CRM Systems & Sales Technology Basics Questions

Practical familiarity with at least one CRM platform (preferably Salesforce given its FAANG prevalence), basic understanding of CRM data structure, record management, and how CRM data feeds into reporting and analysis. Awareness of other common sales tools in the tech stack (Slack, email platforms, automation tools).

HardTechnical

35 practiced

You need to implement Master Data Management (MDM) for Accounts across Salesforce, ERP, and a support system. Propose a technical and governance approach that covers golden record strategy, synchronization methods (push/pull/CDC), conflict-resolution rules, audit trails, and roles/responsibilities for ongoing stewardship.

EasyTechnical

55 practiced

Provide two real-world examples where implementing Salesforce Flow (or equivalent CRM automation) meaningfully replaces a manual process. For each example, describe the trigger, key actions, entry criteria, and the measurable business outcome you expect (time saved, error reduction, throughput).

HardSystem Design

42 practiced

Design a revenue attribution solution that assigns credit across multi-touch marketing interactions using CRM, a marketing automation platform, and a data warehouse. Describe the attribution models you might support (first-touch, last-touch, linear, weighted multi-touch), the data schema to capture interactions, ETL cadence, and trade-offs between accuracy, explainability, and operational complexity.

HardTechnical

38 practiced

Revenue reported in CRM and in the billing system differs by $200k for the quarter. Outline a detailed reconciliation and investigation plan: what queries and exports to run, key records/timestamps to compare (booked date vs invoice date), likely root causes (timing, mapping, FX, discounts), and how to resolve and prevent recurrence.

HardTechnical

38 practiced

Given large warehouse tables:

opportunities(id, account_id, stage, probability, amount, close_date)

Describe a performant SQL approach (or pseudocode) to compute monthly 'weighted pipeline' for the next 12 months, defined as SUM(amount * probability) grouped by month. Discuss partitioning, filtering, use of materialized views, and incremental computation for millions of rows.

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