London Stock Exchange Group Interview Questions, Process, and Tips for Experienced

Q1. All about previous experience and compliance related questions they have asked
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Q1. Technical questions on software testing
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Given problem and asked to write tests & automate

Q1. Culture explained and asked about previous work experience
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Q1. Discussion with Head of Quality. Culture and expectations on the job role.
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Q1. General software testing discussion. Expectations on the current role was explained.
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Q1. Scenario based questions
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Q2. From where you belongs to .. are you from Bangalore or not
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Q1. Java 8, microservices and coding.
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Q1. System design , Java, springboot.
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Q1. CI/CD pipeline, process, nfrs
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Q1. Manager round, normal process followed in he company, describe about the roles and responsibilities.
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Q1. How do you load data from JSON?
Ans. 
Data can be loaded from JSON using Snowflake's COPY INTO command.
• Use the COPY INTO command in Snowflake to load data from JSON files.

• Specify the file format as JSON in the COPY INTO command.

• Map the JSON attributes to the columns in the target table.

• Example: COPY INTO target_table FROM 's3://bucket_name/file.json' FILE_FORMAT = (TYPE = 'JSON');
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Q2. What are the performance tuning options in snowflake?
Ans. 
Performance tuning options in Snowflake include clustering, materialized views, query profiling, and resource monitoring.
• Use clustering keys to organize data for faster query performance

• Create materialized views to pre-aggregate data and improve query speed

• Utilize query profiling to identify and optimize slow queries

• Monitor resource usage to ensure efficient query execution
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Q3. How do you configure snowpipe?
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Snowpipe is configured using a Snowflake account, specifying the source data location and the target table.
• Configure a stage in Snowflake to specify the source data location.

• Create a pipe in Snowflake to define the target table and the stage.

• Set up notifications for the pipe to trigger loading data automatically.

• Monitor the pipe for any errors or issues in data loading.

• Example: CREATE STAGE my_stage URL = 's3://my_buck...read more
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Q4. Data modelling techniques used
Ans. 
Various data modelling techniques like dimensional modelling, ER modelling, and data vault are used.
• Dimensional modelling is used for data warehousing and involves organizing data into facts and dimensions.

• ER modelling is used to visualize the data relationships in an entity-relationship diagram.

• Data vault modelling is used for agile data warehousing and involves creating a flexible and scalable data model.
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Q5. Streans in snowflake
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Streams in Snowflake are used to continuously replicate data from a table to another destination in real-time.
• Streams capture changes made to a table, such as inserts, updates, and deletes.

• They can be used to track changes and replicate data to other tables or external systems.

• Streams are created on a specific table and can be monitored for changes using SQL commands.
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Q1. Snowflake ETL Pipelines
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Q2. Streams in snowflake
Ans. 
Streams in Snowflake are continuous flows of data that can be consumed in real-time for processing and analysis.
• Streams capture changes made to a table and make them available for processing in real-time.

• They can be used to implement CDC (Change Data Capture) solutions.

• Streams can be created using the CREATE STREAM statement.

• Example: CREATE STREAM my_stream ON TABLE my_table;
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Q3. Data migration from on prem to cloud data warehouse.
Ans. 
Data migration from on-premises to cloud data warehouse involves transferring data from local servers to a cloud-based storage solution.
• Assess current data sources and structures on-premises

• Select appropriate cloud data warehouse solution (e.g. Snowflake)

• Plan data migration strategy including data extraction, transformation, and loading (ETL)

• Test data migration process thoroughly before full implementation

• Monitor and o
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Q4. Loading JSON file
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Q5. Stages n snowflake
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Q6. Clustering Keys in snowflake
Ans. 
Clustering keys in Snowflake help improve query performance by organizing data in a specific order.
• Clustering keys determine the physical order of data in Snowflake tables.

• They are defined at the table level and can be set during table creation or altered later.

• Clustering keys can be single or composite, and should be chosen based on the most commonly used columns in queries.

• They help reduce the amount of data scanned ...read more
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