← Salesforce Interview Insights
I started with orders and customers because that felt natural, but I probably should've anchored on stores first since everything else hangs off that.
Start by clarifying the scope and key requirements of the system, then identify the core entities and their relationships. Propose a logical data model with tables, primary/foreign keys, and cardinalities, and discuss how it supports scalability, transactions, and reporting.
Pro tip: Emphasize how your design handles real-world scenarios like inventory depletion, order customization, and loyalty point accrual/redemption, showing you think beyond static entities.
Ask questions to understand expected scale, key use cases (e.g., mobile ordering, in-store POS), and any constraints like multi-store inventory or franchise models.
List main entities (Store, MenuItem, Ingredient, Inventory, Employee, Order, Customer, LoyaltyProgram) and define their relationships and cardinalities.
For each entity, specify attributes, primary keys, foreign keys, and junction tables for many-to-many relationships (e.g., OrderItems, RecipeIngredients).
Discuss how to handle inventory tracking per store, order customization, loyalty point transactions, and historical data (e.g., price changes).
Walk through a sample order flow to validate the model, and mention potential extensions like promotions, suppliers, or analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The order placement API was fine, talked through idempotency and stock validation before confirming.
Start by clarifying the system's context and constraints, then walk through each API endpoint in a structured way, covering HTTP method, path, request/response schemas, and key design decisions. Emphasize trade-offs, error handling, and how the APIs integrate with each other and with Salesforce's ecosystem.
Pro tip: Demonstrate awareness of idempotency, versioning, and rate limiting—these are critical in enterprise systems like Salesforce and show you think beyond basic CRUD. Also, mention how you would leverage Salesforce APIs (e.g., REST, Bulk, Streaming) if relevant.
Ask clarifying questions about scale, consistency needs, authentication, and integration points. This shows you don't jump to solutions without understanding the problem.
Define the endpoint (e.g., POST /orders), request/response payloads, and key operations like validation, payment processing, and order confirmation. Discuss idempotency and error handling.
Define the endpoint (e.g., PATCH /inventory/{itemId}), how updates are triggered (e.g., after order placement), and mechanisms for consistency (e.g., optimistic locking, event-driven updates).
Define the endpoint (e.g., POST /schedules), input parameters (employee availability, shift requirements), and output (schedule). Discuss algorithm considerations and conflict resolution.
Cover authentication, authorization, rate limiting, versioning, monitoring, and how these APIs interact. Highlight trade-offs like sync vs async, REST vs GraphQL, and consistency models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and business definitions (e.g., what constitutes a 'sale', how to handle ties, and whether 'top-selling' means by quantity or revenue). Then outline a query using a window function like ROW_NUMBER() or RANK() partitioned by store and ordered by sales, filtered by the given time period. Finally, discuss performance considerations and edge cases.
Pro tip: Mention that you would confirm whether ties should return multiple items or just one, and propose using RANK() if ties matter or ROW_NUMBER() if a single item is needed. Also, suggest indexing on store_id and order_date for performance.
Ask about the tables involved (e.g., orders, order_items, menu_items, stores) and how 'top-selling' is defined (quantity sold vs. revenue). Confirm the time period format and whether ties should be handled.
Write a subquery or CTE that joins the necessary tables, filters by the given time period, and groups by store and menu item to calculate total sales (e.g., SUM(quantity) or SUM(quantity * price)).
Use a window function like ROW_NUMBER() or RANK() over a partition by store, ordered by total sales descending, to assign a rank to each item within its store.
Select only the rows where the rank equals 1 (or rank <= N if top N is needed). If ties should be included, use RANK() or DENSE_RANK() and filter accordingly.
Mention indexing on store_id and order_date, and consider performance for large datasets. Discuss handling of ties, nulls, and stores with no sales.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Joined inventory to a thresholds table and filtered where current_quantity fell below the threshold.
Start by clarifying the schema and business rules: what defines low stock (e.g., quantity below reorder threshold), which tables hold inventory and store data, and whether alerts should be per product per store. Then write a query that joins inventory with product/store tables, filters rows where quantity < threshold, and returns store, product, and current quantity. Consider edge cases like products not stocked at a store or multiple warehouses per store.
Pro tip: Mention that in a real system, you'd likely need to handle time-based thresholds or dynamic reorder points, and that the query should be efficient with proper indexing on store_id and product_id. Also, clarify whether the alert should be generated only for active stores or products.
Ask about the tables involved (e.g., stores, products, inventory), the definition of low stock (fixed threshold vs. per-product reorder level), and whether alerts are per store-product combination. Confirm if you need to include stores with zero inventory.
Determine which tables to join (e.g., inventory to stores and products) and the filter condition (e.g., quantity < reorder_threshold). Consider if you need to aggregate across multiple locations per store.
Construct a SELECT statement with appropriate JOINs, WHERE clause for low stock, and output columns like store name, product name, and current quantity. Use aliases for readability.
Address scenarios like products not carried at a store, stores with no inventory records, and NULL thresholds. Discuss indexing on foreign keys and filtering columns to optimize the query.
Walk through the query logic, explain how it meets the requirements, and mention any assumptions made. If time permits, suggest how to extend it for dynamic thresholds or scheduled alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second on how to handle overnight shifts where clock-out is the next calendar day.
Start by clarifying the data model and business rules, then outline a step-by-step SQL solution that handles edge cases like overnight shifts and missing punches. Write clean, modular SQL using CTEs to compute hours per shift, aggregate to pay periods, and apply pay rates.
Pro tip: Mention that you would validate results against a manual sample and discuss how to handle exceptions like missed punches or overtime, showing you think about data quality and real-world payroll complexity.
Ask about the schema: tables for employees, shifts, clock-in/out times, pay rates, and any overtime rules. Confirm how to handle overnight shifts, breaks, and missing punches.
Use TIMESTAMPDIFF or equivalent to compute duration between clock-in and clock-out, adjusting for overnight shifts and subtracting unpaid breaks.
Group by employee and pay period (e.g., weekly, bi-weekly) to sum total hours, applying any overtime multipliers as needed.
Join the aggregated hours with employee pay rates to calculate gross pay, handling overtime and other adjustments.
Check results for anomalies, ensure indexes are used for performance, and consider edge cases like multiple shifts per day or timezone differences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cohort analysis in SQL is one of those things I've read about but rarely write from scratch under pressure.
Start by clarifying the business goal and defining the cohort based on the customer's first purchase or signup month. Then write a SQL query that assigns each customer to a cohort, calculates the time since joining (e.g., months), and aggregates metrics like retention or revenue per cohort period. Use window functions and date functions to handle the time-based analysis.
Pro tip: Mention that cohort analysis is often used to measure product-market fit and retention; showing you understand the 'why' behind the query impresses interviewers. Also, discuss how you'd handle edge cases like customers with no activity after joining.
Ask clarifying questions about the metric (e.g., retention, revenue) and time granularity (monthly, weekly). Define the cohort based on the customer's first interaction date, such as first purchase or signup date.
Use a subquery or CTE to find the minimum date per customer (e.g., first order date) and assign a cohort label, typically the year-month of that date.
For each customer activity, compute the period number (e.g., month difference) between the activity date and the cohort date. This creates the 'cohort period' dimension.
Group by cohort and period, then calculate the desired metric (e.g., count of active customers, sum of revenue). Optionally, pivot the results to show cohorts as rows and periods as columns.
Ensure the query is efficient by filtering early and using appropriate indexes. Explain how the results can be used to inform business decisions, such as identifying drop-off points.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and business definitions (e.g., what constitutes revenue, timezone handling, and whether to include returns). Then write a query that groups by date and store, aggregating revenue, and consider edge cases like missing dates or stores with no sales.
Pro tip: Mention that you would validate the rollup against a known total or sample data to ensure accuracy, and discuss how to handle timezone conversions if stores span multiple timezones.
Ask about the table structure, revenue definition, timezone, and whether to include all stores or only those with sales. Confirm if daily rollups should include days with zero revenue.
Determine which tables contain sales transactions, store information, and timestamps. Identify the revenue column and the store identifier.
Use GROUP BY on the date (truncated to day) and store ID, with SUM for revenue. Consider using a date function to extract the day from the timestamp.
Address timezone conversion, missing dates (using a calendar table or generate_series), and stores with no sales (using LEFT JOIN from a stores table).
Check results against expected totals, and discuss indexing or partitioning strategies for performance on large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about pre-aggregated summary tables for daily/weekly rollups vs.
Start by clarifying the system's purpose and the key stakeholders who will consume the reports. Then propose a layered architecture that separates data collection, storage, and presentation, and discuss how you would prioritize reports based on business value and technical feasibility.
Pro tip: Emphasize the importance of defining clear metrics and SLAs for report freshness and accuracy, and mention how you would handle data privacy and multi-tenancy, which are critical at Salesforce.
Ask questions to understand who will use the reports (e.g., product managers, engineers, executives) and what decisions they need to support. Identify the key metrics and dimensions that matter.
Outline how data will be collected, transformed, and stored. Consider batch vs. streaming, ETL vs. ELT, and the need for a data warehouse or data lake.
Propose a layered architecture: data sources, ingestion, storage, processing, and presentation (e.g., dashboards, APIs). Discuss how to ensure scalability, reliability, and performance.
Suggest a framework for prioritizing reports, such as the RICE score or MoSCoW method, based on business impact and effort. Give examples of high-value reports like usage analytics, performance metrics, and error rates.
Discuss trade-offs between real-time and batch processing, cost vs. performance, and build vs. buy. Mention monitoring, alerting, and data governance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.