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I applied via Campus Placement and was interviewed in Sep 2024. There was 1 interview round.
SQL query using aggregate functions to perform calculations on a dataset
Use aggregate functions like SUM, AVG, COUNT, MIN, MAX to perform calculations on a dataset
Group data using GROUP BY clause to apply aggregate functions on specific groups
Filter data using HAVING clause after applying aggregate functions
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I applied via Approached by Company and was interviewed in Dec 2024. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Sep 2024. There were 2 interview rounds.
posted on 11 Jun 2024
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
It stores data in Parquet format and uses a transaction log to keep track of all the changes made to the data.
Delta Lake architecture includes a storage layer, a transaction log, and a metadata l...
Activities in ADF refer to the tasks or operations that can be performed in Azure Data Factory.
Activities can include data movement, data transformation, data processing, and data orchestration.
Examples of activities in ADF are Copy Data activity, Execute Pipeline activity, Lookup activity, and Web activity.
Activities can be chained together in pipelines to create end-to-end data workflows.
Each activity in ADF has prop...
Mounting process in Databricks allows users to access external data sources within the Databricks environment.
Mounting allows users to access external data sources like Azure Blob Storage, AWS S3, etc.
Users can mount a storage account to a Databricks File System (DBFS) path using the Databricks UI or CLI.
Mounted data can be accessed like regular DBFS paths in Databricks notebooks and jobs.
Blob storage is for unstructured data, while Data Lake is for structured and unstructured data with metadata.
Blob storage is optimized for storing large amounts of unstructured data like images, videos, and backups.
Data Lake is designed to store structured and unstructured data with additional metadata for easier organization and analysis.
Blob storage is typically used for simple storage needs, while Data Lake is used ...
I applied via Referral and was interviewed before Nov 2022. There were 3 interview rounds.
SQL coding and Ssis technical skills
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Arrow functions are more concise and have a lexical 'this' binding 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 their own 'arguments' object.
Arrow functions cannot be used as constructors with 'new'.
Arrow functions are more concise and have implicit return when no curly braces are used.
forEach is used to iterate over an array and perform a function on each element, while map creates a new array by applying a function to each element.
forEach does not return a new array, while map does
forEach does not modify the original array, while map creates a new array
forEach is used for side effects, while map is used for transformation
Example: forEach - array.forEach(item => console.log(item)), map - const newAr
posted on 4 Sep 2024
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posted on 25 Nov 2024
I applied via campus placement at National Institute of Technology (NIT), Silchar and was interviewed in Oct 2024. There was 1 interview round.
I applied via Naukri.com and was interviewed in May 2024. There were 2 interview rounds.
Excel pivot tables allow users to create computed fields using formulas.
In Excel pivot tables, computed fields are created by adding a new field with a formula.
Formulas can be simple arithmetic operations or more complex calculations.
Computed fields can be used to perform calculations on existing data in the pivot table.
Examples: calculating profit margin by dividing revenue by cost, calculating average sales per month
Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.
Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Example: In...
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