Top 35
Prep plan
Updated weekly · Last refresh Oct 17

UBS AI Engineer Interview Questions

The questions to prepare for a UBS AI Engineer interview. Questions from real interview reports rank first. Updated daily.

35questions
~6htotal time
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1
CodingStart here. 6 questions · ~63 min
Breadth-First Search Level OrderEasy
Practice
Recently asked

Use breadth-first search to return reachable Delta DIAView assets grouped by distance from a starting asset.

QueuebfsGraphsUBS
Find Two Sum IndicesEasy
Practice
Recently asked

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

Hash TablesArraysSortingUBS
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2
Generative AI & LLMs7 questions · ~73 min
Choose Fine-Tuning vs RAGMedium
Recently asked

Decide when to fine-tune instead of using RAG for a finance-domain assistant under strict latency, cost, and hallucination limits.

RAGLLM EvaluationFine-TuningUBS
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3
Model Evaluation4 questions · ~42 min
Choose a Fraud ThresholdMedium
Recently asked

Decide the right threshold for a fraud detection system using precision, recall, and cost tradeoffs.

PrecisionThreshold TuningRecallUBS
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4
Machine Learning4 questions · ~42 min
Detect Rare Payment FraudMedium
Recently asked

Build an imbalanced binary classifier for payment fraud detection using cost-sensitive learning, threshold tuning, and precision-recall evaluation.

Cross-ValidationFeature EngineeringSupervised LearningUBS
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5
System Design4 questions · ~42 min
Design a Personalized Recommendation RankerHard
Recently asked

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

RetrievalTwo-Tower ModelsRecommendation SystemsUBS
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6
Behavioral & Leadership8 questions · ~84 min
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7
More topics2 questions · ~21 min
Explain Transformer Self-AttentionHard
Recently asked

Explain how transformer self-attention works, including its role in sequence modeling and why it scales better than RNNs.

Neural NetworksLanguage ModelsDeep LearningUBS
Preprocess Noisy Financial TextHard
Recently asked

Design a robust ETL process to normalize, validate, deduplicate, and quality-check noisy unstructured financial text.

Data Qualitydata parsingdata cleaningUBS
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