Accenture Data Analyst Interview Questions (2026)

1. 1Cognitive & Technical Assessment60 minMedium

   Timed online MCQs on aptitude, English, pseudocode, networking, and MS Office/cloud fundamentals.

2. 2Coding Assessment45 minMedium

   1-2 straightforward coding problems in a language of choice; correctness over optimization.

3. 3Communication Assessment30 minEasy

   Automated spoken-English evaluation (listening, pronunciation, fluency) that gates client-facing readiness.

4. 4Technical Interview45 minMedium

   Project walk-through, language/skill-track fundamentals, and scenario questions for the assigned practice.

5. 5Managerial / Client-Fit Round45 minMedium

   Delivery manager probes client-handling scenarios, estimation, escalations, and team leadership for experienced hires.

6. 6HR Discussion30 minEasy

   Compensation, location/shift flexibility, relocation, and joining timeline; light behavioral questions.

1. Q1

   Write a SQL query to find duplicate customer records in Accenture's telecom client's churn reporting. How would you decide which record survives?

   MediumSQL roundduplicate detectionAccenture-specific

   Context: Accenture telecom client's churn reporting

   How to answer: Use GROUP BY/HAVING or ROW_NUMBER over a business key, define survivorship rules, and push the fix upstream. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

2. Q2

   Using window functions, how would you add a 7-day rolling total and a rank within category to Accenture's insurance client's claims data mart?

   MediumSQL roundwindow functionsAccenture-specific

   Context: Accenture insurance client's claims data mart

   How to answer: Contrast aggregate vs window behavior, frame clauses, and partition choices. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

3. Q3

   For Accenture's e-commerce client's returns analysis, when would an INNER JOIN silently drop rows a LEFT JOIN would keep? Show the row counts you would check.

   MediumSQL roundjoinsAccenture-specific

   Context: Accenture e-commerce client's returns analysis

   How to answer: Explain join semantics with unmatched keys, and the count-before/count-after habit. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

4. Q4

   Your monthly GROUP BY summary for Accenture's healthcare client's appointment no-show reporting does not match the source system's total. How do you reconcile them?

   HardSQL roundaggregation and reconciliationAccenture-specific

   Context: Accenture healthcare client's appointment no-show reporting

   How to answer: Compare grain, filters, timezone/date boundaries, late-arriving rows, and duplicates. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

5. Q5

   Which results change when key columns in Accenture's logistics client's delivery SLA tracking contain NULLs — filters, joins, COUNT vs COUNT(col), averages?

   MediumSQL roundNULL handlingAccenture-specific

   Context: Accenture logistics client's delivery SLA tracking

   How to answer: Walk NULL semantics through WHERE, JOIN keys, aggregates, and COALESCE defaults. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

6. Q6

   After adding a join, revenue in Accenture's credit-card client's spend-category dashboard doubled. Debug the query and prove the fix.

   HardSQL roundjoin fan-out debuggingAccenture-specific

   Context: Accenture credit-card client's spend-category dashboard

   How to answer: Detect one-to-many fan-out, pre-aggregate or dedupe the many side, verify with row counts. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

7. Q7

   When would you restructure a nested-subquery report for Accenture's banking client's loan-portfolio reporting into CTEs, and what does it cost?

   MediumSQL roundCTEs and subqueriesAccenture-specific

   Context: Accenture banking client's loan-portfolio reporting

   How to answer: Readability, reuse, debugging layer by layer; note optimizer behavior differences. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

8. Q8

   Find the top 3 products per region by revenue in Accenture's retail client's daily sales dashboard, handling ties explicitly.

   MediumSQL roundranking problemsAccenture-specific

   Context: Accenture retail client's daily sales dashboard

   How to answer: ROW_NUMBER vs RANK vs DENSE_RANK, and which tie behavior the business actually wants. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

9. Q9

   Your XLOOKUP against the master sheet in Accenture's telecom client's churn reporting returns #N/A for valid IDs. What do you check?

   MediumExcel and BI roundlookupsAccenture-specific

   Context: Accenture telecom client's churn reporting

   How to answer: Trailing spaces, number-vs-text types, exact match mode, and TRIM/VALUE cleanup. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

10. Q10

    Build a pivot summary for Accenture's insurance client's claims data mart: what goes in rows, values, and filters, and when does a pivot beat formulas?

    MediumExcel and BI roundpivot tablesAccenture-specific

    Context: Accenture insurance client's claims data mart

    How to answer: Structure the source as a flat table, choose aggregations, and know pivot refresh limits. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

11. Q11

    List the cleaning steps you would run on a raw extract for Accenture's e-commerce client's returns analysis before any analysis.

    MediumExcel and BI rounddata cleaning in ExcelAccenture-specific

    Context: Accenture e-commerce client's returns analysis

    How to answer: Duplicates, blanks, types, date formats, merged cells, and a repeatable order of operations. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

12. Q12

    In Power BI, when does Accenture's healthcare client's appointment no-show reporting need a DAX measure instead of a calculated column?

    HardExcel and BI roundDAX measuresAccenture-specific

    Context: Accenture healthcare client's appointment no-show reporting

    How to answer: Row context vs filter context, storage cost, and totals that only measures get right. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

13. Q13

    Design the Power BI model for Accenture's logistics client's delivery SLA tracking: which tables are facts, which are dimensions, and why not one flat table?

    MediumExcel and BI rounddata modelingAccenture-specific

    Context: Accenture logistics client's delivery SLA tracking

    How to answer: Star schema, relationship directions, and what breaks when everything is one wide table. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

14. Q14

    The client says the Accenture dashboard for credit-card client's spend-category dashboard takes a minute to load. How do you make it fast?

    MediumExcel and BI rounddashboard performanceAccenture-specific

    Context: Accenture credit-card client's spend-category dashboard

    How to answer: Reduce cardinality, trim visuals, pre-aggregate, and measure before optimizing. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

15. Q15

    Which KPIs go on the first screen of Accenture's banking client's loan-portfolio reporting, and what do you deliberately leave out?

    MediumExcel and BI rounddashboard designAccenture-specific

    Context: Accenture banking client's loan-portfolio reporting

    How to answer: Lead with decisions the client makes weekly; park detail in drill-throughs. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

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Clear aptitude + fundamentals; communicate well; prepare basic technical + HR questions

Indicative Data Analyst pay in India: ~₹6–22 LPA (role-level range, not a Accenture-specific figure).

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