# Attributable Impression Rate

> What share of ad impressions can be traced to a real user account

Canonical URL: <https://datadriven.io/problems/attributable_impression_rate>

Domain: SQL · Difficulty: medium · Seniority: L3

## Problem

The attribution team is measuring data completeness in the ad pipeline. What percentage of all ad impressions can be tied back to a known user account? Impressions whose user cannot be found in the user records should still count toward the total but not as attributable. Return a single percentage.

## Worked solution and explanation

### Why this problem exists in real interviews

Working with `ad_impressions`, `users`, this problem isolates conditional branching with CASE combined with division-safe NULLIF guards. The interviewer expects candidates to articulate why `ad_campaign`, `impression_time`, `clicked` matter for correctness before touching the keyboard.

> **Trick to Solving**
>
> Any rate or ratio problem requires **null-safe division**. If the denominator can be zero, the query crashes or returns NULL silently.
> 
> 1. Identify the numerator and denominator conditions
> 2. Use `SUM(CASE WHEN ... THEN 1 ELSE 0 END)` for the numerator
> 3. Wrap the denominator in `NULLIF(..., 0)` to prevent division by zero

---

### Break down the requirements

#### Step 1: Group by `ad_campaign`

`GROUP BY ad_campaign` produces one output row per distinct value of `ad_campaign`.

#### Step 2: Compute the ratio with CASE and NULLIF

The numerator uses `SUM(CASE WHEN clicked THEN 1 ELSE 0 END)`. Wrapping the denominator in `NULLIF(COUNT(*), 0)` prevents division by zero.

#### Step 3: Round and order

Use `ROUND(..., 4)` for clean decimal output and sort by rate descending.

---

### The solution

**Case-branch for attributable impression rate**

```sql
SELECT
    ad_campaign,
    ROUND(
        1.0 * SUM(CASE WHEN clicked = 1 THEN 1 ELSE 0 END)
        / NULLIF(COUNT(*), 0),
        4
    ) AS rate
FROM ad_impressions a
JOIN users b ON a.user_id = b.user_id
GROUP BY ad_campaign
ORDER BY rate DESC
```

> **Cost Analysis**
>
> The main table has 500M rows (32 GB). The GROUP BY reduces the row count early, keeping downstream operations cheap. The smaller dimension table keeps the join selective. An index on the filter or join column would improve performance at scale.

> **Interviewers Watch For**
>
> Strong candidates state the correct `GROUP BY` grain before writing any SQL, showing they think about the output shape first. Division-by-zero handling is a silent correctness bug; interviewers watch for `NULLIF` or equivalent protection.

> **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 `user_id` column in `ad_impressions` has roughly 18% 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 CASE expression branches on `user_id`. What happens if a new category value appears that none of your WHEN clauses match? _(Tests whether the candidate uses a meaningful ELSE branch or lets unmatched rows silently become NULL.)_
- `impression_id` in `ad_impressions` has ~500M distinct values. What index strategy keeps your query from doing a full table scan? _(Tests whether the candidate can design indexes for high-cardinality columns and understands selectivity.)_
- 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/attributable_impression_rate)
- [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)

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