---
title: Excel Interview Questions and Answers (2026 Guide)
description: VLOOKUP vs XLOOKUP, INDEX-MATCH, pivot tables, SUMIFS, COUNTIFS, IFERROR, data cleaning and how to talk through a live Excel skills test — with real answers.
url: https://usegreenroom.app/blog/excel-interview-questions
last_updated: 2026-06-22
---

[← Back to blog](/blog)

**Excel · Data Analyst / Business Analyst**

# Excel Interview Questions and Answers

*June 22, 2026 · 19 min read*

![A laptop showing an Excel spreadsheet with a pivot table mid-build, next to a notepad with interview prep notes](/assets/blog/excel-interview-questions-hero.webp)

There is a very specific kind of silence that happens when a hiring manager shares their screen, opens a spreadsheet with 40,000 rows of regional sales data, and says: "Take a few minutes, then walk me through how you'd find the top three underperforming products by region."

You have used Excel for six years. You have built dashboards. You have a LinkedIn headline that says "Excel power user." And in this exact moment, your brain produces nothing except the faint, ambient memory of a keyboard shortcut you used once in 2021.

This is the **Excel interview** in its natural habitat — not a multiple-choice quiz about cell references, but a live, screen-shared, slightly adversarial test of whether you can actually move through real data under mild social pressure. Data analyst and business analyst roles run this gauntlet constantly, because Excel is still the lingua franca of every finance team, ops team, and "can someone just send me the numbers" Slack thread on earth, regardless of how much a job description name-drops Tableau or Power BI underneath it.

The good news: the actual skill set being tested is narrow and learnable. **VLOOKUP/XLOOKUP**, **INDEX-MATCH**, pivot tables, a handful of formulas (SUMIFS, COUNTIFS, IFERROR), some data cleaning instincts, and the ability to narrate your own thinking out loud. That's the whole test, dressed up in different spreadsheets every time.

This guide covers all of it — the formulas, the pivot table questions, the data cleaning scenarios, the live skills test format, and the answers that separate "I've used Excel" from "I can be handed a mess and make it make sense, while talking."

## Table of contents

- Why Excel still gets its own interview round in 2026
- VLOOKUP vs INDEX-MATCH vs XLOOKUP — the question that comes up every single time
- Core formula questions: SUMIFS, COUNTIFS, IFERROR and friends
- Pivot table questions and a worked example
- Data cleaning and "this spreadsheet is a disaster" scenarios
- Charts and visual storytelling questions
- How to survive a live Excel skills test (screen-share format)
- Excel vs. Google Sheets vs. Power BI vs. SQL — what each interview actually wants
- Frequently asked questions

## Why Excel still gets its own interview round in 2026

Here's the thing nobody puts in the job posting: most companies do not run on a perfectly governed data warehouse with self-serve BI tools and clean semantic layers. Most companies run on a shared drive with seventeen files named `Q3_Forecast_FINAL_v3_actual_final.xlsx`, and somebody — often the person being interviewed right now — is expected to be the one who can open that file and not flinch.

Excel interview rounds exist for three blunt reasons:

1. **It's the fastest way to test real analytical instinct.** A pivot table prompt reveals in ninety seconds whether you think in rows-and-aggregations, the same way a SQL `GROUP BY` does — without needing a database set up.
2. **It's still the actual job, half the time.** Finance, ops, sales analytics, and a large share of "data analyst" roles at non-tech companies live in Excel daily. The interview is testing the tool you'll use on day one, not a tool you might touch eventually.
3. **It filters resume inflation.** "Advanced Excel" is the most over-claimed line on every analyst resume on the planet. A two-minute live test separates the people who can actually build a pivot table from the people who once watched someone else build one.

If you're prepping for adjacent roles, two Greenroom guides pair well with this one: our [business analyst interview questions](/blog/business-analyst-interview-questions) guide covers the requirements-and-stakeholder side of the role, and our [data analyst interview questions](/blog/data-analyst-interview-questions) guide covers the SQL and statistics half of the same job. Excel questions usually show up as one round among several, not the whole interview — so it helps to know what's coming in the others too.

## VLOOKUP vs INDEX-MATCH vs XLOOKUP — the question that comes up every single time

If an interviewer asks you exactly one Excel theory question, it is some version of: *"What's the difference between VLOOKUP and INDEX-MATCH, and which do you use?"* In 2026, the honest, complete answer also has to mention **XLOOKUP**, because pretending it doesn't exist makes you look out of date.

![A comparison card showing VLOOKUP, INDEX-MATCH and XLOOKUP side by side with their trade-offs](/assets/blog/excel-interview-questions-figure.webp)

### What does VLOOKUP do, and what's its biggest limitation?

`VLOOKUP` searches for a value in the **leftmost column** of a range and returns a value from a column to the right of it, in the same row.

```
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])
```

Example: you have an `Employee ID` in column A and want their `Department` from column D of a lookup table.

```
=VLOOKUP(A2, EmployeeTable, 4, FALSE)
```

The `FALSE` (or `0`) at the end means "exact match" — leaving this out, or using `TRUE`, tells Excel to find the *closest* match, which is almost never what you want and is a classic source of silently wrong numbers.

VLOOKUP's real limitations, the ones an interviewer wants to hear:

- **It can't look left.** The value you're searching for must be in the first column of the range. If your lookup value is in column D and the answer is in column A, VLOOKUP simply cannot do it — you'd need to rearrange columns or switch functions.
- **It breaks when columns are inserted.** The `col_index_num` is a hardcoded position (like `4`). Insert a new column into the table, and every VLOOKUP silently starts pulling from the wrong column — no error, just wrong data.
- **It's slower on large datasets** than INDEX-MATCH, because it has to scan the full lookup column for every row.

### What's the INDEX-MATCH combination, and why do some analysts prefer it?

`INDEX-MATCH` separates "where is the match" from "what do I return," using two functions together:

```
=INDEX(return_range, MATCH(lookup_value, lookup_range, 0))
```

Same example as above:

```
=INDEX(EmployeeTable[Department], MATCH(A2, EmployeeTable[Employee ID], 0))
```

Why analysts who learned Excel before 2022 default to this:

- **It can look in any direction** — left, right, doesn't matter, because `MATCH` just returns a position and `INDEX` just returns a value at that position.
- **It's column-order independent.** Insert a column anywhere in the middle of your table, and INDEX-MATCH still works, because it's referencing the column by name/range, not by a hardcoded number.
- **It's measurably faster** on big sheets, since `MATCH` against a single column is cheaper than `VLOOKUP` scanning an entire table array.

### What is XLOOKUP, and is it actually better?

`XLOOKUP`, introduced in Excel 365/2021 and now the default in most modern installs, is Microsoft's answer to "why do I need two functions for this":

```
=XLOOKUP(lookup_value, lookup_array, return_array, [if_not_found])
```

Same example, one function:

```
=XLOOKUP(A2, EmployeeTable[Employee ID], EmployeeTable[Department], "Not found")
```

XLOOKUP genuinely is the better default in 2026, for three reasons worth saying out loud in an interview:

- **One function, not two** — easier to read, easier to audit, easier to explain to a teammate six months later.
- **Built-in "not found" handling** — no need to wrap it in `IFERROR` separately, though you still can.
- **Searches either direction natively**, and defaults to exact match (the safer default VLOOKUP doesn't have).

**The answer that actually lands in an interview** isn't "XLOOKUP is strictly better, full stop." It's something like: *"I default to XLOOKUP when it's available, because it's cleaner and safer by default. I still reach for INDEX-MATCH on older files or shared templates that need to stay compatible with Excel versions before 365, and I know VLOOKUP well enough to read and fix it in legacy workbooks — which is most of what I actually inherit."* That answer shows judgment, not just memorization.

## Core formula questions: SUMIFS, COUNTIFS, IFERROR and friends

After the lookup question, interviewers move to conditional aggregation — because almost every real business question is some version of "sum/count X where Y is true."

### How do SUMIF and SUMIFS differ?

`SUMIF` takes one condition. `SUMIFS` takes multiple conditions, and — this trips people up — the **syntax order is reversed** between them.

```
=SUMIF(range, criteria, [sum_range])
=SUMIFS(sum_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...)
```

Worked example: total revenue where `Region = "West"` and `Quarter = "Q3"`:

```
=SUMIFS(Revenue, Region, "West", Quarter, "Q3")
```

Say this distinction out loud unprompted in an interview — "the sum range comes first in SUMIFS, last in SUMIF" — and you've already signaled you've actually written these formulas under real conditions, not just read about them.

### How do COUNTIF and COUNTIFS work?

Same pattern, but for counting matching rows instead of summing a value:

```
=COUNTIFS(criteria_range1, criteria1, [criteria_range2, criteria2], ...)
```

Example: how many orders were both `Status = "Delayed"` and `Region = "East"`:

```
=COUNTIFS(Status, "Delayed", Region, "East")
```

A common follow-up: *"How would you count unique values matching a condition?"* The honest answer in classic Excel is that `COUNTIFS` alone counts rows, not distinct values — you'd combine it with `SUMPRODUCT` and `1/COUNTIF(...)`, or in modern Excel, just lean on `UNIQUE` plus a `COUNTIFS` wrapped around it, or a pivot table with "Distinct Count" as the summary type. Knowing that the simple function has a real limitation — and naming the workaround — is the signal.

### What does IFERROR actually do, and where does it go wrong?

```
=IFERROR(value, value_if_error)
```

`IFERROR` catches **any** error — `#N/A`, `#DIV/0!`, `#VALUE!`, `#REF!` — and returns a fallback instead. It wraps around a formula:

```
=IFERROR(VLOOKUP(A2, Table, 4, FALSE), "Not found")
```

The trap interviewers love to probe: **IFERROR can hide real bugs.** If your `VLOOKUP` is wrong because of a typo in the lookup table — not because the value is genuinely missing — wrapping it in `IFERROR` will quietly swallow that and show "Not found" everywhere, making the actual bug invisible. The mature answer: use `IFERROR` for genuinely expected missing-data cases (a customer with no email on file), not as a blanket suppressor slapped on every formula to make red triangles go away. If you want to catch only the "not found" case specifically and let other errors surface, `IFNA` is the narrower tool.

### What's the difference between absolute and relative cell references, and why does it matter when copying formulas?

A relative reference (`A2`) shifts when you copy the formula to a new cell. An absolute reference (`$A$2`) stays locked. A mixed reference (`$A2` or `A$2`) locks only the column or only the row.

This matters constantly in practice: copy a `SUMIFS` formula down 500 rows where one of the criteria ranges should always point at the same lookup table, and forgetting the `$` locks means row 2's formula points at the right table, but row 50's formula has silently drifted 48 rows down into empty cells — producing zeros that look like real data. Naming this exact failure mode, unprompted, is one of the highest-signal things you can say in an Excel interview, because it's the bug every single person who's used Excel for more than a month has shipped at least once.

### What are some other formulas worth knowing?

- **`TEXT()`** — formats a number/date as a string for display (`=TEXT(A2, "mmm-yyyy")`).
- **`CONCATENATE`/`TEXTJOIN`** — joins text; `TEXTJOIN` is the modern pick because it has a built-in delimiter and can skip blanks.
- **`TRIM`** — strips leading/trailing (and extra internal) spaces, the single most common fix for "why isn't my VLOOKUP matching" when the data was pasted from somewhere else.
- **`LEFT`/`RIGHT`/`MID`** — substring extraction, common in cleaning concatenated IDs or codes.
- **`IF` nested vs. `IFS`** — `IFS` (modern Excel) replaces deeply nested `IF(IF(IF()))` chains with a flatter, more readable list of condition-result pairs.
- **`UNIQUE`, `FILTER`, `SORT`** — the newer dynamic array functions that spill results across multiple cells automatically, increasingly expected knowledge in 2026 interviews even for non-engineering analyst roles.

## Pivot table questions and a worked example

If lookups test "can you join data," pivot tables test "can you summarize it." This is usually the part of the interview where you're actually handed a real dataset.

### Walk me through how you'd build a pivot table from this sales data

Say it out loud as you go, in this order — interviewers are listening for the sequence, not just the destination:

1. **Select the data range** (or better, confirm it's an Excel Table first with `Ctrl+T`, so the pivot source auto-expands if rows are added later).
2. **Insert → PivotTable**, choosing "New Worksheet" so the raw data stays untouched.
3. **Drag the dimension you're grouping by** (e.g., `Region`) into **Rows**.
4. **Drag the metric** (e.g., `Revenue`) into **Values** — confirm it defaulted to "Sum" and not "Count," which it does silently if the column has even one blank or text cell.
5. **Add a second dimension** (e.g., `Product Category`) into **Rows** below the first, or into **Columns** to cross-tabulate.
6. **Filter or slice** — add a **Filter** field (e.g., `Quarter`) or insert a **Slicer** for interactive filtering if this needs to be reusable by someone else later.
7. **Sort** the Rows by the Values column descending, so the biggest numbers surface first — this is the step people forget, and it's the difference between a pivot table and an *answer*.

### What's the difference between a pivot table's "Sum," "Count," and "Average" summary types, and when would each be wrong by default?

Excel guesses the summary type based on the data in the column you drop into Values: numeric columns default to **Sum**, anything with a single blank or text value defaults to **Count**. This is a real trap — drop a `Revenue` column that has one row where someone typed "TBD" instead of a number, and your entire pivot table silently switches to counting rows instead of summing dollars, with no warning. Always glance at the field's summary type after adding it, every time.

### How would you calculate a percentage of total inside a pivot table?

Right-click the Values field → **Show Values As → % of Grand Total** (or % of Column Total, % of Row Total, depending on the question). This is the "I actually know the tool past the basics" answer — most people instead build a separate formula outside the pivot table to do this manually, which is slower and breaks the moment the pivot table refreshes.

### What's a calculated field, and when do you need one?

A **calculated field** lets you create a new metric *inside* the pivot table using existing fields — e.g., `Profit Margin = SUM(Profit) / SUM(Revenue)`. Built via **PivotTable Analyze → Fields, Items & Sets → Calculated Field**. The key nuance interviewers probe: a calculated field operates on the **aggregated totals**, not row-by-row — so `AVERAGE(Margin per row)` is mathematically different from `SUM(Profit)/SUM(Revenue)` for the whole group, and conflating the two produces a subtly wrong number that looks completely plausible.

### What is a pivot chart, and how is it different from a regular chart built on raw data?

A pivot chart is bound to a pivot table — filter or change the pivot table, and the chart updates automatically. A regular chart built on raw data has to be manually rebuilt or have its source range adjusted. For any dashboard meant to be reused or handed off, pivot charts (or charts on top of pivot table output) save real maintenance time later.

## Data cleaning and "this spreadsheet is a disaster" scenarios

This is the part of the job nobody puts on a slide deck, and it's exactly what interviewers want to see you handle calmly.

### How would you find and remove duplicate rows?

**Data → Remove Duplicates**, selecting which columns define a "duplicate" (a row can be a full duplicate on every column, or just a duplicate on a key like `Customer ID` while other columns differ). Before clicking it, the better instinct to state out loud: *"I'd first use a `COUNTIFS` or conditional formatting to see how many duplicates exist and on what column, so I understand what I'm about to delete before I delete it irreversibly."* Excel's "Remove Duplicates" doesn't ask for confirmation per row — it just deletes, so a quick sanity check first is the professional habit, not paranoia.

### A column of dates is being read as text, not real dates. How do you fix it?

This happens constantly with exports from other systems. The fixes, roughly in order of how often they work:

- **Text to Columns** (Data tab) with no delimiter change, just clicking through the wizard — this often forces Excel to re-parse the column as dates.
- **`=DATEVALUE(A2)`** in a helper column, then paste values back over the original.
- If the format is unusual (like `20260315` as a single text string), use `=DATE(LEFT(A2,4), MID(A2,5,2), RIGHT(A2,2))` to construct a real date from substrings.

The interview signal here isn't memorizing the exact formula — it's recognizing the *symptom* (dates left-aligned instead of right-aligned, `SUM`/date math failing silently) before you're told what's wrong.

### How do you handle inconsistent text — e.g., "USA", "U.S.A.", "United States" all meaning the same thing?

Name the real options and their trade-offs: a manual **Find & Replace** for a small, known list of variants; a `VLOOKUP`/`XLOOKUP` against a small "standardization" mapping table for a larger list (the scalable answer); or, for genuinely messy free text, Power Query's **Group By** to surface every distinct value first so you can see the full scope of the mess before touching anything. Jumping straight to fixing without first *seeing* the full list of variants is how you fix 80% of cases and miss the weird ones that break a downstream report two weeks later.

### What's your process when you're handed a spreadsheet you've never seen before?

This question is really asking "do you have a system, or do you just start clicking." A strong answer, in order:

1. **Scan structure first** — how many sheets, are there merged cells, hidden rows/columns, frozen panes that hint at what mattered to whoever built it.
2. **Check column headers and data types** — does every column actually contain what its header claims, spot-checking a few cells.
3. **Look for blanks and `#N/A`/`#REF!` errors** — `Ctrl+G → Special → Blanks` or a quick filter surfaces these fast.
4. **Sanity-check totals** — does a quick `SUM` or `COUNT` roughly match what you'd expect given the row count, catching gross errors before you build anything on top of bad data.
5. **Only then** start building formulas or pivot tables on top of it.

### What's the difference between Find & Replace and Power Query for cleaning data, and when would you reach for each?

Find & Replace is fast and fine for a one-off, known substitution. **Power Query** (Data → Get & Transform) is the right tool when cleaning needs to be **repeatable** — e.g., a report that refreshes monthly with new raw data in the same messy shape every time. Power Query records your cleaning steps (remove columns, split a column, change a type, filter rows) as a reusable, re-runnable pipeline, so next month's data goes through the exact same cleaning automatically instead of you redoing manual steps by hand. Mentioning Power Query unprompted is a strong signal you've worked with recurring, not just one-off, reporting.

## Charts and visual storytelling questions

### How do you decide which chart type to use for a given dataset?

The honest, simple framework: **comparison across categories** → bar/column chart. **Trend over time** → line chart. **Part-to-whole composition** → stacked bar (pie charts only for very few categories — more than 4-5 slices and a pie chart becomes unreadable, which is worth saying). **Relationship between two numeric variables** → scatter plot. Naming this framework, rather than just "I'd use a bar chart," shows you think about charts as communication, not decoration.

### What makes a chart misleading, and how do you avoid it?

Real answers an interviewer wants to hear: a **truncated y-axis** that doesn't start at zero, exaggerating small differences into apparent dramatic swings; **dual axes** with mismatched scales making unrelated trends look correlated; too many data series crammed onto one chart, burying the actual point. The fix in every case is the same instinct: ask "what's the one thing I want someone to take away from this chart in five seconds," and strip everything that doesn't serve that.

### How would you build a chart that updates automatically as new data comes in?

Base the chart on an **Excel Table** (`Ctrl+T`) rather than a fixed range — Tables auto-expand as rows are added, and any chart or pivot table built on top of a Table inherits that auto-expansion for free, with zero manual range updates needed later.

<div class="verdict">
  <strong>The bottom line:</strong> An Excel interview is rarely testing whether you've memorized a function's exact syntax — it's testing whether you reach for the right tool, notice the trap before it bites, and can narrate your reasoning while doing it live. XLOOKUP over VLOOKUP when you have the choice, SUMIFS/COUNTIFS for conditional aggregation, pivot tables for summarizing, and a calm, structured process for the inevitable messy spreadsheet — that combination covers the overwhelming majority of what gets asked.
</div>

## How to survive a live Excel skills test (screen-share format)

Plenty of analyst interviews include a 15-30 minute segment where you share your screen and work through a real (or realistic, sanitized) dataset live, with the interviewer watching and sometimes interjecting. This format has its own etiquette that has nothing to do with Excel skill and everything to do with not freezing.

**Narrate before you click.** Say "I'm going to insert a pivot table here to summarize by region" *before* you do it, not after. Silent clicking reads as either uncertainty or showing off — neither is the impression you want, and narrating gives the interviewer a chance to redirect you before you've gone five minutes down the wrong path.

**If you don't remember exact syntax, say so and reason through it.** "I don't remember if it's `range_lookup` defaulting to TRUE or FALSE in VLOOKUP, so I always type FALSE explicitly to be safe" is a *better* answer than confidently guessing wrong. Interviewers are grading judgment under uncertainty, not perfect recall — this is the same instinct that matters in a verbal mock interview, which is exactly why practicing out loud (not just reading formulas silently) is the actual skill being tested. It's a big part of why we built Greenroom as a voice-first tool rather than a flashcard app — the muscle you need in this round is talking through your reasoning live, under mild time pressure, which silent reading simply doesn't train.

**Sanity-check your own number before presenting it.** If a pivot table says total revenue is $4.2, and every other number on the sheet is in the thousands, say "wait, that looks wrong, let me check the summary type" out loud — catching your own mistake in real time is a stronger signal than getting it right by luck the first time.

**Ask clarifying questions before diving in**, the same way you would in a real job: "When you say 'underperforming,' do you mean below target, below last quarter, or below the regional average?" A precise definition up front saves you from confidently solving the wrong problem for five minutes.

**Manage the clock out loud.** If you're 10 minutes into a 15-minute segment and not done, say so: "I'm going to simplify my approach here given time — instead of a fully dynamic dashboard, I'll get you the core numbers first." Interviewers would much rather see you triage under a deadline than watch you silently run out the clock on something elaborate and unfinished.

## Excel vs. Google Sheets vs. Power BI vs. SQL — what each interview actually wants

Worth addressing directly, because it comes up: **"Why are we still testing Excel when there's Power BI / Tableau / SQL?"** The honest comparison, and a credible answer if asked:

- **Excel** wins on speed for ad hoc, one-off analysis and is still the universal hand-off format between teams that don't share the same BI tooling — a `.xlsx` opens for literally everyone.
- **Google Sheets** has near-identical formula syntax (most of this guide's formulas work unchanged) but adds real-time collaborative editing, which Excel only recently caught up to with co-authoring — useful to mention if the role is collaboration-heavy.
- **Power BI / Tableau** win for recurring, refreshable dashboards meant for a non-technical audience to self-serve, with proper data modeling underneath. See our [Power BI interview questions](/blog/power-bi-interview-questions) and [Tableau interview questions](/blog/tableau-interview-questions) guides if those are also on your interview slate.
- **SQL** wins the moment the dataset stops fitting comfortably in a spreadsheet, or multiple people need to query the same source of truth without emailing files back and forth. Our [SQL interview questions](/blog/sql-interview-questions) guide covers that side.

A strong answer to "why still Excel" isn't defensive — it's something like: *"Excel is still where messy, first-look analysis happens before it's worth the overhead of a formal pipeline. Knowing when a question deserves five minutes in Excel versus a proper SQL query or a Power BI model is its own skill, and I'd rather not over-engineer a one-time ad hoc request."*

It's also worth being honest about how people actually prepare for this, because interviewers have heard every version of it: a GeeksforGeeks-style page of 50 Excel functions to memorize, a friend's WhatsApp-forwarded PDF of "Excel interview questions," or typing the same questions into ChatGPT and reading the answers silently. All three get you the *facts*. None of them put you under the actual condition you'll face in the room — someone watching you reason live, with a clock running. That gap is exactly what a voice-based mock, talking through a pivot table build or a VLOOKUP failure out loud the way you would on a real call, is built to close.

## Frequently asked questions

### Do I need to memorize exact Excel formula syntax for an interview?

Mostly no — interviewers care far more about whether you know *which* function solves a problem and *why* it's the right one than whether you have the exact argument order memorized. Saying "I'd use SUMIFS here, and I'd double check the sum_range comes first" is a stronger answer than silently guessing and getting an argument order wrong.

### What's the single most common Excel interview question?

Some version of "VLOOKUP vs. INDEX-MATCH" (and increasingly "vs. XLOOKUP") comes up in nearly every Excel-focused interview round. Knowing all three, their trade-offs, and a clear personal default is the highest-leverage thing to prepare.

### Is Excel still relevant if a company uses Power BI or Tableau?

Yes — most companies that have Power BI or Tableau for finished dashboards still use Excel constantly for ad hoc analysis, one-off requests, and as the universal file format for sharing data with people outside the analytics team. Interviewers test Excel because it's still the daily tool for a large share of analyst work, regardless of what the polished dashboard layer is built in.

### How long does an Excel skills test usually last in an interview?

Typically 15 to 30 minutes, often screen-shared, working through a realistic (sometimes sanitized real) dataset with a specific business question to answer — not a written quiz, a live working session.

### What should I do if I don't know the answer to an Excel question?

Say what you do know, name the gap honestly, and reason through it out loud rather than guessing silently. "I'm not sure of the exact function name, but I know I'd want to count distinct values matching a condition — I'd look at COUNTIFS combined with something to dedupe, or just build a quick pivot table with Distinct Count" shows real problem-solving even without perfect recall.

### What's the difference between a formula and a function in Excel?

A **function** is a predefined operation like `SUM` or `VLOOKUP`. A **formula** is the full expression typed into a cell, which may combine one or more functions — e.g., `=IFERROR(VLOOKUP(A2, Table, 4, FALSE), "Missing")` is one formula built from two functions. Interviewers occasionally use this distinction to check basic fluency, not as a trick question.

## Practice it out loud, not just on paper

Reading this guide gets you the facts. The part that actually fails people in the room is reasoning through a pivot table or a broken VLOOKUP live, while talking, with someone watching. Greenroom runs voice-first mock interviews that put you through exactly that pressure beforehand — so the real thing is the second time you've done it, not the first.

If Excel is one round among several for your target role, pair this with our [data analyst interview questions](/blog/data-analyst-interview-questions), [business analyst interview questions](/blog/business-analyst-interview-questions), and [Tableau interview questions](/blog/tableau-interview-questions) guides to cover the rest of the loop.