Dun & Bradstreet Architect Interview Questions and Answers

Q1. Previous project work, achievement, Challenges and gain
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Q2. Database Modelling
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Q1. Last three project description, achievement and challenges
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Q2. Graph Data Model
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Q1. Basic question on past experience and estimation questions
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Autocracy and implications for India

Q1. Tell me about yourself? Project details explain? passion (professional related and why?
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Q1. Explain top credit risk parameters to deep dive into banking portfolio
Ans. 
Top credit risk parameters in banking portfolio
• Probability of Default (PD)

• Loss Given Default (LGD)

• Exposure at Default (EAD)

• Credit Rating of the borrower

• Loan-to-Value ratio (LTV)

• Debt-to-Income ratio (DTI)
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Q2. Expected credit loss computation what is its impact from traditional loss provision framework
Ans. 
Expected credit loss computation impacts traditional loss provision framework by incorporating forward-looking information and requiring more complex calculations.
• Expected credit loss computation considers future expected credit losses based on historical data, current conditions, and reasonable and supportable forecasts.

• Traditional loss provision framework typically only considers incurred losses based on historical d...read more
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Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
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Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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2hrs - sections include aptitude, machine learning, deep learning and two easy python coding questions

Q1. Resume scrutiny with projects and internships
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Q2. How to measure 45 mins duration using two identical wires
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Q3. Partition a round cake into eight equal parts within three cuts
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Q4. 10 coins puzzle (5 heads up and 5 tails up)
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