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Updated weekly · Last refresh Sep 22

Persistent Systems GenAI Engineer Interview Questions

The questions to prepare for a Persistent Systems GenAI Engineer interview. Questions from real interview reports rank first. Updated daily.

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1
Generative AI & LLMsStart here. 3 questions · ~28 min
Compare GANs, VAEs, DiffusionMedium

Compare GANs, VAEs, and diffusion models by training objective, generation behavior, tradeoffs, and practical use cases.

HallucinationLLM EvaluationFine-TuningPersistent Systems
Evaluate an LLM SystemMedium

Explain how to evaluate a generative model using offline and online methods, with attention to hallucination, product metrics, and experiment design.

HallucinationPrompt EngineeringLLM EvaluationPersistent Systems
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2
System Design3 questions · ~28 min
Deploy a Cloud ML Inference SystemMedium

Design a cloud ML deployment system for a security product, covering training, serving, updates, and production monitoring.

InfrastructureFeature DriftModel ServingPersistent Systems
Design a Personalized Recommendation RankerHard

Design a personalized recommendation system that turns user preferences into ranked suggestions with retrieval, ranking, and feedback loops.

RetrievalTwo-Tower ModelsRecommendation SystemsPersistent Systems
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3
Behavioral & Leadership3 questions · ~28 min
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4
More topics5 questions · ~46 min
Preprocessing Data for Model TrainingEasy

Explain a practical preprocessing pipeline for supervised learning, from data cleaning and encoding to validation-ready features.

Hyperparameter TuningCross-ValidationFeature EngineeringPersistent Systems
Approach to Underperforming ModelsMedium

Structured approach for diagnosing an underperforming model and deciding whether to fix data, thresholding, calibration, or the model.

PrecisionAccuracyRecallPersistent Systems
Synthetic Data Generation FunctionMedium
Practice

Generate deterministic weighted samples efficiently using the alias method in O(k + n) time.

RecursionMathArraysPersistent Systems
ML Project ImpactMedium

Tests your ability to deliver ML solutions and communicate measurable business or technical impact.

Cross-ValidationFeature EngineeringSupervised LearningPersistent Systems
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