Business Analytics Mock Interview: 20 Questions in 50 Minutes

A foreign key is a column in one table that refers to the primary key of another, such as orders.customer_id pointing to customers.customer_id. Referential integrity means every foreign key value must exist in the parent table, so you cannot have an order for a customer who does not exist. The database enforces it on insert, update and delete, which keeps joins reliable for reporting.

They assess how you think, not just the final answer. Key areas are clarifying the problem before solving, building a clear and MECE structure, handling numbers accurately with sanity checks, using business sense to prioritise, adapting to new information, and ending with a crisp recommendation with risks and next steps. Thinking aloud and communicating clearly matter as much as getting the right number.

DELETE removes selected rows using a WHERE clause, logs each row and can usually be rolled back inside a transaction. TRUNCATE removes all rows quickly by deallocating data pages, keeps the table structure and normally resets identity counters; rollback behaviour depends on the database. DROP removes the whole table, including its structure, indexes and permissions. Analysts mostly use DELETE carefully on staging tables and should rarely need TRUNCATE or DROP on production data.

System testing is done by the QA team to verify that one system works against its functional and non-functional specifications. SIT (system integration testing) checks that connected systems exchange data correctly, such as CRM to billing to payment gateway. UAT (user acceptance testing) is done by business users to confirm the solution supports real business processes and is ready for go-live. UAT asks whether this is fit for our business, not just whether it matches the specification.

Aggregate revenue per month and use LAG(revenue) OVER (ORDER BY month) to compute (revenue - previous) / previous.

A date table has one row per day with no gaps, plus columns like Year, Quarter, Month, Week and Fiscal Period. It is related to the fact table date column. Marking it as a date table tells Power BI to use it for time intelligence functions like TOTALYTD, SAMEPERIODLASTYEAR and DATESINPERIOD, which require a continuous date range. It also gives consistent month sorting and fiscal calendars across every report.

Guardrail metrics are measures that must not get worse while you try to improve the primary metric. For a test aimed at increasing conversion, guardrails might be refund rate, page load time, unsubscribe rate and customer support tickets. If the primary metric improves but a guardrail drops significantly, the change is not shipped as is. They protect long-term health from short-term optimisation.

SIPOC stands for Suppliers, Inputs, Process, Outputs and Customers. It is a one-page, high-level table that lists 4 to 7 main process steps along with who supplies what and who receives the outputs. A BA uses it at the start of a process improvement project to agree on scope and boundaries with stakeholders before spending time on a detailed BPMN map. It prevents arguments later about where the process starts and ends.

Get distinct user-month pairs, self-join or use LAG on month per user, and keep rows where the previous month is exactly one month earlier.

Top-down: NCR population aged 18–30 × share of graduates × share wanting paid upskilling × average fee. Cross-check bottom-up with institutes × batches × students × fees. State your assumptions.

Top-down starts from a large known number, such as population or total industry size, and narrows it with percentages to the target segment. Bottom-up starts from a unit, such as one store's daily sales or one customer's purchase frequency, and multiplies up by the number of units. Top-down is fast; bottom-up is more grounded in operations. Doing both and comparing results is a strong sanity check.

Find out whether it is a discovery problem or a value problem. Check how many users saw the entry point, how many tried it and how many returned. If few saw it, test better placement or onboarding prompts. If many tried and few returned, interview users and review session recordings to understand why. Also check if the 4 percent are high-value users. Then decide to improve, reposition or remove it.

Map the funnel, find the biggest drop-off, check usability and load time, run A/B tests on fixes and track conversion lift.

A condition on the right table was probably placed in the WHERE clause, for example WHERE o.order_date >= '2026-01-01'. For customers with no orders, o.order_date is NULL, so WHERE removes them and the query behaves like an INNER JOIN. Move the condition into the ON clause, as in LEFT JOIN orders o ON o.customer_id = c.id AND o.order_date >= '2026-01-01', or explicitly allow NULLs.

Compare against the baseline you measured before the change, using the same definitions: cycle time, first-time-right rate, cost per case, backlog and customer complaints. Pull data from system timestamps rather than surveys. Check that the gain is not explained by lower volumes or seasonality, ideally by comparing similar periods. Also check for side effects, such as faster processing but more rework downstream. Present the before-and-after with the business owner, and note where adoption is still incomplete.

The story is ambiguous, so bring the three amigos together: BA or product owner, a developer and a tester. Walk through concrete examples, including edge cases and error cases, and write them as acceptance criteria, ideally in Given-When-Then format. Add business rules, sample data and mock-ups where needed. Update the story and use the same practice in refinement for future stories, so gaps are found before coding starts.

Situation: operations managers did not trust a new demand forecast that drove staffing. Task: explain it so they would use it. Action: I avoided model terms and showed how it used last year's same week, recent trend and festival calendar, with a chart of past forecasts against actuals. I compared the forecast range to a weather forecast. Result: managers started using it for rosters and gave feedback that improved it further.

Situation: a new city launch was 30 percent below its order target after two months. Task: present the review to the leadership team. Action: I opened with the number directly, then broke the gap into traffic, conversion and repeat rate, showing repeat rate was the main problem. I came with three options with estimated impact and cost. Result: leadership approved a focused retention campaign instead of more ad spend, and repeat rate rose over the next quarter.

Situation: my team moved reporting from Excel to Power BI, and I had two weeks to rebuild the weekly sales report. Task: deliver it on time without errors. Action: I took a short online course, practised data modelling on a copy of our data, and asked a colleague to review my DAX measures. I reconciled every number with the old Excel report before launch. Result: the report went live on schedule, and refresh time dropped from half a day to minutes.

Keep it to about 90 seconds in a present, past, future flow. For example: I am a commerce graduate working as an analyst intern, where I build Excel and Power BI reports and write SQL queries for the sales team. Earlier, my college project analysed customer churn for a local gym and recommended changes they adopted. I want to grow as a business analyst because I enjoy turning business problems into clear requirements and data-driven decisions.