Problem Solving and Analytical Thinking Questions

Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.

HardTechnical

0 practiced

Implement an algorithm in Python to find the longest substring of a given string that contains at most k distinct characters. Aim for O(n) time using a sliding-window approach. Provide code and explain the sliding-window invariants and memory usage when handling Unicode characters.

EasyTechnical

0 practiced

You're assigned a bug report: 'Feature X intermittently returns 500 for some users.' As a software engineer, list the clarifying questions you would ask, describe how you'd reproduce the issue reliably or gather evidence if not reproducible, and show how you'd decompose the investigation into concrete steps (logs, metrics, tracing, recent deploys, config diffs). Note what assumptions you'd record before starting.

HardTechnical

0 practiced

You run dozens of experiments simultaneously across many metrics. Explain the multiple comparisons problem and describe methods to control false positives: Bonferroni, Benjamini-Hochberg (FDR), hierarchical testing, and practical trade-offs in product experimentation pipelines.

EasyBehavioral

0 practiced

Tell me about a time you used a repeatable framework (for example, 5-Whys, hypothesis-driven debugging, or scientific method) to solve an ambiguous technical problem. Use the STAR method: Situation, Task, Actions, Results. Also describe any process changes you introduced afterwards to prevent recurrence.

HardTechnical

0 practiced

Given a resource allocation graph with processes and resources, describe an algorithm to detect potential deadlocks (e.g., build a wait-for graph and detect cycles). Provide pseudocode, complexity analysis, and discuss runtime mitigation strategies such as timeouts, resource ordering, and preemption in a production system.

Unlock Full Question Bank

Get access to hundreds of Problem Solving and Analytical Thinking interview questions and detailed answers.

Join thousands of developers preparing for their dream job.