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I applied via Campus Placement
1 good coding question and 33 mcqs
Create a database to store information about colleges, students, and professors.
Create tables for colleges, students, and professors
Include columns for relevant information such as name, ID, courses, etc.
Establish relationships between the tables using foreign keys
Use SQL queries to insert, update, and retrieve data
Consider normalization to avoid data redundancy
I applied via Naukri.com and was interviewed in Oct 2024. There were 2 interview rounds.
Data bricks is a unified analytics platform that provides a collaborative environment for data scientists, engineers, and analysts.
Data bricks simplifies the process of building data pipelines and training machine learning models.
It allows for easy integration with various data sources and tools, such as Apache Spark and Delta Lake.
Data bricks provides a scalable and secure platform for processing big data and running ...
Optimizing code involves identifying bottlenecks, improving algorithms, using efficient data structures, and minimizing resource usage.
Identify and eliminate bottlenecks in the code by profiling and analyzing performance.
Improve algorithms by using more efficient techniques and data structures.
Use appropriate data structures like hash maps, sets, and arrays to optimize memory usage and access times.
Minimize resource us...
SQL window function is used to perform calculations across a set of table rows related to the current row.
Window functions operate on a set of rows related to the current row
They can be used to calculate running totals, moving averages, rank, etc.
Examples include ROW_NUMBER(), RANK(), SUM() OVER(), etc.
Half hour with spark python azure databricks
I applied via Naukri.com and was interviewed before Dec 2023. There were 2 interview rounds.
Tredence interview questions for designations
I applied via Naukri.com and was interviewed in Sep 2023. There was 1 interview round.
I have used activities such as Copy Data, Execute Pipeline, Lookup, and Data Flow in Data Factory.
Copy Data activity is used to copy data from a source to a destination.
Execute Pipeline activity is used to trigger another pipeline within the same or different Data Factory.
Lookup activity is used to retrieve data from a specified dataset or table.
Data Flow activity is used for data transformation and processing.
To execute a second notebook from the first notebook, you can use the %run magic command in Jupyter Notebook.
Use the %run magic command followed by the path to the second notebook in the first notebook.
Ensure that the second notebook is in the same directory or provide the full path to the notebook.
Make sure to save any changes in the second notebook before executing it from the first notebook.
Data lake storage is optimized for big data analytics and can store structured, semi-structured, and unstructured data. Blob storage is for unstructured data only.
Data lake storage is designed for big data analytics and can handle structured, semi-structured, and unstructured data
Blob storage is optimized for storing unstructured data like images, videos, documents, etc.
Data lake storage allows for complex queries and ...
Get interview-ready with Top Tredence Interview Questions
I applied via Naukri.com and was interviewed in Sep 2023. There were 2 interview rounds.
Top trending discussions
I was interviewed in Mar 2017.
I believe the recruiting process is thorough and well-organized.
The company uses a combination of resume screening, interviews, and assessments to evaluate candidates.
They have a clear timeline for the hiring process and keep candidates informed of their progress.
Feedback is provided to candidates after interviews to help them improve for future opportunities.
I applied via Job Portal and was interviewed in Mar 2024. There were 3 interview rounds.
Spark cluster sizing depends on workload, data size, memory requirements, and processing speed.
Consider the size of the data being processed
Take into account the memory requirements of the Spark jobs
Factor in the processing speed needed for the workload
Scale the cluster based on the number of nodes and cores required
Monitor performance and adjust cluster size as needed
Implement a pipeline based on given conditions and data requirement
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