ICICI Prudential Life Insurance Financial Consultant Interview Questions and Answers

Q1. How are heard about HDFC Life?
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Q1. Sql plsql and oracle
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Q1. Sql plsql and oracle
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Q2. Spa plsql and oracle
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Q1. Talk about your experience
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Q2. What is your salary expectation
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Q1. Do you know about CAMELS
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CAMELS is a rating system used by regulators to assess the soundness of financial institutions.
• CAMELS stands for Capital adequacy, Asset quality, Management, Earnings, Liquidity, and Sensitivity to market risk.

• Each component is rated on a scale of 1 to 5, with 1 being the best and 5 being the worst.

• Regulators use CAMELS ratings to determine the overall health and stability of financial institutions.

• For example, a bank ...read more
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Q2. What is substance over form
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Substance over form is a concept in accounting where the economic reality of a transaction is more important than its legal form.
• Focuses on the underlying economic substance of a transaction rather than its legal form

• Ensures that financial statements accurately reflect the true financial position of a company

• Helps prevent misleading financial reporting by looking beyond surface details

• Example: Recording a lease as an o...read more
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Q1. MOSTLY SQL and Power BI Related Question they asked but interviewer is just 2 years of non technical person. They don't know what they are doing in candidate recruitment process. Its completely time waste...read more
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Q2. SQL , DAX , Power BI
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Q3. You job responsibility and Project
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Q4. Dax Function and SQL Windows Function.
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Q5. You are ok with night shift ?
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Q1. Statistical questions
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Easy to crack the interview

Q1. Easy to crack interview
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Q2. About insurance
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Q3. Who Is underwriter
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Q4. Claims meaning and other insurance terms
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Q5. Where do you see yourself in 5yea4s
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Statistics Questions

Q1. Assumptions of Machine Learning
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Assumptions of Machine Learning include data availability, feature relevance, and model interpretability.
• Data availability is crucial for training machine learning models.

• Assumptions about the relevance of features in the data must be made.

• Model interpretability is important for understanding the decisions made by the machine learning model.
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