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• Explain the Amazon Web Services Cloud & the value it provides:
• Pick a collection system that addresses the essential attributes of data, such as order, format, and compression
• Choose a collection system that manages the cycle, volume, and source of data
• Define the operational features of the collection system

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Eligibility Criteria For Exam

To be eligible for the exam and its related AWS Certified Data Analytics – Specialty certification, one should fulfill the eligibility criteria. These include expertise and a minimum of 2 years' experience in using AWS services to perform diverse tasks with analytics solutions. Having at least 5 years of practical background in the field of data analytics technologies is also required. Still, the main step one has to make is passing DAS-C01 exam.

Reference: https://d1.awsstatic.com/training-and-certification/docs-data-analytics-specialty/AWS-Certified-Data-Analytics-Specialty-Exam-Guide_v1.0_08-23-2019_FINAL.pdf

Who should take the AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam

The most suitable candidate for the Amazon Web Services Certified Cloud Practitioner (CLF-C01) exam would be the one possessing knowledge about the following:

• Know-how of the economics of the AWS Cloud
• Security and compliance within the AWS Cloud environment
• Knowledge of the core AWS services
• AWS Cloud concepts

The applicant should at least have six months, or the equivalent, of active commitment with the AWS Cloud, with engagement to AWS Cloud design, implementation, and/or operations. Candidates will exhibit the knowledge of well-designed AWS Cloud solutions.

The Amazon Web Services (AWS) Certified Data Analytics – Specialty (DAS-C01) is an exam designed for individuals who are good at performing the data analytics role. It tests the knowledge of candidates in using AWS services to build, design, maintain, and secure analytics solutions. Those who apply for this test should understand the differences between various data analytics services and how they can fit in the data lifecycle, storage, collection, visualization, and processing.

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