Capgemini Data Engineer Interview Questions and Answers

Q1. Describe yourself
Ans. 
I am a detail-oriented data engineer with a passion for problem-solving and a strong background in programming and data analysis.
• Experienced in designing and implementing data pipelines

• Proficient in programming languages such as Python, SQL, and Java

• Skilled in data modeling and database management

• Strong analytical skills and ability to work with large datasets

• Excellent communication and teamwork skills
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Q2. Summary of your formation
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I have a Bachelor's degree in Computer Science and a Master's degree in Data Engineering.
• Bachelor's degree in Computer Science

• Master's degree in Data Engineering
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Some DSA problems medium level

Q1. Spark archi, hadoop ecosystem, hive
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Q2. Some sql questions also
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Q1. Find the second largest
Ans. 
To find the second largest element in an array
• Sort the array in descending order

• Return the element at index 1
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Q2. Drop duplicates
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Dropping duplicates involves removing repeated entries from a dataset to ensure data integrity and accuracy.
• Use the 'drop_duplicates()' method in pandas to remove duplicate rows from a DataFrame.

• Example: df.drop_duplicates(subset=['column_name'], keep='first') removes duplicates based on 'column_name'.

• In SQL, use 'SELECT DISTINCT' to retrieve unique records from a table.

• Example: SELECT DISTINCT column_name FROM table_n...read more
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Numeric ability , reasoning, maths

Python , data structures, c , c++, java

Q1. Python to write a factorial
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Python code to calculate factorial of a number
• Use a recursive function to calculate the factorial

• Base case: if n is 0 or 1, return 1

• Recursive case: return n * factorial(n-1)

• Example: def factorial(n): return 1 if n == 0 or n == 1 else n * factorial(n-1)
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Q2. Ddl commands and dml
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Q1. Tell me about yourself
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Q2. Why IT after electrical engineering
Ans. 
Combining my electrical engineering background with IT skills allows me to work on cutting-edge technologies and solve complex problems.
• Interest in technology and data analysis sparked during electrical engineering studies

• Realized the potential of combining electrical engineering knowledge with IT for innovative solutions

• Opportunities in data engineering field align with my career goals
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Q1. Optimization technique in pyspark
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One optimization technique in PySpark is using partitioning to distribute data evenly across nodes.
• Use partitioning to distribute data evenly across nodes

• Avoid shuffling data unnecessarily

• Cache intermediate results to avoid recomputation
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Q2. How to handle data skewness ?
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Data skewness can be handled by partitioning data, using sampling techniques, optimizing queries, and using parallel processing.
• Partitioning data based on key values to distribute workload evenly

• Using sampling techniques to estimate skewed data distribution

• Optimizing queries by using appropriate indexes and query optimization techniques

• Using parallel processing to distribute workload across multiple nodes
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Q1. Self Introduction and Project Details
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Q2. What is ADF
Ans. 
Azure Data Factory (ADF) is a cloud-based data integration service for creating data-driven workflows.
• ADF allows for the creation of ETL (Extract, Transform, Load) processes.

• It supports data movement between various sources like Azure Blob Storage, SQL databases, and on-premises data.

• ADF provides a visual interface for designing data pipelines, making it user-friendly.

• It integrates with other Azure services like Azure ...read more
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Q3. Repartition and Coalesce
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Q4. Spark Architecture
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Q5. Wide and Narrow Transformations
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Q6. Window functions in sql and pyspark
Ans. 
Window functions in SQL and PySpark allow for advanced data analysis over specified ranges of rows.
• Window functions perform calculations across a set of table rows related to the current row.

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), SUM(), AVG(), etc.

• In SQL: SELECT employee_id, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank FROM employees;

• In PySpark: df.withColumn('salary_rank', F.r...read more
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Q1. Write a query to find the 2nd highest salary.
Ans. 
Query to find the 2nd highest salary in a database table.
• Use the ORDER BY clause to sort salaries in descending order.

• Use the LIMIT clause to retrieve the second row.

• Consider handling cases where there may be ties for the highest salary.
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Q2. Write a code to count frequency of elements in a list.
Ans. 
Code to count frequency of elements in a list of strings.
• Use a dictionary to store the frequency of each element in the list.

• Iterate through the list and update the count in the dictionary.

• Return the dictionary with element frequencies.
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