Top 29
Prep plan
Updated weekly · Last refresh Oct 17

XANT Research Scientist Interview Questions

The questions to prepare for a XANT Research Scientist interview. Questions from real interview reports rank first. Updated daily.

29questions
~5htotal time
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1
CodingStart here. 4 questions · ~41 min
Graph Traversal with BFS and DFSEasy
Practice

Traverse a graph from a start node using BFS and DFS, returning the visit order for each traversal.

StackQueueGraphsXANT
Find Two Sum IndicesEasy
Practice

Use a hash map to find two array elements that sum to a target in O(n) time.

Hash TablesArraysSortingXANT
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2
Generative AI & LLMs3 questions · ~31 min
Prompt for Reliable Field ExtractionMedium

Design a prompt for structured extraction that improves schema adherence, reduces invented values, and is easy to evaluate.

HallucinationStructured ExtractionPrompt EngineeringXANT
Defend a RAG Assistant from InjectionHard

Design a document-grounded LLM assistant resilient to prompt injection, with strict safety, latency, and cost constraints.

Prompt EngineeringPrompt InjectionRAGXANT
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3
Model Evaluation3 questions · ~31 min
Evaluate Overfitting vs UnderfittingMedium

Explain how to tell whether a model is overfitting or underfitting using train versus validation performance and related checks.

Cross-ValidationBias-Variance TradeoffAccuracyXANT
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4
System Design4 questions · ~41 min
Design Feature Drift Monitoring SystemHard

Design a production ranking system with robust feature drift monitoring across batch and real-time features at high QPS.

Feature StoreFeature DriftModel ServingXANT
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5
Statistics & Probability3 questions · ~31 min
Testing Sales Algorithm LiftMedium

Tests your experimental design and statistical evaluation of algorithmic improvements.

Statistical SignificancePower AnalysisA/B TestingXANT
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6
Behavioral & Leadership10 questions · ~103 min
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7
More topics2 questions · ~21 min
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffXANT
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