Equifax Data Engineer Interview Questions and Answers

Q1. What are different type of joins available in Databricks?
Ans. 
Different types of joins available in Databricks include inner join, outer join, left join, right join, and cross join.
• Inner join: Returns only the rows that have matching values in both tables.

• Outer join: Returns all rows when there is a match in either table.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows ...read more
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Q2. How do you make your data pipeline fault tolerant?
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Implementing fault tolerance in a data pipeline involves redundancy, monitoring, and error handling.
• Use redundant components to ensure continuous data flow

• Implement monitoring tools to detect failures and bottlenecks

• Set up automated alerts for immediate response to issues

• Design error handling mechanisms to gracefully handle failures

• Use checkpoints and retries to ensure data integrity
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Q3. What is AutoLoader?
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AutoLoader is a feature in data engineering that automatically loads data from various sources into a data warehouse or database.
• Automates the process of loading data from different sources

• Reduces manual effort and human error

• Can be scheduled to run at specific intervals

• Examples: Apache Nifi, AWS Glue
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Q1. How do you connect to different services in Azure?
Ans. 
To connect to different services in Azure, you can use Azure SDKs, REST APIs, Azure Portal, Azure CLI, and Azure PowerShell.
• Use Azure SDKs for programming languages like Python, Java, C#, etc.

• Utilize REST APIs to interact with Azure services programmatically.

• Access and manage services through the Azure Portal.

• Leverage Azure CLI for command-line interface interactions.

• Automate tasks using Azure PowerShell scripts.
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Q2. What are linked Services?
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Linked Services are connections to external data sources or destinations in Azure Data Factory.
• Linked Services define the connection information needed to connect to external data sources or destinations.

• They can be used in Data Factory pipelines to read from or write to external systems.

• Examples of Linked Services include Azure Blob Storage, Azure SQL Database, and Amazon S3.
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Q1. Explain adf questions in detail
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ADF questions refer to Azure Data Factory questions which are related to data integration and data transformation processes.
• ADF questions are related to Azure Data Factory, a cloud-based data integration service.

• These questions may involve data pipelines, data flows, activities, triggers, and data movement.

• Candidates may be asked about their experience with designing, monitoring, and managing data pipelines in ADF.

• Exam...read more
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Q2. Project related questions
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Q1. Project data related questions
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Q2. Databricks and SQL interview questions
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Q1. What are the optimization techniques used in Apache Spark?
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Q2. 2 SQL queries , 1 PySpark code and 1 Python Code .
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Q3. 2-3 Scenario Based questions from ADF and databricks .
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Aptitude test involved with quantative aptitude, logical reasoning and reading comprehensions.

Q1. Tell me your introduction.
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Q2. Tell me about your skills.
Ans. 
I have strong skills in data processing, ETL, data modeling, and programming languages like Python and SQL.
• Proficient in data processing and ETL techniques

• Strong knowledge of data modeling and database design

• Experience with programming languages like Python and SQL

• Familiarity with big data technologies such as Hadoop and Spark
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Q1. Are you ready relocate,?
Ans. 
Yes, I am open to relocating for the right opportunity.
• I am willing to relocate for the right job opportunity.

• I have experience moving for previous roles.

• I am flexible and adaptable to new locations.

• I am excited about the possibility of exploring a new city or country.
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Q2. Document verification
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Q1. About python, sql, pyspark
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Q2. Spark Architecture.
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Q1. When can you join.
Ans. 
I can join within two weeks of receiving an offer.
• I can start within two weeks of receiving an offer.

• I need to give notice at my current job before starting.

• I have some personal commitments that I need to wrap up before joining.
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Q2. .
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Q1. Data engineer roles and resposibilities
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Its mandatory test even for experience people

Q1. Related to technology
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Q1. Very good discussion towards work culture, salary and all
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Q1. Tell me about yourself
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Q2. Project Architecture
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Q3. Rate yourself out of 5 in Pyspark , Python and SQL
Ans. 
I would rate myself 4 in Pyspark, 5 in Python, and 4 in SQL.
• Strong proficiency in Python programming language

• Experience in working with Pyspark for big data processing

• Proficient in writing complex SQL queries for data manipulation

• Familiarity with optimizing queries for performance

• Hands-on experience in data engineering projects
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Q4. How to handle duplicates in python ?
Ans. 
Use Python's built-in data structures like sets or dictionaries to handle duplicates.
• Use a set to remove duplicates from a list: unique_list = list(set(original_list))

• Use a dictionary to remove duplicates from a list while preserving order: unique_list = list(dict.fromkeys(original_list))
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Q5. Methods of migrating Hive metdatastore to unity catalog in Databricks ?
Ans. 
Use Databricks provided tools like databricks-connect and databricks-cli to migrate Hive metadata to Unity catalog.
• Use databricks-connect to connect to the Databricks workspace from your local development environment.

• Use databricks-cli to export the Hive metadata from the existing Hive metastore.

• Create a new Unity catalog in Databricks and import the exported metadata using databricks-cli.

• Validate the migration by chec...read more
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Q6. Read a CSV file from ADLS path ?
Ans. 
To read a CSV file from an ADLS path, you can use libraries like pandas or pyspark.
• Use pandas library in Python to read a CSV file from ADLS path

• Use pyspark library in Python to read a CSV file from ADLS path

• Ensure you have the necessary permissions to access the ADLS path
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Q7. There was a table provided on coding screen and asked to write different programs and SQL queries from the table and tell the approach you are taking ? Like age greater than 30 then sum the age how would y...read more
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Q8. How many stages will create from the above code that I have written
Ans. 
The number of stages created from the code provided depends on the specific code and its functionality.
• The number of stages can vary based on the complexity of the code and the specific tasks being performed.

• Stages may include data extraction, transformation, loading, and processing.

• It is important to analyze the code and identify distinct stages to determine the total number.
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Q9. Narrow vs Wide Transformation ?
Ans. 
Narrow transformation processes one record at a time, while wide transformation processes multiple records at once.
• Narrow transformation processes one record at a time, making it easier to parallelize and optimize.

• Wide transformation processes multiple records at once, which can lead to shuffling and performance issues.

• Examples of narrow transformations include map and filter operations, while examples of wide transfor
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Q10. What are action and transformation ?
Ans. 
Actions and transformations are key concepts in data engineering, involving the manipulation and processing of data.
• Actions are operations that trigger the execution of a data transformation job in a distributed computing environment.

• Transformations are functions that take an input dataset and produce an output dataset, often involving filtering, aggregating, or joining data.

• Examples of actions include 'saveAsTextFile'...read more
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Q11. What happens when we enforce the schema and when we manually define the schema in the code ?
Ans. 
Enforcing the schema ensures data consistency and validation, while manually defining the schema in code allows for more flexibility and customization.
• Enforcing the schema ensures that all data conforms to a predefined structure and format, preventing errors and inconsistencies.

• Manually defining the schema in code allows for more flexibility in handling different data types and structures.

• Enforcing the schema can be do...read more
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Q12. What all the optimisation are possible to reduce the overhead of reducing the reading part of large datasets in spark ?
Ans. 
Optimizations like partitioning, caching, and using efficient file formats can reduce overhead in reading large datasets in Spark.
• Partitioning data based on key can reduce the amount of data shuffled during joins and aggregations

• Caching frequently accessed datasets in memory can avoid recomputation

• Using efficient file formats like Parquet or ORC can reduce disk I/O and improve read performance
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Q13. Write a sql query to find the name of person who logged in last within each country from Person Table ?
Ans. 
SQL query to find the name of person who logged in last within each country from Person Table
• Use a subquery to find the max login time for each country

• Join the Person table with the subquery on country and login time to get the name of the person
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Q14. Difference between List and Tuple ?
Ans. 
List is mutable, Tuple is immutable in Python.
• List can be modified after creation, Tuple cannot be modified.

• List is defined using square brackets [], Tuple is defined using parentheses ().

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)
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Q15. Difference between Rank , Dense Rank and Row Number and when we are using each of them ?
Ans. 
Rank assigns a unique rank to each row, Dense Rank assigns a unique rank to each distinct row, and Row Number assigns a unique number to each row.
• Rank assigns the same rank to rows with the same value, leaving gaps in the ranking if there are ties.

• Dense Rank assigns a unique rank to each distinct row, leaving no gaps in the ranking.

• Row Number assigns a unique number to each row, without any regard for the values in the...read more
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Q16. What is List Comprehension ?
Ans. 
List comprehension is a concise way to create lists in Python by applying an expression to each item in an iterable.
• Syntax: [expression for item in iterable]

• Can include conditions: [expression for item in iterable if condition]

• Example: squares = [x**2 for x in range(10)]
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Q17. Tell me about the performance optimization done in your project ?
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Q18. Difference between the interactive cluster and job cluster ?
Ans. 
Interactive clusters allow for real-time interaction and exploration, while job clusters are used for running batch jobs.
• Interactive clusters are used for real-time data exploration and analysis.

• Job clusters are used for running batch jobs and processing large amounts of data.

• Interactive clusters are typically smaller in size and have shorter lifespans.

• Job clusters are usually larger and more powerful to handle heavy w...read more
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Q19. How to add a column in dataframe ? How to rename the column in dataframe ?
Ans. 
To add a column in a dataframe, use the 'withColumn' method. To rename a column, use the 'withColumnRenamed' method.
• To add a column, use the 'withColumn' method with the new column name and the expression to compute the values for that column.

• Example: df.withColumn('new_column', df['existing_column'] * 2)

• To rename a column, use the 'withColumnRenamed' method with the current column name and the new column name.

• Example:...read more
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Q20. Difference between Coalesce and Repartition and In which case we are using it ?
Ans. 
Coalesce is used to combine multiple small partitions into a larger one, while Repartition is used to increase or decrease the number of partitions in a DataFrame.
• Coalesce reduces the number of partitions in a DataFrame by combining small partitions into larger ones.

• Repartition increases or decreases the number of partitions in a DataFrame by shuffling the data across partitions.

• Coalesce is more efficient than Repartit...read more
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