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I applied via Referral and was interviewed in Apr 2021. There were 3 interview rounds.
Live vs Extract in Tableau and why Extract is faster
Live connection directly queries the data source while Extract creates a static snapshot of the data
Extracts are faster because they reduce the amount of data transferred over the network
Extracts can be scheduled to refresh at specific intervals
Extracts can be compressed to reduce file size and improve performance
Yes, it is possible to update data in Tableau Extract.
Tableau Extracts can be refreshed to update data
Data can be added, removed or modified in the data source and then refreshed in Tableau
Extracts can be set to refresh automatically or manually
Relationships are used to connect tables in Tableau Prep while Joins are used to combine tables in Tableau Desktop.
Relationships are used in Tableau Prep to connect tables based on common fields.
Joins are used in Tableau Desktop to combine tables based on common fields.
Relationships are used to create a flow of data between tables in Tableau Prep.
Joins are used to create a single table from multiple tables in Tableau D...
Dashboard Actions are interactive elements that allow users to interact with the data on a dashboard.
Dashboard Actions are used to filter, highlight, or navigate to other dashboards or web pages.
Steps to apply Dashboard Actions include selecting the element to apply the action to, choosing the type of action, and configuring the action settings.
Examples of Dashboard Actions include clicking on a chart to filter the dat...
Use a parameter to filter data and create two separate tables, then combine them using a union.
Create a parameter to select the number of top and bottom rankings to display
Create a table for the top rankings using the parameter and the RANKX function
Create a table for the bottom rankings using the parameter and the RANKX function
Combine the two tables using a union
Add a column to differentiate between top and bottom ra
Sets are a collection of unique values while groups are a collection of related values.
Sets contain only unique values while groups can have repeated values.
Sets are used for filtering data while groups are used for aggregating data.
Sets are used in Tableau for creating custom fields while groups are used for creating hierarchies.
Example of a set: a set of customers who have made a purchase in the last month.
Example of...
Optimize data source, reduce number of rows/columns, use extracts, optimize queries, use filters.
Optimize data source by removing unnecessary columns and rows
Reduce number of rows and columns to only necessary data
Use extracts instead of live connections
Optimize queries by using custom SQL or aggregating data
Use filters to limit the amount of data being extracted
Large data extracts in Tableau are handled by optimizing data sources, using filters, and aggregating data.
Optimize data sources by removing unnecessary columns and filtering data before extracting.
Use filters to limit the amount of data extracted.
Aggregate data to reduce the number of rows and columns.
Consider using Tableau's data engine to improve performance.
Use incremental extracts to only extract new or updated da...
Top trending discussions
NER training using deep learning
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
Break down the assignment into smaller tasks to make it more manageable
Set deadlines for each task to stay on track
Regularly check progress to ensure everything is on schedule
Seek feedback from colleagues or supervisors to improve the quality of work
I applied via Job Fair and was interviewed in May 2024. There were 3 interview rounds.
They gave a span of 3 days to build an AI-powered webapp
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
Experience in setting up and managing virtual machines, storage, and networking in cloud environments
Knowledge of cloud services like EC2, S3, RDS, and Lambda
Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
Developed a predictive model for customer churn in a telecom company
Collected and cleaned customer data from various sources
Performed exploratory data analysis to identify key factors influencing churn
Built and fine-tuned machine learning models to predict customer churn
Challenges included imbalanced data, feature engineering, and model interpretability
My goal in life is to make a positive impact on others and continuously learn and grow.
To make a positive impact on others through my work and actions
To continuously learn and grow personally and professionally
To achieve a work-life balance that allows me to pursue my passions and interests
posted on 18 May 2024
I applied via Walk-in and was interviewed in Apr 2024. There was 1 interview round.
posted on 6 May 2024
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There was 1 interview round.
I applied via Campus Placement
Bulb and switch puzzle
Rope burning and length question
posted on 7 Oct 2023
Basic DP, Array Questions
I applied via Recruitment Consulltant and was interviewed before Aug 2023. There were 2 interview rounds.
Easy array questions.
Developed a machine learning model to predict customer churn for a telecom company.
Used Python and scikit-learn to preprocess data and build the model
Performed feature engineering to improve model performance
Evaluated model using metrics like accuracy, precision, and recall
posted on 9 May 2023
I applied via Recruitment Consulltant and was interviewed in Nov 2022. There were 2 interview rounds.
There are various ML algorithms such as linear regression, decision trees, random forests, SVM, KNN, neural networks, etc.
Linear regression is used for predicting continuous values
Decision trees and random forests are used for classification and regression
SVM is used for classification and regression
KNN is used for classification and regression
Neural networks are used for complex problems such as image recognition and
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