AI Engineering Interview Questions — Practice Library

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Start here: ML Engineer

Six problems, easy to hard. Work through them in order — each one builds on the last.

0/ 6 done
  1. 1
    ML Engineer vs Data Scientist on a Fraud Detection System
    Easy
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  2. 2
    Feature Store Architecture for Real-Time Fraud Detection
    Easy
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  3. 3
    Elbow Method and Silhouette Score for Optimal Cluster Selection
    EasyMicrosoft
    Pro
  4. 4
    Prevent Re-identification in Anonymized Data Through Differential Privacy
    Medium
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  5. 5
    Handling Class Imbalance and Preventing Overfitting in Machine Learning
    MediumAmazon
    Pro
  6. 6
    Multi-tenant vector database with strict isolation and sub-100ms query latency
    HardGoogle DeepMind
    Pro
2 problems
Problem
Tags
Type
Distributed Training Pipeline for Trillion-Parameter Language Model
Reported at NvidiaML EngineeringHard
System Design
Data quality strategies for large language model training pipelines
Reported at NvidiaML EngineeringMedium
System Design