Centific Data Annotator Interview Questions and Answers

Q1. What qualities should employee have?
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Q2. Why am i eligible ?
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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. 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. What is the architecture of Apache Spark?
Ans. 
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?
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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?
Ans. 
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. What are different type of joins available in Databricks?
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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?
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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?
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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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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. Can you provide an overview of your background, including your past experiences and daily activities, as well as the tools you use in your routine?
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Q2. What are the concepts of advanced Excel and Power BI projects, and how are they utilized within a company or for clients?
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Q1. Can you explain your project experience related to Advanced Excel and Power BI?
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Q2. What are the concepts of credit and operations, particularly in relation to Know Your Customer (KYC) procedures and the privacy of client data?
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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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