Guide to pass AWS Data Analytics Speciality Exam

In earlier story, I have written about my AWS cloud certification journey and how I could pass 4 AWS certifications without attending any paid course. In this story, I will discuss about my 5th certification (AWS Data Analytics Speciality) which I passed 2 weeks ago.

AWS Certifications and suggested experience level

You may take “AWS Data Analytics Specialty” certification if you are already working in Data Analytics, Data Migration, Data warehouse, Big Data framework, real time data processing or have strong interests in these technologies.

As with any AWS Specialty certification, you should expect this certification to be much more rigorous than any associate level examinations. You may plan for around 300 hours of study or ~4–5 months (considering you spend 2–2.5 hours a days). I would suggest to take this certification exam only if you have already passed few AWS certifications (at Associate level) or been working in the data engineering domain for last 2–3 years.

I will provide the list of AWS services which are important for this certification exam and how you should prepare for the exam:

1. Kinesis — There are 3 main services in this domain (Kinesis Firehose, Kinesis Stream and Kinesis Data Analytics) and very good knowledge of this domain is key to pass this certification. You may expect ~15–20% questions related to these 3 services. It is recommended to try at least 2–3 hands-on tutorials on each of these 3 services (especially integration with Glue, cross-account, cross-region related use-cases wherever applicable). You may go through recent videos of AWS Tech Talks, AWS Deep Dive sessions, That’s my Architecture available on Youtube to deepen your knowledge. My particular preference is for “That’s My Architecture” in which various CTOs discuss how they implemented AWS services to solve specific business problem in real-life.
2. Glue — This is ETL service and the 2nd most important service for exam. This service is normally used with other Data Analytics related services such as Redshift, DMS, S3, Athena, Lambda, Kinesis etc. You should create some simple architecture using this service in your AWS account to simulate ETL process.
3. S3 — This service is used for Data Lake or storage of data and hence is used in almost every use cases (though storage class may vary depending on the scenario). Solid understanding of this service for different use-cases such as cross-account/cross-region access, integration with Athena, EMR, Redshift, Redshift Spectrum, Quicksight is very important in actual situation as well as for passing the exam.
4. EMR — This service is used for BigData processing and can be used with many types of Apache framework. Although there were not many questions in exam, this is very useful service in actual data processing scenario.
5. Athena & Quicksight — These are used for business intelligence such as ad-hoc query, dashboard, sharing information, story-telling etc. Different approach to save cost, it’s use-cases, key functionalities are very important. You may expect few questions on different functionalities available in Standard/Enterprise edition of Quicksight service.
6. Lambda — This is the most common service in AWS architecture and used mainly for integrations in every data pipeline, so it’s very important to learn this service thoroughly (preferably by doing few use-cases with any coding language of your choice).
7. Opensearch (Elastic Search), Opensearch Dashboards (Kibana)— This is used in case of ad-hoc and real time dashboards. I could see only few (2–3) questions related to this service in actual exams.
8. Other services — There are some services which can be used to provide a reliable, performant and secure solutions such as SNS/SQS/ Gateways,Appflow,VPC,KMS,IAM,DMS, Cloudwatch, CloudTrail etc. These are basic building blocks of architecture and most of questions in exam will have some reference to these commonly used services.

In a nutshell, it is strongly advisable to have some experience with Data Engineering (Data Analysis/Data Science/Data Migration/Data Visualization) and at least AWS Solution Architect Associate certification before preparing for this exam.

Some of the learning materials are mentioned below which I have used during my preparation:

1. Tutorials Dojo — There are many hands-on tutorials, cheat sheets, documentation related to each AWS service. This was my primary resource for hands-on exercises. Ref: https://tutorialsdojo.com/
2. AWS Training & Certification video — This gives a methodical overview of important services and describes how to analyse questions in actual exam and finalize your answer for a given scenario. Ref: https://explore.skillbuilder.aws/learn/public/learning_plan/view/97/data-analytics-learning-plan
3. AWS Tech Talks/AWS Deep Dive Sessions/That’s My Architecture videos.
4. AWS Documentation (detailed study of User Guide, FAQ, Developer Guide)
5. Sample Practice questions on Examtopics for 2 exams (AWS Data Analytics & AWS Big Data). Ref: https://www.examtopics.com/
6. AWS Whitepapers on Data Analytics related services (especially Big Data Options, Streaming Data Solution)
7. Attend regular training courses given by AWS (arranged by your organization, any free seminars etc).

Thanks for reading; I hope that this short article has given you relevant information. You may share your feedback if this has really helped you to pass your certification.

Happy learning!!!