An organization has petabytes of data gathered from a wide range of sources. They want to use the data for strategic analysis and to guide business decisions. What type of service should they use?
I prefer A.
B lacks native multi-cloud data integration.
C doesn’t address cross-cloud data synergy for analytics.
D focuses on app deployment (e.g., Kubernetes), not large-scale data analysis.
These options don't make sense for this question. The closest answer is A but I seriously doubt this question with these choices will be on the actual exam.
Am I the only one here that thinks the question and answer are not really related? The answer to "what type of service should they use" is "data lakehouse", or feasibly "data lake". If one of the answers was Bigquery, that would also be fine. All these answers here are just not related without stretching the bounds of credibility.
And to people saying "it's a container" - I assure you, a container environment is in no way optimised for data analytics. If there's one thing that you don't want in containers, or managed by them, it's petabytes of data. Containers are for application level workloads. Anything that uses the entire ram of the server is where you want to use virtualisation or bare metal.
But ChatGPT says D. Container environment is used primarily for deploying and managing applications in a lightweight and scalable way but is not focused on large-scale data analysis.
It says A is correct because;
allows an organization to use different cloud services from multiple providers to store, manage, and analyze vast amounts of data. This flexibility is especially important when handling petabytes of data from various sources
D: Container Environment: While containers themselves aren't specifically for handling data analytics, they are part of an infrastructure that can efficiently manage and scale data-intensive applications across various environments. Container orchestration platforms like Kubernetes can manage containers that run big data tools and applications, providing flexibility, scalability, and efficiency. This allows for the deployment of complex applications and data processing tasks that can handle large volumes of data effectively.
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