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I applied via Recruitment Consultant and was interviewed in Aug 2020. There were 4 interview rounds.
I am currently working as a Senior Financial Analyst at a multinational corporation.
Preparing financial reports and analysis
Budgeting and forecasting
Providing financial recommendations to senior management
Collaborating with various departments on financial matters
CIR ratio stands for Cash Interest Coverage ratio, which measures a company's ability to pay interest expenses with its operating cash flow.
CIR ratio is calculated by dividing operating cash flow by total interest expenses.
A higher CIR ratio indicates that a company is more capable of covering its interest expenses with its cash flow.
A lower CIR ratio may indicate financial distress or an inability to generate enough c
RoTE is a financial metric used to evaluate the profitability of a company's tangible equity.
RoTE is calculated by dividing net income by average tangible equity.
It measures how efficiently a company is using its tangible assets to generate profit.
Planning and budgeting involve setting financial goals, allocating resources, and monitoring performance to achieve desired RoTE.
For example, a company may set a target RoTE ...
A Banking Profit and Loss (P&L) statement is a financial document that shows the revenues, expenses, and profits of a bank over a specific period of time.
It includes details of interest income, fees and commissions, operating expenses, provisions for loan losses, and net income.
The statement helps in analyzing the financial performance and profitability of the bank.
It is an essential tool for stakeholders, investor...
I applied via Referral and was interviewed in May 2024. There were 2 interview rounds.
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Hadoop architecture is a distributed computing framework for processing large data sets across clusters of computers.
Hadoop consists of HDFS (Hadoop Distributed File System) for storage and MapReduce for processing.
HDFS divides data into blocks and stores them across multiple nodes in a cluster.
MapReduce is a programming model for processing large data sets in parallel across a distributed cluster.
Hadoop also includes ...
Hadoop is a distributed storage system while Spark is a distributed processing engine.
Hadoop is primarily used for storing and processing large volumes of data in a distributed environment.
Spark is designed for fast data processing and can perform in-memory computations, making it faster than Hadoop for certain tasks.
Hadoop uses MapReduce for processing data, while Spark uses Resilient Distributed Datasets (RDDs) for f...
I was interviewed in Mar 2024.
Maths and general knowledge
Environmental studies
I applied via Job Portal and was interviewed in Sep 2023. There was 1 interview round.
Credit risk is the risk of loss due to a borrower's failure to repay a loan or meet contractual obligations.
Credit risk is the risk that a borrower will default on a loan or debt obligation.
It is the potential loss that a lender may suffer if a borrower fails to make payments.
Factors that contribute to credit risk include the borrower's credit history, financial stability, and economic conditions.
Lenders use credit sco...
I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.
Regression is a statistical method to predict continuous outcomes, while classification is used to predict categorical outcomes.
Regression is used when the target variable is continuous, such as predicting house prices based on features like size and location.
Classification is used when the target variable is categorical, like predicting whether an email is spam or not based on its content.
Regression models include lin...
Hyper parameters are settings that are set before the learning process begins and affect the learning process itself.
Hyper parameters are not learned during the training process, but are set before training begins.
They control the learning process and impact the performance of the model.
Examples include learning rate, number of hidden layers, and batch size in neural networks.
Improving model efficiency involves feature selection, hyperparameter tuning, and ensemble methods.
Perform feature selection to reduce dimensionality and focus on relevant features
Optimize hyperparameters using techniques like grid search or random search
Utilize ensemble methods like bagging or boosting to improve model performance
Consider using more advanced algorithms like deep learning for complex data patterns
I applied via LinkedIn and was interviewed in May 2022. There were 2 interview rounds.
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umask - set default file permissions
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