Adobe Data Scientist Interview Questions and Answers

Q1. Basic DS question like how to handle missing features
Ans. 
Handling missing features involves techniques like imputation, deletion, and using algorithms that support missing values.
• Imputation: Replace missing values with mean, median, or mode. Example: If age is missing, use the average age of the dataset.

• Deletion: Remove rows or columns with missing values. Example: Drop a column if more than 50% of its values are missing.

• Using algorithms: Some algorithms like XGBoost can han...read more
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Q1. Case study based questions
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2 coding questions - one on binary tree and list operations.

Q1. Xgboost explanation
Ans. 
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.

• It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.

• Xgboost is known for its ability to handle missing data, regularization techniques, and parall...read more
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Q2. Data structures question compulsory
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Based on topological sort

Q1. Ml baiscs like LR, SVM
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Q1. Ml design questions were asked
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Q1. Why do you want to join
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Q1. Why you are interested in working for clipchamp at Microsoft/
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Q2. How your career goals align with this role?
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Q1. Explain Decision Trees
Ans. 
Decision Trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision Trees can handle both numerical and c...read more
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Q2. How will you finetune LLMs
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Q1. Basic ml related questions,past project discussion
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Q1. AI, ML related, statistical analysis of the graph.
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Q1. Work culture, check
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Q1. Work culture, project discussion, scenario based questions
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Q1. Basic HR questions, why sap salary discussions
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Detailed project discussion and 1 dsa

Q1. Common ml questions
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