By Justin Watkins and Daniel Ives
As more companies embrace data to make business decisions, so the demand for data analytics processing is growing. The combination of a pay-as-you-go pricing model, and almost-unlimited capacity, make cloud platforms such as Amazon Web Services (AWS) the ideal place to perform the heavy-duty computation required to gain insight out of datasets large and small.
QA delivers all of the official AWS training courses, in both remote form and in-person, including how to design systems (Architecting on AWS/AMWSA) and develop applications (Developing on AWS/AMWSD). Alongside the training courses, AWS offers a certification programme for those learners who want to validate their cloud experience.
The AWS Certified Data Analytics – Specialty certificate was introduced at the beginning of 2020. It covers the tools in the AWS cloud platform that can be used to do ingestion, data wrangling, computing and visualisation. This certification replaces the Big Data Specialty certification that was available until April 2020. At the present time (July 2020), there is no course of the same name (yet).
How to prepare for the AWS Data Analytics exam
How can learners prepare for this Data Analytics speciality certification? Learners can still attend classroom training, and there are other resources available to help you prepare.
The existing Big Data classroom training course is still running at QA, and covers a lot of the same topics that are required for the Data Analytics certification. It is a great place to start on the journey. It is available both as classroom delivery and virtual delivery.
There was never an official study book for the Big Data Specialty exam. Preparing for the old exam involved taking the course, doing a lot of reading on the AWS documentation pages, and trying things out with the AWS services in question. An official study guide for the Data Analytics Specialty exam is in preparation, and will be published (according to Amazon.com) on 25 November 2020, and it can be pre-ordered here.
Prior to attempting the Data Analytics Specialty exam, learners are also encouraged to take online digital training, including Data Analytics Fundamentals. This course might also be useful before attending the classroom training.
A Data Analytics – Exam Readiness course is also available online.
Then there's a practice exam, which comprises 20 questions and must be completed in 60 minutes. It is available online through PSI online. The practice exam is not performed under full exam conditions, so nobody is watching you through your webcam.
The Data Analytics exam
The actual Data Analytics exam is performed either in a training centre (via P2P exams or Pearson VUE), or online with an invigilator or proctor watching you through your webcam and screen sharing. It comprises 65 questions and lasts just over 3 hours.
What's in the exam?
Like all of the AWS Specialty exams, this exam requires deep domain knowledge, in this case of the data analytics services. The exam blueprint is required reading. This document effectively tells us how AWS Training & Certification defines a Data Analytics Specialist and is the document that drives the whole exam-building process.
Without going anywhere near the boundaries of our non-disclosure agreement, we can tell you that you will need deep experience with Redshift, Kinesis (all flavours), Athena and Glue, and probably in that order of importance. But you should be competent with all of the services that are clustered under that "Analytics" section in the Management Console, even EMR (because for a long time that was analytics on AWS). Understanding Lambda and CloudWatch events and the various storage services, from S3 to Keyspaces, is likely to be useful as well. As security is job number one at AWS, you should also be aware of how to secure your workloads and data.
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Justin WatkinsJustin is an experienced instructor with a passion for bringing technical content to life. He currently delivers training courses in AWS (Amazon Web Services) cloud architecture, and his training portfolio also includes cluster-admin, Big Data and maths-intensive applications.