Systems design
26 bites tagged Systems design: interview questions with model answers, and 60-second explainers.
Find a novel value proposition from a recent technology breakthrough
Tests translating a technical breakthrough into product strategy: name an underserved market, quantify economic value, map a minimal build. Pick one tech, define pricing power, list 3-4 parts. Red flag: solution seeking a problem or feature lists sans value.
Architectural challenges for deploying ML models on resource-constrained edge devices
Tests Edge MLOps architecture under severe constraints. Strong answers hit quantization and delta OTA updates for flaky networks, power-aware scheduling, and closed-loop drift detection.
Describe RICE scoring and architect data pipelines for Reach and Effort
Define RICE; automate Reach via event streams with time windows; automate Effort from PM tool estimates with calibration.
Implement CDC from an OLTP database to a data warehouse
This tests your grasp of production system trade-offs. A good answer compares log-based and trigger-based CDC, focusing on source impact and data fidelity, then recommends log-based for its low overhead.
Implement an A/B test for a new checkout flow
Tests your grasp of the full A/B testing lifecycle. A great answer outlines a feature flag system, consistent user bucketing via hashing a stable user ID, and an analytics query grouping by variant. A red flag is suggesting simple client-side randomization.
Design a Column-Level Data Lineage System at Scale
Tests your ability to design a metadata system with three distinct components. A strong answer outlines collection (e.g., OpenLineage), storage in a graph database (e.g., Neo4j), and visualization for impact analysis.
Describe tracking a user event from frontend to BI tool
Tests your grasp of the modern data stack. A great answer outlines the five stages: frontend emission, ingestion, loading into a warehouse, transformation, and BI visualization. A red flag is describing only the frontend code and ignoring the data pipeline.
Balancing Emergent Design with Long-Term Architectural Vision
This tests your ability to balance agile practice with large-scale system needs. Explain how an "Architectural Runway," built with "Enablers," reconciles emergent design with intentional architecture for near-term features.
SARIMA vs. LightGBM for Forecasting with External Variables
Tests your grasp of practical trade-offs in model selection. A strong answer contrasts SARIMA's interpretability with LightGBM's power for handling many non-linear variables, covering performance and implementation costs.
Prove API Latency Affects User Engagement
This tests your ability to design a controlled experiment for a backend attribute. A great answer outlines an A/B test that artificially adds latency for a treatment group, details the necessary logging with shared IDs, and explains how to join and analyze…
Average latency is up, but p99 is flat. Why?
This tests your grasp of latency distributions. Hypothesize that a large group of typical requests slowed, pulling up the average but not crossing the p99 threshold. Segment by endpoint or customer to find the cohort.
How do you create a 'golden record' for customers?
Tests your grasp of data governance and systems thinking. A strong answer defines master data, outlines a phased approach (discovery, rule-setting, implementation), and covers ongoing stewardship.
Explain data lineage and how you'd implement it
Tests your practical data governance knowledge. Define lineage (origin, transforms, movement), then outline a solution: metadata collection from services, a central store (graph DB), and a visualization tool (Amundsen/OpenLineage).
Technical Prerequisites for LeSS Feature Teams
This tests your grasp of the engineering practices that enable agile scaling. A great answer covers continuous integration for shared ownership, robust test automation, and a loosely coupled architecture. A red flag is focusing only on Scrum ceremonies.
How would you A/B test a redesigned dashboard?
This tests your ability to translate a product goal into a technical plan. A good answer defines "engagement" with metrics, outlines the bucketing and instrumentation strategy, and discusses statistical significance.
Trunk-Based Development vs. GitFlow for High-Frequency Releases
This tests your grasp of modern CI/CD trade-offs. A great answer explains how TBD's frequent merges to main, decoupled from release by feature flags, enable velocity. Then, contrast this with GitFlow's complexity.
Capacity Planning: Don't Run Out of Room
Capacity planning matches your system's resources to user demand, crucial for handling traffic spikes or budgeting cloud spend. The main footgun is planning for theoretical 'design capacity' instead of realistic 'effective capacity' which accounts for…
FMEA: Systematically Mapping What Could Go Wrong
FMEA is a structured pre-mortem for system components, cataloging potential failures, their causes, and their effects. It's used in system design to proactively identify risks before they become outages. The footgun is treating it as a one-time task.
Failover: Automatic Recovery When Things Go Wrong
Failover is your system's automatic plan B. When a primary component fails, a standby takes over instantly without human intervention. It's essential for high-availability systems. The footgun is assuming the standby is perfectly in sync; it might not be.
Queueing Theory: The Math of Waiting Lines
Queueing theory is the math of waiting lines, helping you predict system performance under load. It's used for capacity planning and setting autoscaling rules.
Configuration Management: Enforcing Desired State
Configuration Management treats your system's setup as code to prevent "configuration drift." It's used to reliably provision servers, deploy apps, and manage fleets, ensuring every component matches its intended design and is reproducible.
MLOps: When to Build vs. Buy Your Infrastructure
Deciding to build or buy MLOps tools hinges on whether it creates a competitive advantage. For commodity tasks like experiment tracking, buying a managed service avoids locking up engineers.
Multi-Agent Systems: A Team of AIs, Not a Monolith
Instead of one giant AI, a Multi-Agent System is a team of specialized AIs that collaborate on a complex problem. This appears in LLM-powered coding assistants and complex simulations.
Stack vs. Heap: Where Go Puts Your Data
The stack is a fast, last-in-first-out region for local, fixed-size data. The heap is slower, flexible memory for dynamic data or values that escape a function's scope.
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