# Average Rating by Category > Category ratings. Some shine, some don't. Canonical URL: Domain: SQL · Difficulty: easy · Seniority: L3 ## Problem Before the next product roadmap meeting, the PM wants to know which categories customers love and which ones need attention. Show the average product rating for each category. ## Worked solution and explanation ### Why this problem exists in real interviews By forcing grouped aggregation on `products`, this question separates candidates who understand how `product_name`, `category`, `price` behave under aggregation from those who guess at the GROUP BY clause. --- ### Break down the requirements #### Step 1: Group by `category` `GROUP BY` at the correct grain produces one row per group. #### Step 2: Compute `AVG(rating)` The AVG function computes the avg per group. #### Step 3: Order by the metric Sort by `avg_rating` desc for readability. --- ### The solution **Group-aggregate for average rating category** ```sql SELECT category, AVG(rating) AS avg_rating FROM products GROUP BY category ORDER BY avg_rating DESC ``` > **Cost Analysis** > > The main table has 15K rows. The GROUP BY reduces the row count early, keeping downstream operations cheap. > **Interviewers Watch For** > > Strong candidates state the correct `GROUP BY` grain before writing any SQL, showing they think about the output shape first. > **Common Pitfall** > > Selecting a non-aggregated column without including it in `GROUP BY` is the most common error. Some engines reject it; others silently return arbitrary values. --- ## Common follow-up questions - The `rating` column in `products` has roughly 5% NULLs. How does your query handle those rows, and would the result change if NULLs were replaced with zeros? _(Tests whether the candidate understands how NULLs propagate through aggregation functions and whether their WHERE/JOIN conditions implicitly filter them out.)_ - Your GROUP BY aggregates `product_id` from `products`. If two groups have the same aggregate value, how is the output ordered, and is that deterministic? _(Tests awareness that ORDER BY on a non-unique value produces non-deterministic row order without a tiebreaker.)_ - The `category` column in `products` has a zipf distribution, meaning a few values dominate. How does that skew affect your query plan and parallelism? _(Tests understanding of data skew: the optimizer may choose a bad plan when histogram statistics are stale.)_ - Could you express this same logic as a single query without CTEs or subqueries? What readability trade-off does that introduce? _(Tests whether the candidate can flatten nested logic and understands when decomposition aids maintainability.)_ ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/average_rating_by_category) - [SQL Interview Questions](https://datadriven.io/sql-interview-questions) - [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep) - [Daily Challenge](https://datadriven.io/daily) --- Source: DataDriven (https://datadriven.io). 100% free data engineering interview prep. Live code execution against Postgres 16, Python 3.11, and Spark sandboxes. No paywall, no premium tier, no signup gate.