GlobalLogic Data Associate Interview Questions and Answers

The aptitude test lasts 30 minutes and focuses on topics relevant to data engineering, including Spark, SQL, Azure, and PySpark.

The coding test is a one-hour examination on PySpark.

Q1. What is the difference between Cache() and Persist()?
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Q2. What does the purpose of the Spark Submit command in Apache Spark?
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Q3. What are window functions in SQL?
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Q1. Could you provide more details about the daily responsibilities associated with this role?
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Q2. How would you describe your work culture?
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Q1. What is the architecture of Apache Spark?
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Apache Spark architecture includes a cluster manager, worker nodes, and driver program.
• Apache Spark architecture consists of a cluster manager, which allocates resources and schedules tasks.

• Worker nodes execute tasks and store data in memory or disk.

• Driver program coordinates tasks and communicates with the cluster manager.

• Spark applications run as independent sets of processes on a cluster, coordinated by the SparkCon...read more
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Q2. What is the difference between the reduceBy and groupBy transformations in Apache Spark?
Ans. 
reduceBy is used to aggregate data based on key, while groupBy is used to group data based on key.
• reduceBy is a transformation that combines the values of each key using an associative function and a neutral 'zero value'.

• groupBy is a transformation that groups the data based on a key and returns a grouped data set.

• reduceBy is more efficient for aggregating data as it reduces the data before shuffling, while groupBy shu...read more
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Q3. What is the difference between RDD (Resilient Distributed Datasets) and DataFrame in Apache Spark?
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RDD is a low-level abstraction representing a distributed collection of objects, while DataFrame is a higher-level abstraction representing a distributed collection of data organized into named columns.
• RDD is more suitable for unstructured data and low-level transformations, while DataFrame is more suitable for structured data and high-level abstractions.

• DataFrames provide optimizations like query optimization and code...read more
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Q4. What are the different modes of execution in Apache Spark?
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The different modes of execution in Apache Spark include local mode, standalone mode, YARN mode, and Mesos mode.
• Local mode: Spark runs on a single machine with one executor.

• Standalone mode: Spark runs on a cluster managed by a standalone cluster manager.

• YARN mode: Spark runs on a Hadoop cluster using YARN as the resource manager.

• Mesos mode: Spark runs on a Mesos cluster with Mesos as the resource manager.
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Q1. Sql queries pandas coding
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Q2. Some questions related to power bi
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Q1. Salary discussion and feedback
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Q1. How can you design an Azure Data Factory pipeline to copy data from a folder containing files with different delimiters to another folder?
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Q2. Write a pyspark program that reads multiple csv files and creates a data frame with count of records against each file
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Q1. DAX Related Syntax and Codes
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Q2. Data Modelling, SQL, Python
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Q1. No Response from HR after calling of selection after Round 1
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Q1. What is the importance of data analysis?
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Data analysis is crucial for making informed decisions, identifying trends, and solving complex problems.
• Helps in making informed decisions based on data-driven insights

• Identifies trends and patterns that can lead to strategic business decisions

• Aids in solving complex problems by analyzing data to find root causes

• Improves efficiency and effectiveness of processes through data-driven optimizations
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Q2. What steps should be taken to become a data analyst?
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To become a data analyst, one should focus on acquiring relevant education, gaining experience with data analysis tools, and developing strong analytical and problem-solving skills.
• Obtain a degree in a related field such as statistics, mathematics, computer science, or data science.

• Gain experience with data analysis tools such as SQL, Python, R, and Excel.

• Develop strong analytical and problem-solving skills by working ...read more
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Q1. How do you utilize the enhanced optimization option in AWS Glue?
Ans. 
Enhanced optimization in AWS Glue improves job performance by automatically adjusting resources based on workload
• Enhanced optimization in AWS Glue automatically adjusts resources like DPUs based on workload

• It helps improve job performance by optimizing resource allocation

• Users can enable enhanced optimization in AWS Glue job settings
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Q2. What are the best practices for optimizing querying in Amazon Redshift?
Ans. 
Optimizing querying in Amazon Redshift involves proper table design, distribution keys, sort keys, and query optimization techniques.
• Use appropriate distribution keys to evenly distribute data across nodes for parallel processing.

• Utilize sort keys to physically order data on disk, reducing the need for sorting during queries.

• Avoid using SELECT * and instead specify only the columns needed to reduce data transfer.

• Use AN...read more
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Database Management system SQL and PlSQL

Database Base Management system SQL and PlSQL

Database Management system

Database Management system

Database Management system

Database Management system

Database Base Management system

Q1. Database Management system
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Q2. SQL and PlSQL Mango Data And Manu Database Management system
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Q3. C Language and C++Language and Java
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Q4. Data Analysis and Data entry
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Q5. DBMS C,C++ Java Data Entry Ms Excel Ms Word Ms PP
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