Canara Bank Assistant Manager Interview Questions and Answers

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Q1. Tell me about yourself.
Ans. 
Experienced professional with a background in retail management and a proven track record of driving sales and improving team performance.
• Over 5 years of experience in retail management

• Strong leadership skills demonstrated through successful team management

• Proven ability to increase sales and meet targets

• Excellent communication and interpersonal skills

• Passionate about delivering exceptional customer service
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Q2. Are you open to travel to rural areas for sales?
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Yes, I am open to travel to rural areas for sales as it allows me to reach a wider customer base and understand diverse market needs.
• I have previous experience traveling to rural areas for sales in my current role.

• I believe that reaching out to customers in rural areas is essential for expanding the business.

• I am willing to travel to rural areas to meet sales targets and build strong customer relationships.
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Q1. Working Experience
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Q2. Knowledge for financial services
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Q1. Working Experience
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Q2. How Handel customer & staff
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Handle customers and staff with empathy, communication, and problem-solving skills.
• Listen actively to customers and staff to understand their needs and concerns.

• Communicate clearly and effectively to address any issues or questions.

• Provide support and guidance to both customers and staff in a professional and respectful manner.

• Resolve conflicts or challenges calmly and efficiently to maintain a positive work environmen...read more
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Q1. Introduction round
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Q2. About the banking
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Q3. Where do you located
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Q4. Salaried expectation
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Q5. And branch location
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Q1. Concepts of class, polymorphism, what are decorators, concepts of inheritance.
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Q2. Difference between bagging and boosting
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Bagging and boosting are ensemble learning techniques used in machine learning to improve the performance of models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent mode...read more
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Q3. Python coding question to find the employee with second largest income
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Python code to find the employee with second largest income in a list of employees
• Sort the list of employees by income in descending order

• Return the employee at index 1 in the sorted list
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Q4. Detect the unique characters in the substring and their length
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The candidate needs to identify unique characters in a given substring and calculate their length.
• Iterate through the substring and store each character in a set to keep track of unique characters

• Calculate the length of the set to determine the number of unique characters

• Return the set of unique characters and their length as an array of strings
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Q5. Concepts of mutable and immutable in python.
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In Python, mutable objects can be changed after creation, while immutable objects cannot be changed.
• Mutable objects: lists, dictionaries, sets

• Immutable objects: strings, tuples, numbers

• Example: x = [1, 2, 3] (mutable), y = 'hello' (immutable)
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Q1. What is confidence interval of an estimator?
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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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