ANZ Banking Group Interview Questions and Answers

Q1. Load 60 GB of csv file and perform transformation.
Ans. 
Use distributed computing framework like Apache Spark to efficiently load and transform the large CSV file.
• Use Apache Spark to distribute the workload across multiple nodes for faster processing.

• Utilize Spark's DataFrame API to read the CSV file and apply transformations.

• Consider partitioning the data to optimize performance.

• Use Spark SQL for complex transformations and aggregations.

• Leverage in-memory processing capabi
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Q1. Explain event driven architecture of node.js
Ans. 
Node.js uses event-driven architecture where events trigger callbacks to handle asynchronous operations.
• Node.js uses event loop to handle asynchronous operations efficiently

• Events are emitted by various objects in Node.js like HTTP servers, file system, etc.

• Callbacks are registered to handle events and execute code when events occur
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Banking related and communication skills along with technical

Q1. What is lending process
Ans. 
Lending process involves the steps taken by financial institutions to evaluate, approve, and disburse loans to borrowers.
• Borrower submits loan application with required documents

• Financial institution evaluates borrower's creditworthiness

• Loan is approved or denied based on evaluation

• If approved, terms and conditions are set including interest rate and repayment schedule

• Funds are disbursed to borrower
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Q2. What is australian market
Ans. 
The Australian market refers to the financial market in Australia where securities are bought and sold.
• The Australian market includes the Australian Securities Exchange (ASX) where stocks, bonds, and other securities are traded.

• It is influenced by factors such as the country's economic performance, government policies, and global market trends.

• Key industries in the Australian market include mining, finance, healthcare,...read more
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Q1. Are you good with data preprocessing?
Ans. 
Yes, I am proficient in data preprocessing, ensuring data quality and readiness for analysis.
• Handling missing values: For example, using mean imputation for numerical data.

• Data normalization: Scaling features to a range, such as using Min-Max scaling.

• Encoding categorical variables: Converting categories to numerical values using one-hot encoding.

• Outlier detection: Identifying and treating outliers using methods like Z-...read more
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