Q: Write a query to find the total number of rides per driver in the last 30 days.

SELECT driver_id, COUNT(*) AS ride_count FROM rides WHERE ride_date >= CURRENT_DATE - 30 GROUP BY driver_id ORDER BY ride_count DESC. **Why**: Simple aggregation with date filter. **Production**: Consider ride status (completed vs cancelled); adjust date column.

Question 1

How would you handle a situation where two team members disagree on a technical approach?

Accepted Answer

Situation: Two engineers disagreed on incremental vs. full-refresh. Task: Facilitate decision. Action: Structured discussion; each presented pros/cons. Ran 1-week spike. Incremental won on cost and runtime. Documented decision....

Question 2

Build an executive dashboard for reporting.

Accepted Answer

**Architecture**: Define KPIs with stakeholders. Source data—pipeline to curated layer. Design schema (star/snowflake). Build pipeline (idempotent, incremental). Visualize (Tableau, Looker). Refresh schedule (daily/hourly). **Key**: Actionable metrics; clean design; filters and drill-downs. **Scalability**: Pre-aggregate for large datasets; use materialized views....

Question 3

Write a function to find the longest palindromic substring in a given string.

Accepted Answer

**Expand Around Center:** For each center (char or between chars), expand while palindromic. O(n²) time, O(1) space. Manacher's = O(n) but complex.

def longest_palindrome(s):
    def expand(l, r):
        while l >= 0 and r < len(s) and s[l] == s[r]: l -= 1; r += 1
        return s[l+1:r]
    return max((expand(i,i) or expand(i,i+1) for i in range(len(s))), key=len, default='')

Question 4

Given a table with sales data, write a query to find consecutive days with decreasing revenue.

Accepted Answer

**LAG approach**: WITH t AS (SELECT date, revenue, LAG(revenue) OVER (ORDER BY date) AS prev_rev FROM sales) SELECT date, revenue FROM t WHERE revenue < prev_rev AND prev_rev IS NOT NULL. **Consecutive pairs**: Self-join ON a.date = b.date + 1 WHERE a.revenue < b.revenue. **N consecutive**: Extend with multiple LAG or recursive pattern....

Question 5

Write a query to find the top three customers by total revenue within each region.

Accepted Answer

SELECT * FROM (SELECT customer_id, region, SUM(revenue) AS total, RANK() OVER (PARTITION BY region ORDER BY SUM(revenue) DESC) rn FROM orders GROUP BY customer_id, region) WHERE rn <= 3. **Why**: Aggregate then rank per region. RANK or DENSE_RANK per tie rule....

Question 6

Write a query to find the total number of rides per driver in the last 30 days.

Accepted Answer

SELECT driver_id, COUNT(*) AS ride_count FROM rides WHERE ride_date >= CURRENT_DATE - 30 GROUP BY driver_id ORDER BY ride_count DESC. **Why**: Simple aggregation with date filter. **Production**: Consider ride status (completed vs cancelled); adjust date column.

Question 7

Explain how Spark handles data partitioning and the role of shuffles in performance tuning.

Accepted Answer

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Spark partitioning: data is divided into partitions; each partition maps to a task. Default partition count from input or 200. Shuffle repartitions by key (hash or range). Shuffles are expensive—network and disk I/O....

Question 8

Design a data model for a ride-hailing app.

Accepted Answer

**Section 1 — The Context (The 'Why')**
This system faces scale and failure challenges at production. A naive approach breaks under load, loses data, or violates compliance. The primary challenge varies by domain: notifications need preference respect; banking needs ACID; pipelines need idempotency....

Question 9

Design a data pipeline for streaming analytics.

Accepted Answer

**Section 1 — The Context (The 'Why')**
Streaming analytics pipelines face the fundamental tension between low-latency ingestion and consistent aggregation at scale. The primary challenge is maintaining exactly-once or at-least-once semantics while supporting late-arriving events, handling backpressure when downstream processors cannot keep pace, and avoiding data skew when hot partitions (e.g., popular products) overwhelm single workers....

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