Capgemini Associate Data Analyst Interview Questions, Process, and Tips

Q1. Tell about yourself and
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Q1. Resume screening Self introduction Roles and responsibilities
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Given task Statics standard deviations Attrition Average of given table values and Given graph economi graph and poverty graph base on that need to gave answers 30 qustion and 60 min time duration

Q1. Versant test should be need to attend Hirepro will have interview prosess in YouTube will check and prepare mid level hard
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Q2. We can goo for online prosess how it is
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Q1. Manger Level round they will ask KPI NPS AHT standard deviations Service Level handling Root cause Analysis Weighted average Same product how will calculate Totall Swer % how will get Many of like bpi <20 ...read more
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Q1. Salary discussion and update structure of our role and responsibilities
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Q1. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Parameters of Decision Tree
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Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
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Q3. Explain any one of your project in detail
Ans. 
Developed a predictive model to forecast customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to the company to reduce customer churn rate
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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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*****, arjumpudi satyanarayana

Q1. What is the python language
Ans. 
Python is a high-level programming language known for its simplicity and readability.
• Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

• It emphasizes code readability and uses indentation for block delimiters.

• Python has a large standard library and a vibrant community of developers.

• Example: print('Hello, World!')

• Example: import pandas as pd
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Q2. What is the code problems
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Code problems refer to issues or errors in the code that need to be identified and fixed.
• Code problems can include syntax errors, logical errors, or performance issues.

• Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

• Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
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Q3. What is the python code
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Python code is a programming language used for data analysis, machine learning, and scientific computing.
• Python code is written in a text editor or an integrated development environment (IDE)

• Python code is executed using a Python interpreter

• Python code can be used for data manipulation, visualization, and modeling
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Q4. What is the project
Ans. 
The project is a machine learning model to predict customer churn for a telecommunications company.
• Developing predictive models using machine learning algorithms

• Analyzing customer data to identify patterns and trends

• Evaluating model performance and making recommendations for reducing customer churn
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Q5. What is the lnderssip
Ans. 
The question seems to be incomplete or misspelled.
• It is possible that the interviewer made a mistake while asking the question.

• Ask for clarification or context to provide a relevant answer.
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Q1. Gave one easy question and asked what will be the output
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Q2. Leetcode 2 sum question
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
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Q2. Parametric vs non parametruc model
Ans. 
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
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