Macquarie Group Interview Questions and Answers for Freshers

Q1. SQL Case Study Questions
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Q2. Python data types and OOPS Questions
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3-4 sections, containing pattern recognition, math and genaral aptitude questions. A mock test link is also given before attempting the real test

Q1. Behavioural and Team fit questions
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Q1. Define your dataset and what difficulties you have faced while preparing your model ?
Ans. 
The dataset consists of customer purchase history and demographic information. Difficulties faced include data cleaning and missing values.
• Dataset includes customer ID, purchase amount, purchase date, age, gender, and location.

• Difficulties faced include handling missing values in the age and location columns.

• Data cleaning involved removing duplicates and outliers to ensure accurate analysis.

• Normalization and standardiz
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Q2. What is Normalization and standardization
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Normalization and standardization are techniques used to rescale data to have a mean of 0 and a standard deviation of 1.
• Normalization is the process of rescaling the data to have values between 0 and 1.

• Standardization is the process of rescaling the data to have a mean of 0 and a standard deviation of 1.

• Normalization is useful when the features have different ranges.

• Standardization is useful when the features have diff...read more
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Q1. Explain past project
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Q1. Questions regarding CV, previous work in PORs, college socities, interest in this role, why Macquarie were asked
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Q2. How would you improve upon survey data collection methods
Ans. 
Implementing online surveys, utilizing incentives, improving survey design, and utilizing technology for data collection.
• Implement online surveys to reach a larger audience and collect data more efficiently.

• Offer incentives such as gift cards or discounts to increase survey participation rates.

• Improve survey design by using clear and concise questions, avoiding leading questions, and providing response options that cov...read more
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Q1. Swaps, Call option, Financial Instruments
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