Cognizant Data Analyst Interview Questions (2026)

1. 1GenC Online Assessment60 minMedium

   Aptitude, verbal, and basic technical MCQs; higher tiers (Elevate/Next) add progressively harder coding sections.

2. 2GenC Next Coding Assessment60 minHard

   2-3 coding problems plus SQL/full-stack questions that gate the top fresher package.

3. 3GenC Technical + HR Interview45 minMedium

   Combined panel covers projects, programming fundamentals, and HR questions on flexibility and joining in one sitting.

4. 4Technical Interview (Lateral)45 minMedium

   Skill/vertical depth: Java/.NET/cloud scenarios, or domain platforms like Facets/Guidewire for healthcare and insurance roles.

5. 5Managerial Round40 minMedium

   Delivery manager tests estimation, client escalation handling, and team leadership for experienced hires.

6. 6HR Discussion30 minEasy

   Compensation, location/shift preferences, notice-period negotiation, and documentation.

1. Q1

   Write a SQL query to find duplicate customer records in Cognizant's insurance client's claims data mart. How would you decide which record survives?

   MediumSQL roundduplicate detectionCognizant-specific

   Context: Cognizant insurance client's claims data mart

   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 Cognizant's e-commerce client's returns analysis?

   MediumSQL roundwindow functionsCognizant-specific

   Context: Cognizant e-commerce client's returns analysis

   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 Cognizant's healthcare client's appointment no-show reporting, when would an INNER JOIN silently drop rows a LEFT JOIN would keep? Show the row counts you would check.

   MediumSQL roundjoinsCognizant-specific

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

   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 Cognizant's logistics client's delivery SLA tracking does not match the source system's total. How do you reconcile them?

   HardSQL roundaggregation and reconciliationCognizant-specific

   Context: Cognizant logistics client's delivery SLA tracking

   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 Cognizant's credit-card client's spend-category dashboard contain NULLs — filters, joins, COUNT vs COUNT(col), averages?

   MediumSQL roundNULL handlingCognizant-specific

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

   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 Cognizant's banking client's loan-portfolio reporting doubled. Debug the query and prove the fix.

   HardSQL roundjoin fan-out debuggingCognizant-specific

   Context: Cognizant banking client's loan-portfolio reporting

   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 Cognizant's retail client's daily sales dashboard into CTEs, and what does it cost?

   MediumSQL roundCTEs and subqueriesCognizant-specific

   Context: Cognizant retail client's daily sales dashboard

   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 Cognizant's telecom client's churn reporting, handling ties explicitly.

   MediumSQL roundranking problemsCognizant-specific

   Context: Cognizant telecom client's churn reporting

   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 Cognizant's insurance client's claims data mart returns #N/A for valid IDs. What do you check?

   MediumExcel and BI roundlookupsCognizant-specific

   Context: Cognizant insurance client's claims data mart

   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 Cognizant's e-commerce client's returns analysis: what goes in rows, values, and filters, and when does a pivot beat formulas?

    MediumExcel and BI roundpivot tablesCognizant-specific

    Context: Cognizant e-commerce client's returns analysis

    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 Cognizant's healthcare client's appointment no-show reporting before any analysis.

    MediumExcel and BI rounddata cleaning in ExcelCognizant-specific

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

    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 Cognizant's logistics client's delivery SLA tracking need a DAX measure instead of a calculated column?

    HardExcel and BI roundDAX measuresCognizant-specific

    Context: Cognizant logistics client's delivery SLA tracking

    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 Cognizant's credit-card client's spend-category dashboard: which tables are facts, which are dimensions, and why not one flat table?

    MediumExcel and BI rounddata modelingCognizant-specific

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

    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 Cognizant dashboard for banking client's loan-portfolio reporting takes a minute to load. How do you make it fast?

    MediumExcel and BI rounddashboard performanceCognizant-specific

    Context: Cognizant banking client's loan-portfolio reporting

    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 Cognizant's retail client's daily sales dashboard, and what do you deliberately leave out?

    MediumExcel and BI rounddashboard designCognizant-specific

    Context: Cognizant retail client's daily sales dashboard

    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 the aptitude and coding test; revise programming and SQL; prepare project and HR questions

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

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