30+ Maruti Exim Interview Questions and Answers

Logic for distributing cards among 4 boxes in a circular manner.

• Use modulo operator to distribute cards among boxes in a circular manner.

• If card number is divisible by 4, assign it to box 4.

• If card number is divisible by 3, assign it to box 3.

• If card number is divisible by 2, assign it to box 2.

• If card number is not divisible by any of the above, assign it to box 1.

Client mode is better for very less latency due to direct communication with the cluster.

• Client mode allows direct communication with the cluster, reducing latency.

• Standalone mode requires an additional layer of communication, increasing latency.

• Client mode is preferred for real-time applications where low latency is crucial.

When a spark job is submitted, various steps are executed at the backend to process the job.

• The job is submitted to the Spark driver program.

• The driver program communicates with the cluster manager to request resources.

• The cluster manager allocates resources (CPU, memory) to the job.

• The driver program creates DAG (Directed Acyclic Graph) of the job stages and tasks.

• Tasks are then scheduled and executed on worker nodes in the cluster.

• Intermediate results are stored in memory o...read more

Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.

• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my project, I optimized Spark performance by increasing executor me...read more

Optimizing SQL queries involves using indexes, avoiding unnecessary joins, and optimizing the query structure.

• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Use EXPLAIN to analyze query performance and make necessary adjustments

Calculate second highest salary using SQL and pyspark

• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• In pyspark, use orderBy() and take() functions to achieve the same result

The two types of modes for Spark architecture are standalone mode and cluster mode.

• Standalone mode: Spark runs on a single machine with a single JVM and is suitable for development and testing.

• Cluster mode: Spark runs on a cluster of machines managed by a cluster manager like YARN or Mesos for production workloads.

Factors to consider when designing a road curve

• Radius of the curve

• Speed limit of the road

• Banking of the curve

• Visibility around the curve

• Traffic volume on the road

• Road surface conditions

• Presence of obstacles or hazards

• Environmental factors such as weather conditions

I have worked on projects involving building data pipelines, optimizing data storage, and implementing data processing algorithms.

• Built data pipelines to extract, transform, and load data from various sources

• Optimized data storage by implementing efficient database schemas and indexing strategies

• Implemented data processing algorithms for real-time and batch processing

• Worked on data quality monitoring and data governance initiatives

SparkContext is the main entry point for Spark functionality, while SparkSession is the entry point for Spark SQL.

• SparkContext is the entry point for low-level API functionality in Spark.

• SparkSession is the entry point for Spark SQL functionality.

• SparkContext is used to create RDDs (Resilient Distributed Datasets) in Spark.

• SparkSession provides a unified entry point for reading data from various sources and performing SQL queries.

Choosing the right technology depends on the specific requirements of the situation.

• Consider the data size and complexity

• Evaluate the processing speed and scalability

• Assess the cost and availability of the technology

• Take into account the skillset of the team

• Examples: Hadoop for big data, Spark for real-time processing, AWS for cloud-based solutions

As a data engineer, I have experience with tools like Apache Spark, Hadoop, and SQL. Challenges include data quality issues and scalability.

• Experience with Apache Spark for processing large datasets

• Proficiency in Hadoop for distributed storage and processing

• Strong SQL skills for querying and manipulating data

• Challenges include dealing with data quality issues

• Challenges with scalability as data volume grows

Snowflake features include automatic scaling, zero-copy cloning, and data sharing.

• Automatic scaling allows for seamless adjustment of compute resources based on workload demands.

• Zero-copy cloning enables quick and efficient creation of copies of data without duplicating storage.

• Data sharing feature allows for secure and controlled sharing of data across different accounts or regions.

Optimizing SSIS package involves reducing memory usage, improving data flow, and using efficient transformations.

• Use data flow task instead of multiple transformations

• Use buffer size optimization

• Use fast load option for bulk data transfer

• Avoid using unnecessary columns in data flow

• Use parallelism for faster execution

• Use appropriate data types for columns

• Use indexes for faster lookup

• Use logging and error handling for debugging

• Use connection managers efficiently

Encapsulation is the concept of bundling data and methods that operate on the data into a single unit.

• Encapsulation helps in hiding the internal state of an object and restricting access to it.

• It allows for better control over the data by preventing direct access from outside the class.

• An example of encapsulation is a class in object-oriented programming that has private variables and public methods to access and modify those variables.

Python is a high-level programming language known for its simplicity, readability, and versatility.

• Python is preferred for data engineering due to its ease of use and readability, making it easier to write and maintain code.

• It has a large number of libraries and frameworks specifically designed for data processing and analysis, such as Pandas, NumPy, and SciPy.

• Python's flexibility allows for seamless integration with other languages and tools commonly used in data engineering...read more

Spark internal mechanism involves Directed Acyclic Graph (DAG) for task execution. Tasks are units of work performed on data.

• Spark uses DAG to represent the logical flow of operations in a job

• DAG is a series of vertices and edges where vertices represent RDDs and edges represent operations to be applied on RDDs

• Tasks are individual units of work within a stage, executed on a partition of data

• Tasks are scheduled by the Spark scheduler based on dependencies and available resourc...read more

Day to day activities in a data engineering project involve data collection, processing, analysis, and maintenance.

• Collecting and storing data from various sources

• Cleaning and transforming data for analysis

• Building and maintaining data pipelines

• Collaborating with data scientists and analysts

• Monitoring and optimizing data infrastructure

• Implementing data security and privacy measures

Challenges in Snowflake include managing costs, data governance, and data integration.

• Managing costs can be a challenge due to the pay-per-second pricing model of Snowflake.

• Ensuring proper data governance and security measures is crucial in Snowflake.

• Data integration can be complex when dealing with multiple data sources and formats in Snowflake.

The 4 pillars in DSA are Data Structures, Algorithms, Problem Solving, and Coding.

• Data Structures - organizing and storing data effectively, examples include arrays, linked lists, trees

• Algorithms - step-by-step procedures for solving problems, examples include sorting algorithms like quicksort, mergesort

• Problem Solving - analyzing problems and devising solutions, examples include dynamic programming, greedy algorithms

• Coding - implementing solutions in a programming language, ...read more

Time travel and fail safe are concepts in data engineering related to managing data backups and ensuring data integrity.

• Time travel refers to the ability to access historical versions of data to track changes over time.

• Fail safe mechanisms ensure that data is backed up and can be recovered in case of system failures or data corruption.

• Examples of fail safe practices include regular backups, redundancy in storage systems, and data validation checks.

• Time travel can be implement...read more

Using PySpark to handle different file formats

• Use PySpark's built-in functions to read and write different file formats such as CSV, Parquet, JSON, etc.

• Specify the file format when reading data using PySpark's read method, for example: spark.read.format('csv').load('file.csv')

• When writing data, specify the file format using PySpark's write method, for example: df.write.format('parquet').save('file.parquet')

I rate myself highly in SQL and Snowflake, with extensive experience in both technologies.

• Proficient in writing complex SQL queries for data manipulation and analysis

• Skilled in optimizing queries for performance and efficiency

• Experienced in working with Snowflake for data warehousing and analytics

• Familiar with Snowflake's unique features such as virtual warehouses and data sharing

Implement a Word Count program in Spark Scala

• Use Spark's RDD API to read input text file

• Split each line into words and map them to key-value pairs

• ReduceByKey operation to count occurrences of each word

• Save the result to an output file

PySpark architecture refers to the structure and components of the PySpark framework for processing big data using Apache Spark.

• PySpark architecture includes components like Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• It follows a master-slave architecture with a driver program that communicates with a cluster manager to distribute tasks.

• Data is processed in parallel using Resilient Distributed Datasets (RDDs) and transformations like map, reduce, filter, etc.

• Py...read more

List is mutable, tuple is immutable in Python.

• List can be modified after creation, tuple cannot.

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

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)

Code to sort an array of strings

• Use the built-in sort() function in the programming language of your choice

• If case-insensitive sorting is required, use a custom comparator

• Consider the time complexity of the sorting algorithm used

Higher Order Functions in Scala are functions that take other functions as parameters or return functions as results.

• Higher Order Functions allow for more concise and readable code.

• Examples include map, filter, reduce, and flatMap in Scala.

• They promote code reusability and modularity.

• Higher Order Functions are a key feature of functional programming.

SQL streams in pyspark allow for real-time processing of data streams using SQL queries.

• SQL streams in pyspark enable continuous processing of data streams using SQL queries

• It allows for real-time analysis and transformation of streaming data

• Example: SELECT * FROM stream_table WHERE value > 100

Hive External vs managed

• Hive External tables store data outside of the Hive warehouse directory

• Managed tables store data in the Hive warehouse directory

• External tables can be used to access data from different storage systems

• Managed tables are easier to manage as Hive takes care of data storage and metadata

• External tables require manual management of data and metadata

I have 5 years of experience working as a Data Engineer in various industries.

• Developed ETL pipelines to extract, transform, and load data from multiple sources into a data warehouse

• Optimized database performance by tuning queries and indexes

• Implemented data quality checks to ensure accuracy and consistency of data

• Worked with cross-functional teams to design and implement data solutions for business needs