Top 34
Prep plan
Updated weekly · Last refresh Aug 30

Capgemini Engineering AI Engineer Interview Questions

The questions to prepare for a Capgemini Engineering AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

34questions
~6htotal time
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1
CodingStart here. 10 questions · ~97 min
Reverse a Linked ListEasy
Practice

Reverse a singly linked list in place using pointer manipulation.

RecursionLinked ListsCapgemini Engineering
Basic Linear Regression FunctionEasy
Practice

Implement ordinary least squares to fit a line and predict values for new inputs.

RegressionMathArraysCapgemini Engineering
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2
Machine Learning5 questions · ~49 min
Handling Missing Values in MLEasy

Explain practical strategies for handling missing values in a supervised learning workflow, from diagnosis to modeling and validation.

Cross-ValidationFeature EngineeringRegularizationCapgemini Engineering
Supervised vs Unsupervised LearningEasy

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

Unsupervised LearningFeature EngineeringBias-Variance TradeoffCapgemini Engineering
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3
System Design3 questions · ~29 min
Design an LLM Serving PlatformHard

Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.

Cold StartFeature StoreModel ServingCapgemini Engineering
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4
Model Evaluation3 questions · ~29 min
Choose Classification MetricsMedium

Choose the right classification metrics, and explain when precision, recall, and F1 score matter most.

F1 ScorePrecisionRecallCapgemini Engineering
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5
Generative AI & LLMs4 questions · ~39 min
Explain Vector Search in RAGMedium

Design a grounded document Q&A system and explain how vector search improves retrieval quality, latency, and hallucination control in RAG.

Vector SearchPrompt EngineeringRAGCapgemini Engineering
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6
Behavioral & Leadership8 questions · ~78 min
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7
More topics1 question · ~10 min
Choose NLP Algorithms by TaskMedium

Explain how to choose practical NLP algorithms across tokenization, TF-IDF, embeddings, and text classification tasks.

Language ModelsTF-IDFTokenizationCapgemini Engineering
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