Deloitte Data Analytics Interview Questions & Answers

MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.

Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.

Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.

A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.

An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.

Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.

You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.

Use RFM scoring or clustering on behaviour and value, validate segments with business teams and attach an action to each segment.

Indexes speed up lookups by keeping sorted pointers. They slow writes and use space, so index columns used in filters and joins, not every column.

Compare with a control group or use difference-in-differences, control for seasonality, and measure incremental sales against campaign cost.

Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.

Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.

Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.

Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.

DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.

INNER JOIN returns only matching rows. LEFT JOIN returns every row from the left table and NULLs where the right table has no match — e.g. all customers, including those with no orders.

SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.

Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.

Bookmarks save a report view state for navigation; drill-through jumps to a detail page filtered to the item you picked.

It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.

It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.

A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.

Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.

A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.

df = pd.read_csv('file.csv'); then head(), info(), describe() and isna().sum() for a quick health check.

A bridge that lets the Service refresh data from on-premises sources securely on a schedule.

Start from the business questions, show 4–6 top KPIs with trend vs target, allow drill-downs, keep colours consistent, remove clutter, add time/region filters and give one clear insight per visual.

SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "North", Month, "Jan")

A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.

Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.

A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.

A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).

Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.

Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.

Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.

Read the execution plan, add or fix indexes, select only needed columns, filter early, avoid functions on indexed columns, and replace correlated subqueries with joins.

SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.

A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.

pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.

df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).

A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.

Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.

melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.

A Microsoft BI tool made of Power BI Desktop for building reports, the Service for sharing, and Mobile; it connects, models and visualises data.

A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.

Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.

Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.

Mean for symmetric data without outliers, median for skewed data (salaries, house prices), mode for categories (the most common city).

CALCULATE evaluates an expression in a modified filter context, e.g. CALCULATE([Total Sales], Region[Name] = "North"). It is the key function for time intelligence and comparisons.

Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.

Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.