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Technical Interview Guide

This guide was created to help you prepare for QuintoAndar's technical interviews, with detailed tips, study suggestions, examples, and free resources.

# Machine Learning

Here you find the tips and guidelines we have prepared for the technical interviews in the Machine Learning area.

Choose where to start or review everything at your own pace. Happy reading and happy preparing!

Tech Screening

An interview is a two-way street. We don’t expect perfect answers, but clarity of thought, good choices, and a constructive attitude. 🚀 Here are some valuable tips to help you do well: 

  • What we expect:

    In this first conversation, we will discuss your experience and some machine learning concepts. The idea is to use a simplified case study to understand how you think. If we do well here, you advance to the next stages!

      How to Prepare – Tech Screening

      • Portfolio: Be ready to detail your professional background, focusing especially on Machine Learning and Data projects, including technical details if relevant.

      • Business Problem Solving: Practice translating business needs into technical solutions. When addressing a problem, ask clear questions and structure your hypotheses logically.

      • Data and Modeling Discussion: Reflect on the useful data types for solving business problems and how you would approach modeling, for both structured and unstructured data.

      • Technical Challenges: Consider common challenges in ML and AI projects, such as the MLOps cycle, context engineering, search, and recommendation.

      📌  Checklist – Tech Screening

      Quer garantir que não esqueceu de nada? Use essa checklist para guiar sua abordagem:

      ✅ I am ready to detail my professional background and ML/Data projects.
      ✅ I practiced translating business needs into technical solutions.
      ✅ I reflected on data types and modeling approaches (structured and unstructured) for business problems.
      ✅ I studied technical challenges in ML and AI, such as MLOps, context engineering, search, and recommendation.

Coding

In this interview, you will need to use algorithms and SQL to solve one or more programming problems within a Machine Learning context. This is the moment to showcase your coding skills in practice!

Important: The stage will be conducted on Codility, and it only supports the languages described in this topic. Understand that choosing another language will remove access to certain features, such as syntax highlighting, code completion, and access to a runtime environment, which may affect your performance.

  • ✅ Best practices in the interview:

    • Read and validate the problem with the interviewer.
    • Take a moment to understand the problem and outline your approach (planning).
    • Start with a basic solution and optimize later.
    • Test your code with different inputs.
    • Name variables clearly.
    • Ask for a minute to think if you need it — this is completely common and necessary!

      📚 How to Prepare – Coding:

      The goal is to assess your ability to apply logic and programming to build the foundation of a data project.

      Programming Languages: Be comfortable with at least one programming language of your choice. It’s important to know Object-Oriented Design and how to test your code.

      Data Structures: Familiarize yourself with arrays (lists), linked lists, stacks, queues, sets, maps (dictionaries), trees, and graphs.

      Complexity: Demonstrate that you can reason about the time and space complexity of an algorithm and how to improve it. It is essential to know Big-O notation ($O(n)$).

      Sorting and Recursion: Review common sorting algorithms, such as insertion sort, quicksort, and mergesort. Many problems also require recursion, be prepared to use it.Correctness: You will use an online IDE with basic features, such as code completion and syntax highlighting, as well as the ability to run and test your code. Take advantage of these resources.

      📌 Checklist – Coding:

      To help you prepare with more confidence, we’ve put together a checklist with everything worth reviewing before the interview. Mark the items as you progress through your studies and practice:

      ✅ I understand and can apply loop logic, conditionals, and recursion.
      ✅ I know how to structure my code with good practices (readability, modularity).
      ✅ I master JOINs and Window Functions in SQL.
      ✅ I tested my code with different cases (valid and edge cases).
      ✅ I have Machine Learning projects to discuss in depth.
      ✅ I can focus on solving the problem, and not on a specific method.
      ✅ I can demonstrate my thought process and my line of reasoning.
      ✅ I can start with a simple solution and then optimize it.

Machine Learning Interview

In this interview, we will dive into topics related to Machine Learning, Deep Learning, and GenAI. Be prepared to answer technical questions and solve a business case without coding.

  • What We Expect

    • Ability to Structure and Communicate (Break Down the Problem);
    • Critical Analysis and Component Choice (Trade-Offs);
    • Estimation and Sizing.

      📚 How to Prepare – Machine Learning Interview

      Being sharp on the suggestions below can help you succeed in our selection process. Important Concepts:

      Problem Decomposition: The fundamental skill of breaking down a large, complex problem into smaller, more manageable modules or components. It is the first step in dealing with the open-ended nature of design questions. The candidate should guide the interviewer by asking questions to define the scope and requirements (e.g., How many users? What is the data volume? What is the fault tolerance?).

      Trade-Off Analysis: There is no perfect solution. This is the concept of justifying a technological choice by comparing its pros and cons relative to other options. For example: “Using a NoSQL database (like Cassandra) is good for horizontal scalability, but it implies a trade-off in data consistency (sacrificing ACID for BASE).” The candidate must demonstrate an understanding of the implications of their choices.

      Scaling and Dimensioning Thinking (Scalability & Dimensioning): The ability to quantify system requirements to ensure it can handle the expected load (millions of users, RPS Peaks – Requests Per Second). This concept involves applying Quick Calculations (Napkin Math) to estimate storage volume, throughput, and the number of necessary servers. It is proof that the candidate thinks beyond the prototype and considers the real world.

      Iterative and Pragmatic Design (Iterative Design): The approach of starting with the simplest, most functional solution (the Minimal Viable Architectural Product) and improving it in stages, adding complexity only when necessary (e.g., introducing a cache after identifying a latency bottleneck). The candidate demonstrates technical maturity by avoiding Over-Engineering, focusing on initial simplicity and effectiveness.

      Knowledge of Fundamentals (Fundamentals & Data Structures): Although the focus is not on the code itself, the candidate is expected to understand how the underlying data structures and algorithms function in the system components. For example, knowing why a hash table is ideal for caching or how a B-Tree works in a database. This point shows a solid foundation in Computer Science.