The document lists over 30 common AWS certification interview questions focused on AWS BI and data visualization services, detailing services like Amazon QuickSight, Redshift, Athena, EMR, and Kinesis. It covers functionalities, integrations, security features, and specific use cases for each service. Moreover, it highlights how these services can be utilized for real-time data analysis, big data, and machine learning, emphasizing collaboration and data processing capabilities.

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Top 30+ Latest AWS Certification Interview
Questions on AWS BI & Data Visualization
Services
Here are some commonly asked AWS certification interview question related to AWS BI and data visualization services
1. What is Amazon QuickSight?
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to deliver insights to
everyone in your organization.
2. What is Amazon Redshift?
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to
analyze all your data using your existing business intelligence tools.
3. What is Amazon Athena?
Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL.
4. What is Amazon EMR?
Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly and cost-effectively.
5. What is Amazon CloudWatch?
Amazon CloudWatch is a monitoring service for AWS resources and the applications you run on the Amazon Web Services
(AWS) cloud.
6. What is Amazon Kinesis?
Amazon Kinesis is a fully managed, cloud-based service for real-time processing of streaming data at massive scale.
7. How does Amazon QuickSight integrate with other AWS services?
Amazon QuickSight integrates with other AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon
Athena, Amazon EMR, and more, enabling you to easily and quickly bring in your data and start generating insights.
8. Can you explain how Amazon Redshift works?
Amazon Redshift works by managing a cluster of nodes, where each node is comprised of one or more CPU cores and disk
drives. The data is automatically distributed across all nodes, and you can use standard SQL to query the data.

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9. How does Amazon Athena differ from Amazon Redshift?
Amazon Athena is a managed service that allows you to run interactive queries directly on data stored in Amazon S3, while
Amazon Redshift is a data warehouse that you can use to store and analyze large amounts of structured data. The main
difference between the two is the size of the data set and the type of analysis you need to perform.
10. What are some common use cases for Amazon Kinesis?
Some common use cases for Amazon Kinesis include real-time data processing for applications such as fraud detection,
real-time metrics and logs, real-time market data analysis, and IoT device data analysis.
11. How can you access Amazon QuickSight?
You can access Amazon QuickSight via the AWS Management Console, APIs, or embedded in your applications.
12. What is Amazon S3 Select?
Amazon S3 Select is a feature of Amazon S3 that enables you to retrieve only the data you need from an object, reducing the
amount of data you retrieve and the time it takes to transfer the data over the network.
13. What is Amazon S3 Inventory?
Amazon S3 Inventory is a feature of Amazon S3 that provides a scheduled alternative to Amazon S3 Inventory reports, which
gives you a list of your objects and their metadata on a daily or weekly basis.
14. What is Amazon S3 Transfer Acceleration?
Amazon S3 Transfer Acceleration is a feature of Amazon S3 that enables fast, easy, and secure transfers of large files to and
from Amazon S3 over the public Internet.
15. How does Amazon Redshift handle data compression?
Amazon Redshift uses advanced columnar storage and data compression techniques to reduce the amount of disk space
required to store data. This enables you to store and analyze large amounts of data in a cost-effective manner.
16. Can you explain how Amazon Redshift handles data security?
Amazon Redshift provides multiple security features to ensure that your data is secure. These features include network
isolation using Amazon VPC, encryption of data at rest using AES-256 encryption, encryption of data in transit using SSL,
and control over network access using security groups and network ACLs.
17. Can you explain how Amazon QuickSight supports
collaboration?
Amazon QuickSight supports collaboration by enabling multiple users to access, analyze, and share data insights. You can
easily share insights with others by sending a link or embedding a dashboard in your application.

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18. Can Amazon QuickSight be used for real-time data analysis?
Yes, Amazon QuickSight supports real-time data analysis by providing the ability to connect to real-time data sources such
as Amazon Kinesis or other streaming data sources.
19. Can Amazon QuickSight be used for big data analysis?
Yes, Amazon QuickSight can be used for big data analysis by connecting to big data sources such as Amazon Redshift,
Amazon EMR, or other big data sources.
20. Can you explain how Amazon Athena integrates with other AWS
services?
Amazon Athena integrates with other AWS services such as Amazon S3, Amazon Glue, Amazon CloudWatch, and more,
enabling you to easily and quickly bring in your data, process it, and start generating insights.
21. How does Amazon Kinesis handle data security?
Amazon Kinesis provides multiple security features to ensure that your data is secure. These features include encryption of
data in transit using SSL/TLS, encryption of data at rest using AWS Key Management Service (KMS), and control over
network access using security groups and network ACLs.
22. Can Amazon Kinesis be used for real-time data processing?
Yes, Amazon Kinesis is designed specifically for real-time data processing, making it a great choice for applications such as
real-time metrics, real-time logs, real-time market data analysis, and IoT device data analysis.
23. Can you explain the Amazon Kinesis data model?
The Amazon Kinesis data model consists of three main components: streams, shards, and records. Streams are collections
of data records that are ordered and accessible over a certain period of time. Shards are units of a stream that you can use
to scale a stream’s capacity horizontally. Records are individual units of data that are stored within a shard.
24. Can Amazon Redshift be used for real-time data analysis?
While Amazon Redshift is not designed for real-time data analysis, it can be used in near real-time scenarios. By using
Amazon Redshift in conjunction with Amazon Kinesis, you can perform real-time data processing and then analyze the
results in near real-time using Amazon Redshift.
25. How does Amazon CloudWatch handle data security?
Amazon CloudWatch provides multiple security features to ensure that your data is secure. These features include
encryption of data in transit using SSL/TLS, encryption of data at rest using AWS Key Management Service (KMS), and
control over network access using security groups and network ACLs.
26. Can you explain the Amazon CloudWatch data model?
The Amazon CloudWatch data model consists of metrics, dimensions, and alarms. Metrics are time-ordered data that you
can use to monitor the health and performance of your applications and infrastructure. Dimensions are metadata that you
can use to categorize metrics and isolate specific subsets of metrics for analysis. Alarms are used to trigger actions based on
specified conditions for metrics.

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27. Can Amazon QuickSight be used for machine learning?
Yes, Amazon QuickSight can be used for machine learning by integrating with Amazon SageMaker, a fully-managed
machine learning service. With Amazon SageMaker, you can train and deploy machine learning models, and then use
Amazon QuickSight to visualize the results and insights generated by the models.
28. Can Amazon Redshift be used for machine learning?
Yes, Amazon Redshift can be used for machine learning by integrating with Amazon SageMaker or other machine learning
services. With Amazon Redshift, you can store and process large amounts of data, and then use machine learning algorithms
to generate insights from the data.
29. How does Amazon S3 handle data security?
Amazon S3 provides multiple security features to ensure that your data is secure. These features include encryption of data
at rest using AES-256 encryption, encryption of data in transit using SSL/TLS, and control over network access using IAM
policies and resource-based access control.
30. Can Amazon QuickSight be used with multiple data sources?
• Yes, Amazon QuickSight can be used with multiple data sources, including Amazon S3, Amazon Redshift, Amazon RDS, Amazon
Aurora, Amazon Athena, and more. You can easily connect to these data sources and create visualizations using the drag-and-
drop interface.
31. Can Amazon Quicksight be used for reporting?
• Yes, Amazon QuickSight can be used for reporting, allowing you to create and share interactive reports with team members. You
can use the drag-and-drop interface to create visualizations, add filters and parameters, and apply custom styles. You can also
schedule reports to be delivered via email or published to a shared folder.