Capgemini Ai Ml Engineer Interview Questions and Answers

Discussion on Gradient, SGD, K Mean ++, Silhouette Score, How to Handle High Variation Data,
Coding asked to code KNN, Hyper-Parameter Tuning, Two Difficult Questions on Coding...DSA Based Stumped on Those.

Verdict... Not Selected

Simple Coding No Chat GPT Support Should Be There

Q1. Ask me on project
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Q2. Aws, sql, python, flask ask me
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Basic math and ml and dl questions

Simple project demonstration

Manager discussion and project explanation

Q1. Explain the ML project you recently worked?
Ans. 
Developed a recommendation system for an e-commerce platform using collaborative filtering
• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves
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Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
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Q3. Project specific questions
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Q4. Easy-medium coding questions
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Q1. What technologies you are working on?
Ans. 
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
• Python programming language

• TensorFlow framework

• scikit-learn library
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Q2. How you will approach on machine learning problem?
Ans. 
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (clas...read more
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Q1. Steps of pre-processing
Ans. 
Pre-processing steps involve cleaning, transforming, and preparing data for machine learning models.
• Data cleaning: removing missing values, duplicates, and outliers

• Data transformation: scaling, encoding categorical variables, and feature engineering

• Data normalization: ensuring all features have the same scale

• Data splitting: dividing data into training and testing sets
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Q2. What is lemmatization ?
Ans. 
Lemmatization is the process of reducing words to their base or root form.
• Lemmatization helps in standardizing words for analysis.

• It reduces inflected words to their base form.

• For example, 'running' becomes 'run' after lemmatization.
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Q1. Question on NLP and Coding test
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Q2. Maximum coding question from list and Regex
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Q1. Project based question
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30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

Q1. Explain Oops concepts
Ans. 
Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.
• Inheritance: Allows a class to inherit properties and behavior from another class.

• Encapsulation: Bundling data and methods that operate on the data into a single unit.

• Polymorphism: Ability to present the same interface for different data types.

• Abstraction: Hiding the complex implementation det
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Q2. Explain file handling
Ans. 
File handling refers to the process of managing and manipulating files on a computer system.
• File handling involves tasks such as creating, reading, writing, updating, and deleting files.

• Common file operations include opening a file, reading its contents, writing data to it, and closing the file.

• File handling in programming languages often involves using functions or libraries specifically designed for file operations.

• E...read more
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Q3. Explain supervised and unsupervised learning algorithms of your choice.
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.
• Supervised learning requires input-output pairs for training

• Examples include linear regression, support vector machines, and neural networks

• Unsupervised learning clusters data based on similarities or patterns

• Examples include k-means clustering, hierarchical clustering, and principal component analysis
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Q4. Coding question on pandas which had 10 followup questions
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Q1. Simple discussion on compensation
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Q1. Q. explain your project Q. Project-related questions asked basic to hard Q. What is backpropagation in deep learning? Q. What is the Hugging Face module and working Q. Object detection YOLO NAS Vs YOLO 8
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