Katkat

Company

Cresta interview questions

10 practice questions in the style of Cresta's interviews, covering Software Engineering Fundamentals, Coding & Algorithms, ML System Design, Machine Learning, System Design, Behavioral & Leadership.

Questions10

1 easy9 medium0 hard

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Cresta interview questions by the numbers

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Katkat has 10 Cresta interview questions. The largest topics are Software Engineering Fundamentals (40%), Coding & Algorithms (20%) and Behavioral & Leadership (10%). By difficulty: 10% easy and 90% medium. The most common round is Online Assessment (40%). Frequent concepts include Data Structures, Hashing, Object-Oriented Design and Software Architecture.

Difficulty

  • Easy10% · 1
  • Medium90% · 9

Interview rounds

  • Online Assessment40% · 4
  • Onsite30% · 3
  • Technical Screen30% · 3

Roles

  • Software Engineer100% · 10

Frequent concepts

  • Data Structures10% · 1
  • Hashing10% · 1
  • Object-Oriented Design10% · 1
  • Software Architecture10% · 1

Showing 1–10 of 10 results

  • An AI-assisted coding round: turn a Python starter script into a terminal chat agent that uses LLM tool calling to search flights, save passenger details and book a flight through a three-endpoint REST API. It tests tool schemas, a multi-step tool loop, ID handling, booking safeguards and error handling.

    CrestaSoftware EngineerSoftware Engineering FundamentalsOnsite
  • Simulate wind gusts over a grid in which every cell holds a number of leaves: each gust shifts all leaves one cell in its direction, and leaves pushed off the edge are lost for good. Return how many leaves remain, testing careful two-dimensional state updates and boundary handling under time pressure.

    CrestaSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Implement an in-memory feature flag evaluation engine that evaluates flags against user attributes using rule predicates, deterministic percentage rollouts, and a true-wins resolution policy.

    CrestaSoftware EngineerSoftware Engineering FundamentalsOnsite
  • An ML system design question about a platform that lets teams evaluate AI agents and measure how a change of system prompt or model affects quality, tool use and response speed. It tests evaluation design, scoring with references and LLM judges, statistical comparison of versions and scalable execution.

    CrestaSoftware EngineerML System DesignOnsite
  • This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design sequence decoding with greedy and beam search states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    CrestaSoftware EngineerMachine LearningTechnical Screen
  • A coding exercise to implement Executor and JobDispatcher classes that send each integer job ID to the executor with the fewest pending jobs, breaking ties round-robin, while executors join and leave. It tests precise tie-break semantics, consistent load tracking, reassigning a removed executor's queued jobs, and edge cases.

    CrestaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Given two strings, return the length of the longest suffix of the first string that is also a prefix of the second. The problem tests precise string indexing, the direction of the overlap, and boundary cases such as no overlap at all or an overlap covering the whole shorter string.

    CrestaSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Explain how to take a successful proof of concept into production, including the engineering and operational gaps that must be closed.

    CrestaSoftware EngineerSystem DesignTechnical Screen
  • A recorded behavioral item from a timed online assessment for a forward-deployed AI engineering role: walk through an AI project you built and explain exactly which parts you owned. It tests concise technical storytelling, clear ownership, evaluation and outcomes without a live interviewer to prompt you.

    CrestaSoftware EngineerBehavioral & LeadershipOnline Assessment
  • Write a Python function that searches a directory and all of its subdirectories for a file matching a requirement and returns the file's absolute path. It tests practical use of os and pathlib, deterministic handling of multiple or missing matches, relative roots, symbolic links and unreadable directories.

    CrestaSoftware EngineerSoftware Engineering FundamentalsOnline Assessment

Cresta interview experiences

All 5 →
  • Five Timed OA Parts for the FDE RoleCresta · Software Engineer · Online Assessment · Oct 2026

    I applied for the Cresta Forward Deployed Engineer (FDE) role, on the AI Agent side. I just finished the OA, and it is very different from the usual pure-algorithm OA. Overall it leans toward practical coding, understanding requirements, and real engineering ability. Sharing the question types for reference. The OA has 5 parts in total, each one timed separately, with these limits: 10 / 30 / 20 /…

  • AI-Assisted Coding Round: Build a Flight-Booking Chat Agent in PythonCresta · Software Engineer · Onsite · Sep 2026

    You get an api.md doc plus an agent_starter.py file, and you have to use AI assistance to implement an AI Agent that searches for and books flights. The format is a chat dialog. Note: the language has to be Python, and you need to set up the whole project in your own IDE, so make sure you can actually run Python locally first. They'll also give you OPENAI_API_KEY, OPENAI_BASE_URL, and FLIGHT_API_…

  • Phone Screen Building a Least-Load Job DispatcherCresta · Software Engineer · Technical Screen · Sep 2026

    Write a Job Dispatcher-Executor system. You are tasked with implementing a simple job dispatcher that distributes incoming jobs to a pool of executors (i.e., workers). Each job is represented as an integer (its ID), and executors are dynamically added and removed during runtime. You must define two classes: Executor – Represents a worker that can accept jobs. LoadBalancer – Maintains a list of ex…

  • System Design Round: Build an Eval Platform for AI AgentsCresta · Software Engineer · Onsite · Sep 2026

    System design round They have you design an eval system to evaluate the results of an Agent using different models, e.g. tool usage, response speed, plus testing different system prompts, that kind of thing. The original question is below: > Design a system for evaluating AI agents. Users of the system are going to be teams developing AI agents, e.g. agents which automatically analyze contact cen…

  • A Feature-Flag Evaluation Engine, Then Rejected After a ‘Great Chat’Cresta · Software Engineer · Onsite · Jul 2026
    Rejected

    I used Claude to help organize this write-up. The interviewer kept saying he really enjoyed our conversation, and then rejected me anyway. Problem background: you're given three JSON files and asked to implement a feature-flag evaluation engine that maps every user to a final boolean value (on/off) for every flag. Input files: flags.json — definitions for 7 flags user.json — 10,000 users expected…

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