Session 33 · Phase 5: Business & Mock Rounds

Mock Technical Assessment

A timed assessment covering SQL, Excel, Python, and analytics fundamentals — exactly the four areas the job description's technical round tests.

⏱ ~1.5 hrs 🧠 Mock test 🎯 Highest priority

How to Take This Assessment

Attempt the self-test questions below without peeking.
Then take the 10-question graded quiz at the bottom.
Aim for 8/10. Review any area you miss using the relevant earlier session.

Part A — SQL (Self-Test)

A1. Write a query to find customers with more than 5 orders.

SELECT customer_id, COUNT(*)
FROM orders
GROUP BY customer_id
HAVING COUNT(*) > 5;

A2. How do you find rows with no match in another table?

LEFT JOIN ... WHERE right_key IS NULL — the anti-join.

A3. What's the difference between WHERE and HAVING?

WHERE filters rows before grouping; HAVING filters groups after.

A4. How do you rank rows by a metric within each group?

ROW_NUMBER() OVER (PARTITION BY group_col ORDER BY metric DESC).

Part B — Excel (Self-Test)

B1. How do you look up a price by product ID?

=VLOOKUP(id, range, col, FALSE) or =XLOOKUP(id, ids, prices).

B2. How do you summarize sales by region without formulas?

A Pivot Table — drag region to Rows, sales to Values.

B3. What's the difference between COUNT and COUNTA?

COUNT counts only numbers; COUNTA counts all non-empty cells.

Part C — Python / Pandas (Self-Test)

C1. How do you read a CSV and check for missing values?

df = pd.read_csv("file.csv")
df.isnull().sum()

C2. How do you total sales per region?

df.groupby("region")["sales"].sum()

C3. What's the difference between loc and iloc?

loc = labels; iloc = integer positions.

Part D — Analytics Fundamentals (Self-Test)

D1. Which is robust to outliers — mean or median?

The median.

D2. Does correlation imply causation?

No — a confounding variable may drive both.

D3. A metric dropped — what do you check first?

Whether it's a data/pipeline issue vs a real change.

📋 Stable content — Reviewed: August 2026

Interview Questions — Top Technical Picks

The highest-frequency technical questions across all tools. Self-test before revealing.

IQ1. Write a query to find the top 5 customers by revenue.

SELECT customer_id, SUM(amount) AS revenue
FROM orders
GROUP BY customer_id
ORDER BY revenue DESC
LIMIT 5;

IQ2. What's the difference between WHERE and HAVING?

Model answer: "WHERE filters rows before aggregation; HAVING filters groups after. So WHERE COUNT(*) > 5 is invalid — COUNT doesn't exist until after GROUP BY."

IQ3. How do you handle missing data in Pandas?

Model answer: "Detect with isnull().sum(), then either drop with dropna() or fill with fillna() — choosing based on how much data I'd lose and why it's missing."

IQ4. What's the difference between loc and iloc?

Model answer: "loc selects by label; iloc by integer position. df.loc['Pen'] uses the name; df.iloc[0] uses the position."

IQ5. What's the difference between VLOOKUP and XLOOKUP?

Model answer: "VLOOKUP only looks right and defaults to approximate match; XLOOKUP works in any direction and defaults to exact match. XLOOKUP is the modern replacement."

IQ6. How would you build a sales summary in Excel?

Model answer: "Clean the data first, then a Pivot Table with region in Rows and sales in Values, sorted descending, with a chart and conditional formatting to highlight the top performers."

IQ7. What's the difference between a measure and a calculated column in Power BI?

Model answer: "A measure is computed dynamically at query time and not stored; a calculated column is stored per row. I use measures for aggregations."

IQ8. How do you approach a technical question you don't immediately know?

Model answer: "I restate the problem, break it into parts, and talk through my reasoning even if I'm not sure of the exact syntax. Showing the approach matters more than a silent correct answer."

Key Takeaways

SQL: WHERE before grouping, HAVING after; anti-join finds no-match rows.

Excel: pivot tables summarize; XLOOKUP is the modern lookup.

Pandas: groupby aggregates; dropna/fillna handle missing.

Stats: median is robust; correlation ≠ causation.

Narrate your reasoning on anything you're unsure of.