Accenture Jr. Data Scientist Interview Questions and Answers for Freshers

Q1. What is L1 and L2 Regularization?
Ans. 
L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding penalty terms to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term to the cost function.

• L2 regularization adds the squared values of the coefficients as penalty term to the cost function.

• L1 regularization can lead to sparse models by forcing some coefficients to be exactly zer...read more
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Debugging,data analysis, coding,

Data analysis, debugging,coding

Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Q1. What are hyperparameters in random forest
Ans. 
Hyperparameters in random forest are parameters that are set before the learning process begins.
• Hyperparameters control the behavior of the random forest algorithm.

• They are set by the data scientist and are not learned from the data.

• Examples of hyperparameters in random forest include the number of trees, the maximum depth of trees, and the number of features considered at each split.
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Q2. How to do QnA system with LLM
Ans. 
A QnA system with LLM is a system that uses the Language Model for Information Retrieval and Question Answering.
• Preprocess the input question and convert it into a format suitable for the LLM model.

• Fine-tune the LLM model on a dataset of question-answer pairs.

• Use the fine-tuned model to generate answers for new questions.

• Evaluate the performance of the QnA system using metrics like precision, recall, and F1 score.

• Itera...read more
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Q3. How to do unit testing
Ans. 
Unit testing is a process of testing individual units of code to ensure they function correctly.
• Write test cases for each unit of code

• Test inputs, outputs, and edge cases

• Use testing frameworks like JUnit or pytest

• Automate tests to run regularly

• Ensure tests are independent, isolated, and repeatable
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Q1. Question on based resume points
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Q2. 2 to 3 codes
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Q3. Project explanation
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Q1. Deep question and answering to know candidature behavior based on Resume
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Q2. Coding in python
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Q3. Project explanation
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Q1. Scenario round, knowing how you think at particular situation
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Just prepared all the tricks before attempting aptitude so that exam gets solved quicker.

Q1. They asked me about my Background and what all projects I’ve worked on
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Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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