10+ Mohan Impex Interview Questions and Answers

To delete duplicate rows based on key in a table, use the DELETE statement with a subquery.

• Identify the key column(s) that define the uniqueness of a row

• Use the GROUP BY clause to group the rows by the key column(s)

• Use the HAVING clause to filter out the groups that have more than one row

• Use the subquery to select the duplicate rows to be deleted

• Use the DELETE statement with the subquery to delete the duplicate rows

Fact and dimension are two types of data in a data warehouse.

• Fact is a measurable event that can be analyzed, while dimension provides context to the fact.

• Fact is quantitative, while dimension is qualitative.

• Fact is stored in a fact table, while dimension is stored in a dimension table.

• Examples of fact include sales, revenue, and profit, while examples of dimension include time, location, and product.

• Fact and dimension are used in data modeling to create a star schema or snow...read more

SCDs are used to track changes in data over time. There are three types: Type 1, Type 2, and Type 3.

• Type 1 SCDs overwrite old data with new data.

• Type 2 SCDs add a new row for each change, with a start and end date.

• Type 3 SCDs add columns to the existing row to track changes.

• SCDs are commonly used in data warehousing and business intelligence.

• The choice of SCD type depends on the specific use case and data requirements.

Outer join returns all records from one table and matching records from another, while inner join returns only matching records.

• Outer join uses the (+) symbol in Oracle and LEFT/RIGHT OUTER JOIN in SQL Server

• Inner join uses INNER JOIN keyword in SQL

• Sample data: Table A has 5 records, Table B has 3 records, Outer join returns 8 records while Inner join returns 3 records

Degenerate dimension refers to a dimension with only one value.

• A degenerate dimension is a dimension table with only one column and one row.

• It is used to join fact tables with different granularity levels.

• For example, a time dimension with only one value can be used to join a sales fact table with daily granularity and a returns fact table with hourly granularity.

ETL stands for Extract, Transform, Load and DWH stands for Data Warehouse.

• ETL is a process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target system.

• DWH is a system used for storing and managing data from various sources for business intelligence purposes.

• ETL is a crucial step in populating a DWH with data.

• ETL involves data extraction, data transformation, and data loading.

• DWH is designed to support decis...read more

OLTP is a transactional system for day-to-day operations, while OLAP is analytical system for decision-making.

• OLTP deals with real-time data processing, while OLAP deals with historical data analysis.

• OLTP is optimized for write operations, while OLAP is optimized for read operations.

• OLTP is used for operational tasks like order processing, while OLAP is used for strategic tasks like sales forecasting.

• OLTP databases are normalized, while OLAP databases are denormalized.

• Example...read more

Yes, fact tables can be joined together in a data warehouse to combine related information.

• Fact tables contain quantitative data and are typically joined using common dimensions.

• Joining fact tables allows for more comprehensive analysis and reporting.

• For example, joining a sales fact table with a customer fact table can provide insights on customer behavior and purchasing patterns.

Arrow functions are concise syntax for writing functions in JavaScript, compared to normal functions.

• Arrow functions do not have their own 'this' keyword, they inherit it from the parent scope.

• Arrow functions do not have 'arguments' object.

• Arrow functions cannot be used as constructors.

• Arrow functions are more concise and easier to read compared to normal functions.