Aristo Pharmaceuticals Executive EDP Interview Questions and Answers

Maths English excel shortcuts reasoning basic finance

Q1. Job role study. About it
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It was related to maths and aptitude. If you have an idea of basic maths then, it will be easy for you to attempt.

Latest and trending topics.

Q1. The questions were quiet simple and related to your goals and company-related.
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Q2. Q1 Introduction. Q2 Your future goals? Q3 Where do you find yourself after 5-7 years? Q4 Company's culture related. Q5 Projects, skills, and tools used. Q6 Why Morningstar?
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Q1. Normal questions
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60 question - 60 minutes all MCQ.
1. general equity and Accounting questions
2. aptitude
3. English test

Q1. 1. merger and acquisition 2. swops 3. other general questions
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Q1. Face value, book value, market value profit and loss etc. NON domain questions - how do you deal with monotonous work why shall we hire you?
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Q1. Tell me about yourself.
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Q2. What is your background
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The first round for MDP Associate role is General Aptitude Test which is based on English usage, numerical reasoning and logical reasoning

Q1. Technical Interview questions are related to financial and leadership ship qualities
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Q2. HR/Manager Interview questions are related to task solving and other normal questions
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Create a business requirement document

Q1. What are the strengths and weakness
Ans. 
Strengths include strong analytical skills and attention to detail. Weaknesses may include difficulty with public speaking and time management.
• Strengths: strong analytical skills

• Strengths: attention to detail

• Weaknesses: difficulty with public speaking

• Weaknesses: time management
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Q1. Explain random forest
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Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q2. What is boosting
Ans. 
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.
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