Addend Analytics Data Engineer Intern Interview Questions and Answers

Q1. What is the role of data engineer?
Ans. 
Data engineers are responsible for designing, building, and maintaining the infrastructure that allows for the storage and analysis of data.
• Designing and implementing data pipelines to collect, process, and store data

• Building and maintaining data warehouses and databases

• Optimizing data workflows for efficiency and scalability

• Collaborating with data scientists and analysts to ensure data quality and accessibility

• Impleme...read more
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Q2. Explain operator in sql
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Operators in SQL are symbols used to perform operations on data, such as arithmetic, comparison, and logical operations.
• Operators in SQL include arithmetic operators (+, -, *, /), comparison operators (=, <>, >, <, >=, <=), logical operators (AND, OR, NOT), and more.

• Arithmetic operators are used to perform mathematical calculations on data, such as adding two numbers together.

• Comparison operators are used to compare va...read more
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Q3. Create query on notepad,
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Create a query to find the total sales amount for each product category.
• Use GROUP BY clause to group the sales data by product category

• Use SUM() function to calculate the total sales amount for each category
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Q1. Resume screening Self introduction Roles and responsibilities
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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

Q1. Versant test should be need to attend Hirepro will have interview prosess in YouTube will check and prepare mid level hard
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Q2. We can goo for online prosess how it is
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Q1. Manger Level round they will ask KPI NPS AHT standard deviations Service Level handling Root cause Analysis Weighted average Same product how will calculate Totall Swer % how will get Many of like bpi <20 ...read more
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Q1. Salary discussion and update structure of our role and responsibilities
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Q1. Tell me About Yourself
Ans. 
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
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Q2. Project working and day to day activities I do
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Q3. Rate Yourself In SQL out of 5
Ans. 
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
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Q4. Sql 3 question based on windows function
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Q5. Very basic python 2 Question
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Q6. What You do on power bi in your work Explain with full details workflow
Ans. 
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
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Q1. All 3 sql Questions very simple Round but they didn't provide any Data tables just tell this and that so it may get confusing I slove 2 out of three but rejected on this Round questions Are intermidate
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Q1. They asked me regarding project
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Q2. Sql queries and data bricks related questions
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Q1. Details explaination on project that I have worked and optimization techniques
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Q1. Scenario based questions on Azure data factory and pipelines
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Q2. Optimisation technic to improve the performance of databricks
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Q3. What is Autoloader
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Q4. What is unity catalog
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Q5. How you do the alerting mechanism in adf for failed pipelines
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Q1. How do you optimize SQL queries?
Ans. 
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
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Q2. How do you do performance optimization in Spark. Tell how you did it in you project.
Ans. 
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...read more
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Q3. What is SparkContext and SparkSession?
Ans. 
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
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Q4. When a spark job is submitted, what happens at backend. Explain the flow.
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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 ...read more
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Q5. Calculate second highest salary using SQL as well as pyspark.
Ans. 
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
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Q6. 2 types of modes for Spark architecture ?
Ans. 
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.
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Q7. If you want very less latency - which is better standalone or client mode?
Ans. 
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.
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Q1. Scenario based. Write SQL and pyspark code for a dataset.
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Q2. If you have to find latest record based on latest timestamp in a table for a particular customer(table is having history) , how will you do it. Self join and nested query will be expensive. Optimized query...read more
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Q1. Can you introduce yourself and describe your current project experience?
Ans. 
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
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Q2. Decorators in python
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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.
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Q3. What is the SQL query to group by employee ID in order to combine the first name and last name with a space?
Ans. 
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
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Q4. What are constructors in Python?
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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)
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Q5. Indexing in sql
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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...read more
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Q6. Why spark works well with parquet files?
Ans. 
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...read more
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Q1. What are different type of joins available in Databricks?
Ans. 
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 ...read more
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Q2. How do you make your data pipeline fault tolerant?
Ans. 
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
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Q3. What is AutoLoader?
Ans. 
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
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Q1. How do you connect to different services in Azure?
Ans. 
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.
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Q2. What are linked Services?
Ans. 
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.
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