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10+ Fresenius Kabi Interview Questions and Answers

Updated 13 Jan 2025
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Q1. how to migrate 1000s of tables using spark(databricks) notebooks

Ans.

Use Spark (Databricks) notebooks to migrate 1000s of tables efficiently.

  • Utilize Spark's parallel processing capabilities to handle large volumes of data

  • Leverage Databricks notebooks for interactive data exploration and transformation

  • Automate the migration process using scripts or workflows

  • Optimize performance by tuning Spark configurations and cluster settings

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Q2. What is the process for finding the missing number from a list?

Ans.

To find the missing number from a list, calculate the sum of all numbers in the list and subtract it from the expected sum of the list.

  • Calculate the sum of all numbers in the list using a loop or a built-in function.

  • Calculate the expected sum of the list using the formula n*(n+1)/2, where n is the length of the list.

  • Subtract the sum of the list from the expected sum to find the missing number.

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Q3. Dataflow vs Dataproc, layering processing and curated environments in gcp , Data cleaning

Ans.

Dataflow and Dataproc are both processing services in GCP, but with different approaches and use cases.

  • Dataflow is a fully managed service for executing batch and streaming data processing pipelines.

  • Dataproc is a managed Spark and Hadoop service for running big data processing and analytics workloads.

  • Dataflow provides a serverless and auto-scaling environment, while Dataproc offers more control and flexibility.

  • Dataflow is suitable for real-time streaming and complex data tran...read more

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Q4. What are some methods for optimizing Spark performance?

Ans.

Optimizing Spark performance involves tuning configurations, partitioning data, caching, and using efficient transformations.

  • Tune Spark configurations for memory allocation, parallelism, and resource management.

  • Partition data properly to distribute work evenly across nodes and minimize shuffling.

  • Cache intermediate results in memory to avoid recomputation.

  • Use efficient transformations like map, filter, and reduceByKey instead of costly operations like groupByKey.

  • Opt for column...read more

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Q5. end to end project architecture.

Ans.

The end-to-end project architecture involves designing and implementing the entire data pipeline from data ingestion to data visualization.

  • Data ingestion: Collecting data from various sources such as databases, APIs, and files.

  • Data processing: Cleaning, transforming, and aggregating the data using tools like Apache Spark or Hadoop.

  • Data storage: Storing the processed data in data warehouses or data lakes like Amazon S3 or Google BigQuery.

  • Data analysis: Performing analysis on t...read more

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Q6. delete duplicates from table in spark and sql

Ans.

To delete duplicates from a table in Spark and SQL, you can use the DISTINCT keyword or the dropDuplicates() function.

  • In SQL, you can use the DISTINCT keyword in a SELECT statement to retrieve unique rows from a table.

  • In Spark, you can use the dropDuplicates() function on a DataFrame to remove duplicate rows.

  • Both methods compare all columns by default, but you can specify specific columns to consider for duplicates.

  • You can also use the partitionBy() function in Spark to remov...read more

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Q7. expectations from EPAM

Ans.

I expect EPAM to provide challenging projects, opportunities for growth, a collaborative work environment, and support for continuous learning.

  • Challenging projects that allow me to utilize my skills and knowledge

  • Opportunities for professional growth and advancement within the company

  • A collaborative work environment where teamwork is valued

  • Support for continuous learning through training programs and resources

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Q8. types of transformations,no of jobs,tasks,actions

Ans.

The question is asking about types of transformations, number of jobs, tasks, and actions in the context of a Senior Data Engineer role.

  • Types of transformations: Extract, Transform, Load (ETL), MapReduce, Spark transformations, SQL transformations

  • Number of jobs: Depends on the complexity and scale of the data engineering projects

  • Number of tasks: Varies based on the number of data sources, data transformations, and data destinations

  • Actions: Data ingestion, data cleaning, data ...read more

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Q9. optimisation in spark,sql,bigquery,airflow

Ans.

Optimization techniques in Spark, SQL, BigQuery, and Airflow.

  • Use partitioning and bucketing in Spark to optimize data processing.

  • Optimize SQL queries by using indexes, query rewriting, and query optimization techniques.

  • In BigQuery, use partitioning and clustering to improve query performance.

  • Leverage Airflow's task parallelism and resource allocation to optimize workflow execution.

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Q10. architecture of spark,airflow,bigquery,

Ans.

Spark is a distributed processing engine, Airflow is a workflow management system, and BigQuery is a fully managed data warehouse.

  • Spark is designed for big data processing and provides in-memory computation capabilities.

  • Airflow is used for orchestrating and scheduling data pipelines.

  • BigQuery is a serverless data warehouse that allows for fast and scalable analytics.

  • Spark can be integrated with Airflow to schedule and monitor Spark jobs.

  • BigQuery can be used as a data source or...read more

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Interview Process at Fresenius Kabi

based on 12 interviews
3 Interview rounds
Technical Round - 1
Technical Round - 2
Technical Round - 3
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