Celebal Technologies Interview Questions, Process, and Tips

Q1. What is append in power query
Ans. 
Append in Power Query is used to combine multiple tables or queries into a single table or query.
• Append is used to stack tables on top of each other, adding rows from one table to the end of another.

• It is useful when you have multiple tables with the same structure and want to combine them into one.

• You can append tables from different data sources or within the same data source.

• For example, you can append sales data fr...read more
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Q2. What is merge in power query
Ans. 
Merge in Power Query combines multiple tables into one by matching rows based on specified columns.
• Merge is used to combine tables in Power Query

• You can merge based on one or more columns

• Different types of merges include inner, left outer, right outer, and full outer joins
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Q1. What is filter context
Ans. 
Filter context determines which rows of data are visible to calculations in DAX formulas.
• Filter context is dynamic and changes based on user interactions.

• It can be set by slicers, filters, or relationships between tables.

• Filter context is used to calculate results based on the current filter selections.

• It helps in determining which rows of data are included in calculations.
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Q2. What is summarize function
Ans. 
Summarize function is used in Power BI to aggregate data based on specified columns.
• Used to group data and perform aggregate functions like sum, count, average, etc.

• Can be used in DAX formulas to create calculated columns or measures.

• Example: SUMMARIZE('Table', 'Column1', 'Column2', SUM('Value'))
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Q1. Probability related
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Q2. Stats related like IQR etc.
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Q1. Difference between entropy & information gain
Ans. 
Entropy measures randomness in data, while information gain measures the reduction in uncertainty after splitting data.
• Entropy is used in decision trees to measure impurity in a dataset before splitting it.

• Information gain is used in decision trees to measure the effectiveness of a split in reducing uncertainty.

• Entropy ranges from 0 (pure dataset) to 1 (completely impure dataset).

• Information gain is calculated as the d...read more
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Q2. LSTM & GRU, Which to use when ?
Ans. 
LSTM for longer sequences, GRU for faster training and less complex models.
• Use LSTM for tasks requiring long-term dependencies and memory retention.

• Use GRU for faster training and simpler models with fewer parameters.

• Consider using LSTM for tasks like language translation or speech recognition.

• Consider using GRU for tasks like sentiment analysis or text generation.
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Time Series data were given, we have to provide some insights

Q1. Majorly around databricks
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Q2. Azure Data Factory
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Q1. Explain the project you are working on.
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Q2. A code on python, strings.
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Q3. Two tables given and asked how many outputs will you get if you apply inner joim
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Q4. Versioning in aws
Ans. 
Versioning in AWS allows you to manage different versions of your resources.
• AWS S3 supports object versioning to keep multiple versions of an object in the same bucket.

• AWS Lambda supports versioning to manage different versions of your functions.

• AWS API Gateway supports versioning to manage different versions of your APIs.
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Q5. Redshift related questions
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Q1. What is indexing in SQL?
Ans. 
Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
• Indexes are created on columns in a database table to speed up the retrieval of data.

• They work similar to the index in a book, allowing the database to quickly find the rows that match a certain condition.

• Indexes can be created using a single column or a combination of columns.

• ...read more
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Q2. Lift capacity and passenger sql query
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Q3. What is your experience in Pyspark , python
Ans. 
I have extensive experience in using Pyspark and Python for data engineering tasks.
• I have worked on various projects involving data processing, transformation, and analysis using Pyspark and Python.

• I am proficient in writing efficient and optimized code in Pyspark for big data processing.

• I have experience in handling large datasets and implementing complex data pipelines using Pyspark and Python.
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Q4. Tell me about architecture of the project that you are working on
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It was smoothly done and questions are easy.

It was smoothly done and difficulty level good

Q1. Oops concepts and ask defination of all concepts
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Q2. Pattern program using any language
Ans. 
Print a pattern program using any programming language
• Use nested loops to print the desired pattern

• Identify the pattern and determine the number of rows and columns needed

• Experiment with different loop structures to achieve the desired output
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Q1. Tell me about yourself
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Q2. Why are you want to join us
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Q1. Sql queries, python program and my clg background
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Q2. Star pattern, codding and sql question oral
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Q1. Joins , sub queries
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Q2. Trigger, group by
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Programing questiion star pattern