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Machine Learning Developer Interview Questions
8,207 machine learning developer interview questions shared by candidates
Q: Tell me about yourself. Q: Why did you quit your last job? Q: What's your salary expectation? (Even they company didn't consider to give me an offer)
I only processed 2 easy algorithm questions so far.
Apart from questions about the past work the main focus in the interviews was about how to solve different bussiness problems with machine learning. I recommend to spend enough time on searching the information about the problems which Booking.com solves using machine learning during a preparation to an interview.
I chose the Quant quizz. It contained probability-related questions (Bayes, Distributions), and mathematical brain teasers.
How would you learn a new challenging skill?
Can you explain the architecture and design decisions of your machine learning solution, including data processing, model selection, deployment strategy, and how you would scale it in a production environment?
1: ¿Cómo diseñarías una arquitectura de MLOps completa para gestionar modelos de machine learning en producción, garantizando escalabilidad, observabilidad y reproducibilidad? R: Expliqué un enfoque basado en Kubernetes, CI/CD, pipelines de MLOps, monitoreo con Prometheus/Grafana, gestión de artefactos y modelos en MLflow, y despliegues controlados con infraestructura como código. P2: ¿Cómo gestionarías la integración de flujos de datos de diferentes equipos dentro de un entorno corporativo de gran escala? R: Describí estrategias de integración mediante API Gateways, control de versiones de datos, almacenamiento distribuido y monitoreo de la calidad del flujo de datos. P3: ¿Cómo garantizarías la seguridad, cumplimiento y eficiencia en entornos híbridos de entrenamiento y producción de modelos? R: Comenté el uso de IAM, redes privadas virtuales, control de acceso granular, auditorías automáticas y prácticas de DevSecOps. P4 (Presentación técnica): P1: ¿Cómo diseñarías una arquitectura de MLOps completa para gestionar modelos de machine learning en producción, garantizando escalabilidad, observabilidad y reproducibilidad? P2: ¿Cómo integrarías flujos de datos de diferentes equipos dentro de un entorno corporativo de gran escala? P3: ¿Qué medidas aplicarías para garantizar seguridad y cumplimiento en entornos híbridos (on-premise y cloud)? P4: Durante la presentación técnica, se pidió exponer un caso real de arquitectura aplicada a entornos de IA, detallando decisiones técnicas, automatización y gestión operativa.
For the programming challenge, I was asked to train an NLP model to predict the product in a customer complaint. The submission was expected to be production-level code (as much as one can do within one week).
1. Based on Resume projects and tools. 2. Basic ds algo questions . 3. Machine Learning Algorithms . Neural Network , Tensorflow .
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