Baker Hughes Reporting Analyst Interview Questions and Answers

Q1. Conflict management questions
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Q2. Team work and attitude
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Q1. Stress strain curves
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Q2. Asme codes for oil and gas
Ans. 
ASME codes are widely used in the oil and gas industry to ensure safety and quality standards are met.
• ASME B31.3 is the code for process piping in oil and gas facilities

• ASME Section VIII is used for pressure vessels in oil and gas industry

• ASME B31.4 is for liquid pipelines in oil and gas industry
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Q1. Self introduction and about previous job experiences
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Q1. All related to desktop and power BI
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If everyone is cheating you should also do it?

Q1. Everyhting about project..
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Q2. Linear vs logistic regression
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Linear regression is used for continuous variables, while logistic regression is used for binary outcomes.
• Linear regression is used to predict a continuous outcome based on one or more input variables.

• Logistic regression is used to predict the probability of a binary outcome based on one or more input variables.

• Linear regression assumes a linear relationship between the independent and dependent variables, while logist...read more
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Q3. Normalization in dbms
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Normalization in DBMS is the process of organizing data in a database to reduce redundancy and improve data integrity.
• Normalization involves breaking down data into smaller, more manageable tables and establishing relationships between them.

• It helps in reducing data redundancy, minimizing data anomalies, and improving data consistency.

• There are different normal forms such as 1NF, 2NF, 3NF, BCNF, and 4NF, each with spec...read more
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Q4. List array difference etc
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Array difference is finding the elements that are present in one array but not in another.
• Use set operations like difference to find elements in one array but not in another.

• Example: arr1 = ['a', 'b', 'c'], arr2 = ['b', 'c', 'd'], arr_diff = set(arr1) - set(arr2) = ['a']
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Aptitude test on various functions of an organization

Q1. Level 1 technical
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Q2. Questions about Supply Chain, Logistics, ERP
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Q1. Level 2 technical
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Q1. Standard HR round with HR
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Q1. Final interview with SAP delivery head.
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Q1. Explain your project
Ans. 
Developed a predictive model to forecast sales based on historical data
• Collected and cleaned sales data from multiple sources

• Performed exploratory data analysis to identify trends and patterns

• Built a machine learning model using regression techniques

• Evaluated model performance using metrics like RMSE and MAE
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Q2. Oops concept was asked
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