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I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.
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I applied via Walk-in and was interviewed in Dec 2024. There were 5 interview rounds.
Given task Statics standard deviations Attrition Average of given table values and Given graph economi graph and poverty graph base on that need to gave answers 30 qustion and 60 min time duration
I was interviewed in Dec 2024.
I am passionate about data entry and aim to grow in this field by improving my skills and taking on new challenges.
I have a strong attention to detail, ensuring accuracy in data entry tasks.
I am proficient in using data entry software and tools, such as Microsoft Excel.
I am eager to learn and adapt to new technologies and processes in the data entry field.
I have experience in handling large volumes of data and meeting ...
I have a Bachelor's degree in Computer Science.
Bachelor's degree in Computer Science
Specialization in data management and analysis
Relevant coursework in database management systems
Hands-on experience with data entry software
I am interested in this position because of my strong data entry skills and passion for organizing information efficiently.
I have excellent typing speed and accuracy, which are essential for this role.
I enjoy working with data and organizing information in a systematic way.
I am detail-oriented and have a strong attention to accuracy, which is crucial for data entry tasks.
I am motivated to perform well at work by setting personal goals, receiving recognition for my achievements, and enjoying a sense of accomplishment.
Setting personal goals helps me stay focused and driven
Receiving recognition for my achievements boosts my morale and motivates me to continue performing well
Enjoying a sense of accomplishment from completing tasks successfully drives me to excel in my work
One of my biggest weaknesses is that I can be overly critical of my own work.
I tend to be a perfectionist and can spend too much time on a task trying to make it perfect.
I sometimes struggle with delegating tasks to others because I want to ensure they are done correctly.
I can be too hard on myself when I make a mistake, which can affect my confidence.
I handle pressure and deadlines by prioritizing tasks, staying organized, and maintaining open communication with team members.
Prioritize tasks based on urgency and importance
Break down tasks into smaller, manageable chunks
Set realistic goals and deadlines
Stay organized with to-do lists and calendars
Communicate with team members about progress and potential challenges
I chose this career because of my strong attention to detail and passion for organizing data.
I have always been detail-oriented and enjoy working with data
I find satisfaction in organizing information and ensuring accuracy
I believe my skills are well-suited for the role of a Data Entry Operator
My greatest strength is my attention to detail and accuracy in data entry tasks.
Strong attention to detail
High level of accuracy
Ability to work efficiently and effectively under pressure
Experience with various data entry software and tools
I have a Bachelor's degree in Computer Science.
Bachelor's degree in Computer Science
Specialized in data management and analysis
Completed courses in database management and programming languages
I attended XYZ College for my degree in Computer Science.
XYZ College
Degree in Computer Science
Graduated in 2020
I plan to overcome my weakness by acknowledging it, seeking feedback, setting goals for improvement, and practicing regularly.
Acknowledge the weakness and accept it as an opportunity for growth
Seek feedback from colleagues or supervisors to understand how the weakness impacts your work
Set specific goals for improvement and create a plan to achieve them
Practice regularly to strengthen the skills associated with the weak...
I am interested in this position because I have strong data entry skills and enjoy working with data.
I have a strong attention to detail which is essential for accurate data entry
I enjoy organizing and categorizing information
I have experience using data entry software such as Microsoft Excel
Work done from all assigments
I was interviewed in Dec 2024.
I am a data analyst with a background in statistics and experience in analyzing large datasets.
Background in statistics
Experience in analyzing large datasets
Proficient in data visualization tools like Tableau
Strong problem-solving skills
Excellent communication skills
I would rate myself a 4 out of 5 in SQL proficiency.
Proficient in writing complex SQL queries
Experienced in optimizing database performance
Familiar with data manipulation and analysis functions
Comfortable working with large datasets
I use Power BI to analyze and visualize data for insights and decision-making in my work.
Connect to data sources to import data
Transform and clean data using Power Query Editor
Create relationships between different data tables
Design interactive reports and dashboards
Use DAX formulas for calculations and measures
Share reports with stakeholders and collaborate on insights
I was interviewed in Dec 2024.
I applied via Naukri.com and was interviewed in Oct 2024. There were 2 interview rounds.
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
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 projec...
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
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 ...
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.
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.
I applied via Naukri.com and was interviewed in Nov 2024. There was 1 interview round.
I am a Senior Data Engineer with experience in building scalable data pipelines and optimizing data processing workflows.
Experience in designing and implementing ETL processes using tools like Apache Spark and Airflow
Proficient in working with large datasets and optimizing query performance
Strong background in data modeling and database design
Worked on projects involving real-time data processing and streaming analytic
Decorators in Python are functions that modify the behavior of other functions or methods.
Decorators are defined using the @decorator_name syntax before a function definition.
They can be used to add functionality to existing functions without modifying their code.
Decorators can be used for logging, timing, authentication, and more.
Example: @staticmethod decorator in Python is used to define a static method in a class.
SQL query to group by employee ID and combine first name and last name with a space
Use the GROUP BY clause to group by employee ID
Use the CONCAT function to combine first name and last name with a space
Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name
Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.
Constructors are defined using the __init__() method in a class.
They are used to initialize instance variables of a class.
Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)
Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.
Indexes can be created using CREATE INDEX statement in SQL.
Types of indexes include clustered indexes, non-clustered indexes, unique indexes, an...
Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.
Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.
Parquet files support efficient compression, reducing storage space and improving read performance in Spark.
Spark can push down filters to Parquet files, allowing f...
I applied via Recruitment Consulltant and was interviewed in Nov 2024. There were 2 interview rounds.
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 ...
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
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
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.
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.
I applied via Naukri.com and was interviewed in Nov 2024. There was 1 interview round.
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