BARC Instrument Engineer Interview Questions and Answers

Basic aptitude questions of quant and reasoning and general english

Case study of varies products asked around 15-20 which were easy to attempt

Q1. About yourself, practical knowledge, family, data related,etc.
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Q2. What is sql. Excel formulas
Ans. 
SQL is a programming language used for managing and manipulating relational databases. Excel formulas are used for performing calculations and data analysis in Microsoft Excel.
• SQL is used to retrieve, insert, update, and delete data from databases.

• Excel formulas are written using functions and operators to perform calculations and manipulate data.

• SQL example: SELECT * FROM customers WHERE age > 30;

• Excel formula example...read more
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Normal aptitude questions

Q1. Interview with manager
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There will be MCQ Questions. They will ask you to use internet and answer the questions.
They want to check your research skills.

Q1. If you are a fresher then you can easily clear the interview, Hr will ask normal questions about yourself and about your education background.
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Aptitude test of 30 min is conducted

Basic coding questions And logical questions

Simple program for mathematics

Q1. State linear regression assumptions.
Ans. 
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Linearity: The relationship between the independent and dependent variables is linear.

• Independence: The residuals are independent of each other.

• Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Normality: The residuals are normally distributed.

• Example: If we are predict...read more
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Q2. What is multicollinearity? How to tackle this condition?
Ans. 
Multicollinearity is a phenomenon in which two or more predictor variables in a regression model are highly correlated.
• Multicollinearity can lead to unstable estimates of the coefficients and make it difficult to determine the effect of each predictor variable on the outcome.

• One way to tackle multicollinearity is to identify the highly correlated variables and consider removing one of them from the model.

• Another approa...read more
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Q3. What are the components of time series analysis?
Ans. 
Time series analysis components include trend, seasonality, cyclicality, and irregularity.
• Trend: Long-term movement or direction of the data.

• Seasonality: Regular patterns that occur at specific intervals.

• Cyclicality: Repeating patterns that are not necessarily at fixed intervals.

• Irregularity: Random fluctuations or noise in the data.

• Examples: Trend in stock prices, seasonality in retail sales, cyclicality in economic c
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Q4. What are the preventive measures if regression assumptions are not met?
Ans. 
Preventive measures for regression assumptions not met
• Check for multicollinearity among independent variables

• Transform variables if they are not normally distributed

• Consider using non-parametric regression methods

• Use robust regression techniques to handle outliers

• Collect more data to improve model performance
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Q1. How to tackle with missing values ?
Ans. 
Handling missing values is crucial in data analysis. Various techniques like imputation, deletion, or prediction can be used.
• Use imputation techniques like mean, median, mode to fill in missing values.

• Consider using predictive modeling to estimate missing values based on other variables.

• Delete rows or columns with a high percentage of missing values if they cannot be accurately imputed.

• Use advanced techniques like K-ne...read more
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Q2. What is the difference between tuple and list ?
Ans. 
Tuple is immutable and fixed in size, while list is mutable and can change in size.
• Tuple is created using parentheses, while list is created using square brackets.

• Tuple elements can be of different data types, while list elements are usually of the same data type.

• Tuple is faster than list for iteration and accessing elements.

• Example: tuple = (1, 'a', True), list = [1, 2, 3]
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Q3. How to detect outlier ?
Ans. 
Outliers can be detected using statistical methods like Z-score, IQR, or visualization techniques like box plots.
• Calculate Z-score for each data point and identify points with Z-score greater than a certain threshold (usually 3 or -3).

• Use Interquartile Range (IQR) to identify outliers by determining data points that fall below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR.

• Visualize the data using box plots and identify points...read more
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Q1. Why do you think you are a fit ?
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Written test covering economic questions

Q1. Please introduce yourself in mandarin
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