Black Knight Mortgage Associate Interview Questions and Answers

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Q1. Its easy they will ask about your experience
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Some mcq questions and coding question. Both where easy to medium level. Prepare combination/permutation/Time complexity etc

A question related to Binary search and some other follow ups.

Q1. Tell me about yourself
Ans. 
I am a dedicated and detail-oriented analyst with a strong background in data analysis and problem-solving.
• I have a Bachelor's degree in Statistics and have completed multiple data analysis projects during my studies.

• I am proficient in using statistical software such as R and Python for data analysis.

• I have experience in conducting market research and creating reports to help businesses make informed decisions.

• I am a q...read more
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Q2. What do you know about our client
Ans. 
Our client is a multinational technology company specializing in software development and cloud services.
• Our client is known for their popular operating system used by millions of people worldwide.

• They offer a wide range of productivity software and cloud services for businesses and individuals.

• The company was founded in the 1970s by Bill Gates and Paul Allen.

• They are also involved in hardware products such as laptops,
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Q1. Prior Experience
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Q2. Capital Market, Hedge Fund
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Q3. Derivatives, Scenario-based question
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Q1. Prior Experience
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Q2. How do you handle escalations?
Ans. 
I handle escalations by addressing the issue promptly, involving necessary stakeholders, and providing regular updates.
• Address the issue promptly to prevent further escalation

• Involve necessary stakeholders to gather input and support

• Provide regular updates to all parties involved to ensure transparency and progress

• Implement solutions to prevent similar escalations in the future
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Q3. What was the toughest escalation I have ever managed
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Q4. Excel - Index, Match, Lookups - XLookup, VLookup, HLookup
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Q5. Job Description
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Q1. Long term vs short-term securities
Ans. 
Long-term securities are investments held for an extended period, while short-term securities are held for a shorter duration.
• Long-term securities typically have maturity dates of over one year, such as bonds or stocks.

• Short-term securities have maturity dates of one year or less, like Treasury bills or certificates of deposit.

• Long-term securities offer higher potential returns but also higher risk, while short-term se...read more
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Q2. Data analysis, vizualization
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Q3. Yield to Maturity, the relationship between yield and interest
Ans. 
Yield to Maturity is the total return anticipated on a bond if it is held until it matures.
• Yield to Maturity is the annual rate of return an investor can expect to earn if the bond is held until it matures.

• It takes into account the bond's current market price, par value, coupon interest rate, and time to maturity.

• Yield to Maturity helps investors compare different bonds and make informed investment decisions.

• For exampl...read more
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Q4. About my project
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Q5. Job Description
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They asked basic java programs
Adding 2 no by using strams.

Q1. 1) basic introduction 2) regrarding java selenium 3) Framework design 4) cicd pipeline 5) git 6) testng 7) postman
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Q1. Basic salary discusion , notice period
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Q1. Exceptions and thread implementation
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Q2. Basics of restful web services
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Q1. Explain random forest
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q2. What is boosting
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Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.
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