Accenture Data Analyst Intern Interview Questions and Answers

It was a training+ internship

Q1. NUmber of groups and group wise projects
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Aptitude test was the basics asked in every exam

Q1. What is difference between 4stroke and 2 stroke engine?
Ans. 
4-stroke engines have 4 strokes per cycle, while 2-stroke engines have 2 strokes per cycle.
• 4-stroke engines are more fuel-efficient and produce less pollution than 2-stroke engines.

• 2-stroke engines are simpler and lighter than 4-stroke engines.

• 4-stroke engines have separate intake, compression, power, and exhaust strokes, while 2-stroke engines combine intake and compression, and power and exhaust strokes.

• Examples of 4...read more
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Q2. What is a bevel gear? Give an example
Ans. 
A bevel gear is a type of gear used to transmit power between shafts at an angle.
• Bevel gears have conical shapes and are used to change the direction of rotation.

• They are commonly used in automobiles, airplanes, and power plants.

• An example of a bevel gear is the ring and pinion gear in a car's differential.

• Another example is the bevel gear used in a hand drill to transmit power from the motor to the chuck.
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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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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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The first round was easy which consist of basic aptitude questions including java,c,cpp,dbms,sql,reasoning,etc

Q1. Explain Data Analysis Life Cycle
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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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Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
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Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
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Q1. Salary discussion, project discussion, why change? Why Wipro
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