Infosys Data Analyst interviews focus on robust SQL, business intelligence tools (PowerBI/Tableau), and communicating data insights to corporate clients. Preparing for Infosys requires practicing structured answers that highlight Business reporting, ETL pipelines, client dashboards, data governance. Below are the highest-yield interview questions with sample spoken responses.
1. Identify partition and order keys. 2. Use SUM(amount) OVER. 3. Use LAG() to fetch prior month. 4. Compute percentage growth safely.
"I use SUM(revenue) OVER (PARTITION BY region ORDER BY order_month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) to generate running cumulative totals. For MoM growth, I use LAG(revenue, 1) OVER (ORDER BY order_month) to retrieve previous month revenue, and calculate ((current - previous) / NULLIF(previous, 0)) * 100 to guard against division-by-zero errors."
Using subqueries or cursor loops instead of window functions; failing to handle division by zero or NULLs; incorrect frame clause.
1. Source validation & logging. 2. Deduplication using ROW_NUMBER(). 3. Missing data imputation vs flagging. 4. Automated alerts.
"In our ingestion stage, I partition by primary composite keys using ROW_NUMBER() OVER (PARTITION BY client_id, transaction_date ORDER BY updated_at DESC) and retain only row 1 to purge duplicates. For missing data, if non-critical, I impute using business defaults or rolling medians; if critical (e.g. monetary amounts), I route to an exception queue and alert upstream data stewards."
Deleting duplicate rows blindly without logging; filling missing financial data with arbitrary zeros; ignoring audit trails.
1. Star schema modeling (Fact & Dimension tables). 2. Pre-aggregating data in DB. 3. Reducing DAX/LOD visual complexity. 4. Incremental refresh.
"I structure data in clean Star Schemas rather than wide denormalized tables. I push heavy calculations upstream into SQL views or data warehouse aggregates, minimize expensive DAX measures or Table LODs evaluated on visual render, and configure incremental refreshes so the dashboard queries only delta updates rather than the full multi-year history."
Pulling raw transactional logs directly into memory; creating dozens of complex calculated columns; ignoring user filter context.
1. Context of client hypothesis. 2. Rigorous data validation. 3. Collaborative walkthrough of findings. 4. Business impact.
"A retail client assumed young demographics drove their weekend sales surge. My cohort analysis proved that existing loyalty card holders over 45 accounted for 68% of weekend basket value, while younger shoppers had high footfall but low conversion. Instead of telling the client they were wrong, I presented actionable suggestions: optimize campaigns for high-value loyalty segments and retool checkout offers for students, boosting overall weekend margin by 14%."
Being argumentative with stakeholders; presenting raw charts without a narrative; backing down and altering numbers to please the client.
Studying question lists gives you the theory, but live video calls with Infosys interviewers can be intimidating. When high-pressure behavioral or architecture curveballs hit, you need clarity instantly.
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