NISM Assistant Manager Interview Questions and Answers

Aptitude , vocn, maths, sales

Q1. Hr round with detailed information on role
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Q1. Your resume background
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I have a diverse background in retail management, customer service, and team leadership.
• Managed a team of 15 retail associates in a fast-paced environment

• Implemented customer service training programs to improve satisfaction rates

• Led inventory management initiatives to reduce costs and increase efficiency
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Q2. Previous expirience
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I have 5 years of experience in retail management, including 2 years as an Assistant Manager.
• Managed a team of 15 employees, overseeing their schedules and performance

• Implemented new sales strategies that resulted in a 10% increase in revenue

• Handled customer complaints and resolved issues effectively
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Q3. Why sbilife in your view
Ans. 
Sbilife offers a strong reputation, competitive products, and a commitment to customer service.
• Sbilife has a strong reputation in the insurance industry

• They offer competitive products and services

• The company has a commitment to excellent customer service
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Q4. Answer Your strength and weekness
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Q1. About previous work experience
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Q2. About bank knowledge
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Q1. What is confidence interval of an estimator?
Ans. 
Confidence interval of an estimator is a range of values that is likely to contain the true value of the parameter being estimated.
• Confidence interval provides a range of values within which we are confident the true parameter value lies.

• It is calculated based on the sample data and the desired level of confidence.

• For example, a 95% confidence interval for the mean weight of a population would be a range of values with...read more
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Q2. How logistic regression works for multiple class classification?
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Logistic regression works by calculating the probability of each class and assigning the observation to the class with the highest probability.
• Logistic regression calculates the probability of each class using the logistic function.

• The model then assigns the observation to the class with the highest probability.

• It is commonly used for binary classification, but can be extended to multiple classes using techniques like
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Q3. How do you deal with missing values in a data?
Ans. 
I handle missing values by identifying them, deciding on a strategy (imputation or deletion), and documenting the process.
• Identify missing values by checking for null values or using descriptive statistics.

• Decide on a strategy for handling missing values such as imputation (mean, median, mode) or deletion (row or column).

• Document the process of handling missing values to ensure transparency and reproducibility.
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Q1. Working history
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Q2. Last working profile details
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Q3. Discuss about last job role and current job role difference.
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Q1. Salary discussion
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Q1. Tell me all your day
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Q2. Tell me about the process you do
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Q1. Tell me your Working in previous Company
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Q2. Tell About Your Family and Last company experience
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Q3. Why you change your Current Organisation
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Q1. Qualification and experience
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Q2. Previous Company Experience and Working shedule
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