Top 4 Machine learning Interview Questions
Browse 4 real-world technical and behavioral interview questions about Machine learning. Review scenarios, edge cases, and architectural best practices.
A churn model scores 0.94 AUC offline and barely beats the baseline in production. One of its strongest features is populated by a process that runs after the customer has already churned. Explain what happened and how you would fix it.
The feature encodes the outcome, so the model reads the answer rather than predicting it. Offline the label and the feature come from the same snapshot, which is why validation looks excellent; in production the feature is empty at scoring time and the signal vanishes. Fix by defining features by when they were available and validating on a forward time split.
Your fraud model is 99.4% accurate. Is that good?
Almost certainly not: if fraud is 0.6% of transactions, a model predicting 'not fraud' for everything scores 99.4% and catches nothing. The metric has to follow from which error costs more, which makes it a business decision expressed in numbers rather than a modelling one.
How would you architect a defence-in-depth strategy to prevent model inversion and training data extraction attacks on a publicly accessible generative AI service fine-tuned on highly sensitive corporate data?
Exploring robust security architectures to prevent attackers from extracting sensitive training data from generative AI models.
How do you architect a strictly low-latency, real-time pipeline to ingest, embed, and index tens of thousands of unstructured documents per second into a vector database?
Designing a robust streaming architecture for embedding generation, detailing GPU adaptive batching, Kafka-driven backpressure management, and asynchronous HNSW indexing. Use this machine learning answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects data engineering to the point an interviewer is testing.