London Stock Exchange Group Snowflake Data Engineer Interview Questions, Process, and Tips

Q1. Detailed project questions
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Q2. Explain completed gen ai project
Ans. 
Developed a generative AI model to create realistic images of fictional characters.
• Used GANs (Generative Adversarial Networks) to generate new images based on existing data.

• Trained the model on a dataset of character images from various sources.

• Implemented techniques like style transfer to enhance the diversity and creativity of generated images.

• Evaluated the model's performance based on image quality metrics and user
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Basic aptitude questions

Data structure and algorithms

Q1. Java, SQL questions
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Q1. Casual talk about roles
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Q1. Tell me about your experience with Thoughtspot BI tool.
Ans. 
I have extensive experience using Thoughtspot BI tool to analyze and visualize data.
• Utilized Thoughtspot to create interactive dashboards for stakeholders

• Performed data cleaning and transformation within Thoughtspot platform

• Generated insights and recommendations based on data analysis in Thoughtspot
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Q2. What is your experience with API integration in Thoughtspot?
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I have extensive experience with API integration in Thoughtspot.
• Developed custom API integrations to pull data from external sources into Thoughtspot

• Utilized Thoughtspot's REST API to automate data loading and report generation

• Worked closely with IT teams to troubleshoot and optimize API connections
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Basic aptitude test like distance problem , age etc

Q1. Tell me about your projects
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Q2. What do you know about mutual funds
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Mutual funds are investment vehicles that pool money from multiple investors to invest in a diversified portfolio of securities.
• Mutual funds are managed by professional fund managers who make investment decisions on behalf of the investors.

• Investors can buy shares of mutual funds, which represent their ownership in the fund's portfolio.

• Mutual funds offer diversification, liquidity, and professional management to invest...read more
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Q1. Do you have prior experience with ML
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Q2. What are your long term plans
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Q1. Explain any Data Science project
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Developed a predictive model to forecast customer churn for a telecommunications company.
• Identified key features such as customer tenure, monthly charges, and service usage

• Collected and cleaned data from customer databases

• Built a machine learning model using logistic regression or random forest algorithms

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to reduce
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Q2. Types of Error in Statistics
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Types of errors in statistics include sampling error, measurement error, and non-sampling error.
• Sampling error occurs when the sample does not represent the population accurately.

• Measurement error is caused by inaccuracies in data collection or measurement instruments.

• Non-sampling error includes errors in data processing, analysis, and interpretation.

• Examples: Sampling error - selecting a biased sample, Measurement err...read more
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Q3. Types of Machine learning models
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Types of machine learning models include supervised learning, unsupervised learning, and reinforcement learning.
• Supervised learning: Models learn from labeled data, making predictions based on past examples (e.g. linear regression, support vector machines)

• Unsupervised learning: Models find patterns in unlabeled data, clustering similar data points together (e.g. k-means clustering, PCA)

• Reinforcement learning: Models le...read more
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Q4. Functions of pandas library, such as get_dummies()
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get_dummies() function in pandas library is used to convert categorical variables into dummy/indicator variables.
• get_dummies() function creates dummy variables for categorical columns in a DataFrame.

• It converts categorical variables into numerical representation for machine learning models.

• Example: df = pd.get_dummies(df, columns=['column_name'])
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Q1. Explain Data types in SQL?
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Data types in SQL define the type of data that can be stored in a column of a table.
• Data types include integer, float, text, date, boolean, etc.

• Each data type has specific properties and constraints.

• Examples: INT for integers, VARCHAR for variable-length character strings.
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Q2. Explain the difference between DBMS and RDBMS?
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DBMS is a software system that manages databases, while RDBMS is a type of DBMS that stores data in a structured format using tables.
• DBMS stands for Database Management System, which is a software system that allows users to interact with a database.

• RDBMS stands for Relational Database Management System, which is a type of DBMS that stores data in a structured format using tables with relationships between them.

• RDBMS e...read more
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Q1. Question related to SQL.
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Q2. Concept of Data Warehousing.
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Data warehousing is the process of collecting, storing, and managing data from various sources for analysis and reporting.
• Data warehousing involves extracting data from multiple sources and consolidating it into a central repository.

• It is used for analytical reporting, business intelligence, and decision-making purposes.

• Data warehouses are designed for query and analysis rather than transaction processing.

• Examples of d...read more
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