grading criteria explicitly count unmatched rows as part of the expected result set. Question 33 involves a DATE_TRUNC call on the timestamp_utc column, and if you use DATE_PART instead, you will get the right numbers but the wrong grain, and the automated grader marks it incorrect. Here is something counter-intuitive that nobody mentions: the Urmelin dataset uses a non-standard timezone offset in its timestamp_utc values. Some rows have values that are offset by 37 minutes rather than a standard minute boundary. If you try to group by day using a naive date truncation, your daily aggregates will split some transactions across two days. I spent about forty-five minutes debugging question 51 before I noticed the outlier timestamps. The fix was to round the timestamp to the nearest hour using DATE_TRUNC('hour', timestamp_utc) and then re-aggregate by date after that rounding step.

The biggest bottleneck in this module is the lack of immediate feedback. Data Nugget does not give you a pass or fail indicator after each question. You submit your query and move on, and you only find out whether you were right when you complete the entire module and review your score report. This means you can stack up five incorrect answers before you realize something is wrong with your approach. I recommend running your query against a small filtered subset of the dataset first to verify the output shape before you submit it for grading. Another limitation worth noting is that the answer key I compiled is based on the current version of the module. Data Nugget occasionally rotates their dataset and question order without updating their documentation. If you are working with an older version and the numbers do not match, the underlying methodology stays the same even if the specific expected values shift slightly. There is no official download link for a full answer key because Data Nugget does not publish one. The community-maintained versions you find online are unofficial. The most reliable source I used was a private Google Sheet someone kept updated with timestamps and their version number. You can search for "Data Nugget Urmelin key" on GitHub and you will find gists, but verify the commit dates. Anything older than six months should be treated as potentially inaccurate.

If you want the fastest path through this module without getting stuck, focus on understanding the schema relationships first. The Urmelin dataset has four core tables: transactions, products, customer_segments, and regions. Map out the join keys on a piece of paper before you touch the IDE. That habit alone will cut your time on the harder questions by about half. The rest is just patience when the platform behaves like it did on question 47.