Lognormal Analytics Associate Data Engineer Interview Questions and Answers for Freshers

Q1. Technical questions
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Q2. Tell me about yourself
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Q3. Questions on SQL queries
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Q4. Python questions
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Q5. DBT/snowflake questions
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Round 1 was online Test where one basic coding question was there and few aptitiude , verbal ability and python based question.

Q1. What is Data WareHouse..?
Ans. 
A Data Warehouse is a centralized repository that stores integrated data from multiple sources for analysis and reporting.
• Data Warehouses are designed for query and analysis rather than transaction processing.

• They often contain historical data and are used for decision-making purposes.

• Data Warehouses typically use a dimensional model with facts and dimensions.

• Examples of Data Warehouse tools include Amazon Redshift, Sn
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Q2. Nested Queries in Bigquery..?
Ans. 
Nested queries in BigQuery allow for querying data from within another query, enabling complex data analysis.
• Nested queries are queries that are embedded within another query

• They can be used to perform subqueries to filter, aggregate, or manipulate data

• Nested queries can be used in SELECT, FROM, WHERE, and HAVING clauses
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Q1. Tell me about your self
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Q2. If experienced, then Tell me about your previous job role and responsibilities of previous job role.
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Q3. Tell me about your resume.
Ans. 
My resume showcases my experience in data analysis, including proficiency in SQL, Python, and data visualization tools.
• Proficient in SQL for data querying and manipulation

• Skilled in Python for data analysis and automation

• Experience with data visualization tools like Tableau and Power BI

• Strong analytical and problem-solving skills

• Previous projects include analyzing sales data to identify trends and patterns
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Q4. Tell me about your memorable Moment of life.
Ans. 
My memorable moment was when I graduated from college.
• Graduating from college after years of hard work

• Celebrating with family and friends

• Feeling a sense of accomplishment and pride
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The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.

Normal aptitude questions

SQL and spark code for Fibonacci series

Q1. Pyspark optimization technique
Ans. 
One pyspark optimization technique is using broadcast variables to efficiently distribute read-only data across all nodes.
• Use broadcast variables to efficiently distribute read-only data across all nodes

• Avoid shuffling data unnecessarily by using partitioning and caching

• Optimize data processing by using appropriate transformations and actions
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Q1. Agile questions
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Q1. What is L1 and L2 Regularization?
Ans. 
L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding penalty terms to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term to the cost function.

• L2 regularization adds the squared values of the coefficients as penalty term to the cost function.

• L1 regularization can lead to sparse models by forcing some coefficients to be exactly zer...read more
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Q1. What is data entry? How to do data entry?
Ans. 
Data entry is the process of entering, updating, or verifying data in a computer system or database.
• Data entry involves inputting alphanumeric data into a computer system.

• Accuracy and speed are important in data entry.

• Common tools used in data entry include keyboards, scanners, and data entry software.

• Examples of data entry tasks include entering customer information, updating inventory records, and transcribing handwr
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Q2. What is your passion?
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Q3. What kind of work you hava to work in thise office
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Q4. How to like thise company
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A="bala",b="Babu";
Print=A+b
O/p:
balababu

Python is a computer programming language use to build up software and websites designed by Rossum appear in 20 Feb 1991
1.Numpy is a python libraries working with arrays.
2.pandas is used to read the data sets.
3.matplotlib libraries for visualisation.
4.mission learning is divided into two parts.
*Supervised learning
*Unsupervised learning
5.deep learning is used to create artificial neurons.
6.advance excel is used for graphs and calculate the whole members at same time lot of useful things that have.
7mysql is mainly used for store the data.

Python, numpy, pandas, mission learning, deep learning, basic statistics, advance excel, MySQL

Python, numpy, pandas, mission learning, deep learning, basic statistics, advance excel, MySQL

Q1. Why we use mission learning Mission learning used for analysis the data's and we can able to predict and we add some additional algorithm it's mainly used for prediction and AI.
Ans. 
Mission learning is used for data analysis and prediction with additional algorithms for AI.
• Mission learning is a subset of machine learning that focuses on predicting outcomes based on data analysis.

• It involves using algorithms to learn patterns and make predictions based on new data.

• Examples include image recognition, natural language processing, and recommendation systems.
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Q2. Deep learning uses: Deep learning Mainly used for creating artificial neurons in the neural network. Deep learning is very aid about the mission how to work and artificial intelligence helps also
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Q3. Statistics: Statistics is used for mathamatics operation and it helps to prediction. Example: We started to calculate how many persons live in between 20to25 age. That time statistics is very help to reduc...read more
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I don't know idea about group discussion