# Active Tokens on Target Date

> One specific day. Which tokens were still alive?

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

Domain: SQL · Difficulty: medium · Seniority: L3

## Problem

The security team needs to identify which token owners had a live API token on November 1, 2026. A token is considered live on that date if its status is 'active', it was issued before that date, and it had not yet expired. Tokens with no expiration date should be treated as still valid. Return a deduplicated list of owner IDs.

## Worked solution and explanation

### Why this problem exists in real interviews

The `api_tokens` schema makes this a clean test of conditional branching with CASE combined with division-safe NULLIF guards. Columns like `scope`, `status`, `issued` introduce enough ambiguity that only candidates who clarify assumptions produce correct results.

> **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: Filter to qualifying rows

Apply the WHERE clause to isolate the correct subset before computing the ratio.

#### Step 2: Group by `status`

`GROUP BY status` produces one output row per distinct value.

#### Step 3: Compute the ratio with CASE and NULLIF

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

#### Step 4: Round and order

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

---

### The solution

**Case-branch for active tokens target date**

```sql
SELECT
    status,
    ROUND(
        1.0 * COUNT(CASE WHEN status = 'active' THEN 1 END)
        / NULLIF(COUNT(*), 0),
        4
    ) AS rate
FROM api_tokens
WHERE status = 'active'
GROUP BY status
ORDER BY rate DESC
```

> **Cost Analysis**
>
> The main table has 500K 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. 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 `expires` column in `api_tokens` has roughly 8% 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 `owner_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.)_
- The `owner_id` column in `api_tokens` 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/active_tokens_on_target_date)
- [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.