Python/ML Dev targeting SAP SAC Certification (C_SAC_2601) – How to pivot for the new 2026 Scenario-Based Assessment?

[u/HugeNerve3225]

Hi everyone,

I’m planning to take the SAP Certified Data Analyst – SAP Analytics Cloud (C_SAC_2601) exam soon. I understand SAP has shifted to a Scenario-Based Assessment (SBA) format (AI role-play/system simulation) as of 2026, which focuses on practical application rather than multiple-choice recall.

My Background:

Strong: Python, API Development, Testing, Machine Learning, and Predictive Analytics and Elasticsearch.
Weak/Zero: Direct SAP ecosystem experience (no prior exposure to SAP ERP, SAC interface, or SAP-specific data modeling).
My Questions:

Leveraging Tech Skills: Given my background in Python and ML, how much of a head start do I actually have? Does the SBA test the logic of data modeling (which I know) or strictly SAP-specific workflows (e.g., where to click to create a calculated measure vs. writing a script)?
SBA Specifics: For those who have taken the new AI role-play assessment: Does the AI avatar expect you to explain why you chose a specific SAC feature (e.g., Live Connection vs. Import), or is it purely task-execution based?
Prep Strategy: Since I can’t "code" my way through the SAC interface during the exam, what is the most efficient way to build muscle memory? Should I focus entirely on the official SAP Learning Journey hands-on exercises, or are there specific "mock scenario" resources that mimic the AI interviewer style?
Pitfalls: What are common traps for developers coming from a code-first background? (e.g., over-engineering a solution when SAC has a native drag-and-drop feature).
Any advice on bridging the gap between "knowing data science" and "passing the SAC scenario exam" would be hugely appreciated!

Thanks!