Coforge Technical Analyst Interview Questions and Answers

Q1. Explain Spark Architecture.
Ans. 
Apache Spark is a distributed computing system designed for fast data processing and analytics.
• Spark operates on a master-slave architecture with a Driver and Executors.

• The Driver program coordinates the execution of tasks and maintains the SparkContext.

• Executors are worker nodes that execute tasks and store data in memory for fast access.

• Spark uses Resilient Distributed Datasets (RDDs) for fault tolerance and parallel...read more
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Q2. What are the different optimization techniques in Spark.
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Spark optimization techniques enhance performance by improving resource utilization and reducing execution time.
• 1. Catalyst Optimizer: Automatically optimizes query plans in Spark SQL, improving execution efficiency.

• 2. Tungsten Execution Engine: Focuses on memory management and code generation for better performance.

• 3. Data Serialization: Use efficient serialization formats like Kryo to reduce data transfer time.

• 4. Bro...read more
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Q3. Difference between SparkSession and SparkContext.
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SparkSession is the entry point for Spark SQL, while SparkContext is the entry point for Spark Core functionalities.
• SparkSession encapsulates SparkContext and provides a unified entry point for DataFrame and SQL operations.

• SparkContext is used to connect to a Spark cluster and is the primary interface for Spark Core functionalities.

• You can create a SparkSession using: `SparkSession.builder.appName('example').getOrCreat...read more
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Q4. What are different read and write modes.
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Read and write modes define how data is accessed and modified in files or streams, impacting data integrity and performance.
• Read Mode (r): Opens a file for reading only. Example: 'file = open('data.txt', 'r')'

• Write Mode (w): Opens a file for writing, truncating the file if it exists. Example: 'file = open('data.txt', 'w')'

• Append Mode (a): Opens a file for writing, appending data to the end without truncating. Example: ...read more
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Q1. What are the challenges you faced in previous project.
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Faced challenges in data accuracy, stakeholder communication, and adapting to market changes in previous projects.
• Data Accuracy: Encountered discrepancies in historical data which required extensive validation and cleaning before analysis.

• Stakeholder Communication: Misalignment with stakeholders on project goals led to revisions; implemented regular updates to ensure clarity.

• Market Changes: Rapid shifts in market trend...read more
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Q2. What are your preferences whether Azure or GCP.
Ans. 
Both Azure and GCP have unique strengths; preference depends on specific project needs and organizational goals.
• Azure offers seamless integration with Microsoft products, ideal for enterprises using Windows Server and SQL Server.

• GCP excels in data analytics and machine learning, with tools like BigQuery and TensorFlow for advanced data processing.

• Azure has a strong hybrid cloud strategy, allowing businesses to integrat...read more
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Q1. Create Custom hook to fetch data
Ans. 
Custom hook to fetch data in React
• Create a custom hook using the 'useEffect' and 'useState' hooks

• Use 'fetch' or any other method to fetch data from an API

• Return the fetched data from the custom hook
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Q2. Javascript concept and output
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Q1. React hooks and lifecycle
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Q2. Custom hook and some tricky output question
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Good and confident level increaser

Write a program to convert digits in to numbers

Q1. Scenario of data migration
Ans. 
Data migration involves transferring data from one system to another while ensuring data integrity and consistency.
• Plan the migration process carefully to minimize downtime and data loss.

• Backup all data before starting the migration process.

• Verify data integrity after migration to ensure all data has been successfully transferred.

• Consider using tools or scripts to automate the migration process.

• Communicate with stakeho...read more
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Q1. 1. Tell me about yourself?
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Q2. Types of partitioning in gcp big query?
Ans. 
GCP BigQuery supports time-based and integer range partitioning for efficient data management and querying.
• Time-based partitioning: Automatically partitions data based on a TIMESTAMP or DATE column. Example: daily partitions for sales data.

• Integer range partitioning: Divides data into ranges based on an INTEGER column. Example: partitioning user IDs into ranges.

• Partitioned tables improve query performance and reduce co...read more
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Q3. Sql query to find the third highest salary of employee in each department?
Ans. 
Retrieve the third highest salary of employees within each department using SQL queries with ranking functions.
• Use of RANK() or DENSE_RANK(): These functions help in assigning a rank to each salary within a department.

• Common Table Expression (CTE): A CTE can be used to simplify the query structure and improve readability.

• Example Query: WITH RankedSalaries AS (SELECT department_id, salary, DENSE_RANK() OVER (PARTITION B...read more
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Q4. How do you load a csv file to big query?
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Loading a CSV file to BigQuery can be done using the web UI, command line, or API.
• Use the BigQuery web UI: Navigate to BigQuery, select your dataset, click 'Create Table', and upload your CSV file.

• Command-line tool: Use the bq command with 'bq load' command. Example: bq load --source_format=CSV dataset.table gs://bucket/file.csv

• Using Python client library: Use 'google-cloud-bigquery' to load CSV. Example: client.load_t...read more
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Q5. Upsertion in gcp big query?
Ans. 
Upsertion in GCP BigQuery allows for efficient data updates and inserts using SQL syntax.
• Upsertion combines INSERT and UPDATE operations based on whether a record exists.

• Use the MERGE statement for upsertion: MERGE INTO target_table USING source_table ON condition.

• Example: MERGE INTO target_table USING source_table ON target.id = source.id WHEN MATCHED THEN UPDATE SET target.value = source.value WHEN NOT MATCHED THEN I...read more
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Q6. How do you debug a long running sql query in gcp big query?
Ans. 
Debugging long-running SQL queries in GCP BigQuery involves analyzing execution plans, optimizing queries, and monitoring performance.
• Use the BigQuery Query Execution Details to analyze the execution plan and identify bottlenecks.

• Check for large data scans; use SELECT statements to limit the amount of data processed.

• Optimize joins by ensuring that you are using the correct join types and filtering data early.

• Consider u...read more
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Q7. About dataproc in gcp?
Ans. 
Google Cloud Dataproc is a fully managed service for running Apache Spark and Hadoop clusters in the cloud.
• Quickly create and manage clusters for big data processing.

• Supports popular open-source tools like Apache Spark, Hadoop, and Hive.

• Integrates seamlessly with other GCP services like BigQuery and Cloud Storage.

• Allows for autoscaling of clusters based on workload demands.

• Example: Use Dataproc to process large dataset...read more
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Q8. Avro and parquet file difference in gcp?
Ans. 
Avro and Parquet are both columnar storage formats used in GCP, each with unique features and use cases.
• Avro is a row-based storage format, while Parquet is a columnar storage format.

• Avro is best for write-heavy operations and supports schema evolution, making it suitable for streaming data.

• Parquet is optimized for read-heavy operations and is efficient for analytical queries, making it ideal for big data processing.

• Av...read more
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Q9. There is an employee to ceo level complete hierarchy in employee table, sql query to find the complete hierarchy of a given employee? Result should be in one row only.
Ans. 
SQL query to retrieve the complete hierarchy of an employee in a single row from an employee table.
• Use a Common Table Expression (CTE) to recursively fetch the hierarchy.

• Example CTE: WITH RECURSIVE EmployeeHierarchy AS (SELECT * FROM employee WHERE id = ? UNION ALL SELECT e.* FROM employee e INNER JOIN EmployeeHierarchy eh ON e.manager_id = eh.id) SELECT * FROM EmployeeHierarchy;

• Replace '?' with the employee's ID to ge...read more
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Q10. Employee table has some missing employee id records which normally follow a sequence for employee id ranging from suppose say 101 to 150. Sql query to find missing employee ids?
Ans. 
SQL query to identify missing employee IDs in a sequential range from 101 to 150.
• Use a Common Table Expression (CTE) to generate a sequence of numbers from 101 to 150.

• Join the generated sequence with the Employee table to find missing IDs.

• Example SQL query: WITH seq AS (SELECT 101 + LEVEL - 1 AS emp_id FROM dual CONNECT BY LEVEL <= 50) SELECT emp_id FROM seq LEFT JOIN Employee ON seq.emp_id = Employee.emp_id WHERE E...read more
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