Preparing for the AWS Certified Data Analytics — Specialty Exam
About a month ago, I obtained the AWS Certified Data Analytics — Specialty (DAS-C01) Certification ✅ .
I’ve found it legitimately hard. To the point where I postponed it several times, feeling I wasn’t quite up to the mark. It almost felt like I was constructing a complex decision tree in my mind to ace it. Perhaps due to my AWS not-very-extensive experience (I’m working with GCP shops day-to-day for several years now), it took me several weeks to feel confident enough to take it.
However, the right study materials can turn the tide in your favor. If you’re eyeing this certification, here are the main building blocks for me:
1️⃣ The Udemy course by Stéphane Maarek and Frank Kane. Spanning 187 lectures over 16.5 hours, this course provides an in-depth understanding of various technologies, their applications, and practical demonstrations. It served as an excellent starting point for me.
2️⃣ The book — AWS Certified Data Analytics Study Guide by Asif Abbasi. Not only does it come with access to a Test Bank with Flash Cards (a huge bonus!), but it also provides a holistic view. After delving into videos, this book solidified my understanding, helping me to grasp even the minute details.
3️⃣ Practice Exams from Jon Bonso on Tutorial Dojo — These are golden for gauging your progress. I’d take a mock after the video course to identify gaps. The book then helped to refine and fill those gaps. Rinse & repeat.
🔥 Bonus: Don’t miss out on the complimentary AWS Exam Readiness course and the invaluable AWS Cheat Sheets on the same platform.
Also, be sure to check the free AWS-provided Exam Readiness course and the great AWS Cheat Sheets on the same platform.
4️⃣ It’s of course recommended to review the AWS Exam guide and Sample question (why not). You need to know what you’re up against. 🏁 The whitepapers are also there for deep dives into the AWS toolbox, right from its developers.
Wishing you a productive study journey! 💡
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This story was originally posted on my blog at https://datawise.dev. Head over there for more insights on Data Analytics and Data Engineering.
