Goal - To test your understanding of data analytics focused on AWS and various tools and techniques based on the AWS cloud offerings:
An understanding of the full data life cycle in a Cloud environment and ensure the "five Vs" of data analysis and their context are well understood.
Ingesting Data - Mechanisms for collecting the data which includes attention to details such as variety of data, velocity of data.
Storing the Data - Primarily driven by the volume of data but is also subtly influenced by velocity, variety and value (this aspect esp. for securing the data).
Processing the Data - The key aspect and the "crown jewel" of data analytics - this ensure the Value of data is properly "enshrined" so data transforms to information and thence on to its key mission of delivering Value and Insights (which brings the "sixth V" - visualization). This layer as part of its data flow analysis is also tasked with verifying the integrity and accuracy of the data (veracity).
Obtaining Insights from the Data - this serves as a pre-requisite as well as a post narrative to data that was processed. The key here is to understand how one can categorize a large volume of data succinctly in a picture to derive insights about it (Remember, a simple picture is worth a 1000 data points :) )
Security - Data security is a dimension that pervades all of the above aspects and involves controls to access as well as encryption (at rest and transit). In addition, multi-level security such as MFA as well as temporally vanishing credentials are key principles towards preserving only those who have the need to access data can indeed access and use the data.
The AWS offerings that come into play for managing data life cycle in the above dimensions will be examined in detail to test your knowledge in the practice exams.
Notes on AWS Data Analytics Certification Specialty Exam. The exam consists of five domains with weights indicated below (as of this writing). The questions tend to mix and match the domains to test your understanding of data life cycle as well as within a domain to test your depth of knowledge.
Domain 1: Collection - 18%
Domain 2: Storage & Data Management - 22%
Domain 3: Processing - 24%
Domain 4: Analysis and Visualization - 18%
Domain 5: Security - 18%
This course can strengthen your foundations for advanced Data Analytics in the cloud exams both via its depth as well as its use case driven questions. The latter tends to be the style of the AWS exam questions. This course can also help to evolve your data journey from Analytics to more advanced aspects such as ML based processing.
The course is organized via two tests that are designed to bring out deep nuances associated with the setup, development, working, algorithmic, security, and operational aspects of a data life-cycle, AWS services, their interplay in data management, and "How-to-do" data analysis optimizing cost, or effort or other key criteria.
Key Hints for AWS DAS-C01 exam takers:
This exam replaces the Big Data certification that was in place before. This is much more comprehensive than the Big Data specialty exam. Ensure you have completed a basic certification or a course in the AWS cloud (equivalent of Cloud Practitioner or Assoc. Architect or Developer). This will ensure needful foundations are in place to help on any Specialty exam. It is also important to distinguish between "Data Analytics" versus "Databases" - this course is not about the latter (which is a specialty of its own).
Useful Exam Prep Resource:Please review AWSSkill Builder for free publicly available AWSquestion sets (incl. practice exams in Skill Builder)to further "Data Augment" the knowledge provided by these tests.
Next Steps
Once you have complete this course, I encourage you to check the next advanced step in data analysis: my Machine Learning practice tests titled "Amazon Sagemaker &MLin the Cloud:In-depth Practice Tests".
Focus on the pattern of the ask in the practice test questions, not just the question.
Good Luck!
