Make Automation
Develop the skills to design a Make scenario with explicit data and error paths through a learning path centred on Make Automation. Explore concepts, guided practice and an applied project.
Build practical Make Automation skills.
Skills and knowledge developed in this course.
- Scenario model
- Data handling
- Reliability
- Scenario lab
- Applied project: Design a Make scenario with explicit data and error paths
- Make Automation — validation and review
About this course
This course develops practical capability in Make Automation. Its course-specific content covers Scenario model, Data handling, Reliability, Scenario lab, followed by an applied project focused on design a make scenario with explicit data and error paths. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Make Automation or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of no-code, low-code & automation. Prior experience with the named product helps with the practical exercises.
Design a Make scenario with explicit data and error paths
Deliver design a make scenario with explicit data and error paths. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Scenario model
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- Modules, bundles and execution
- Scheduling and webhook triggers
Practical exerciseApply scenario model to the design a make scenario with explicit data and error paths and capture the validation evidence.
02Data handling
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- Mapping, iterators and aggregators
- Filters and branching routes
Practical exerciseApply data handling to the design a make scenario with explicit data and error paths and capture the validation evidence.
03Reliability
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- Error handlers and retries
- Connection ownership and execution limits
Practical exerciseApply reliability to the design a make scenario with explicit data and error paths and capture the validation evidence.
04Scenario lab
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- Automate a sample multi-step workflow
- Validate incomplete input and inspect run evidence
Practical exerciseApply scenario lab to the design a make scenario with explicit data and error paths and capture the validation evidence.
05Applied project: Design a Make scenario with explicit data and error paths
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- Define inputs, assumptions and acceptance criteria for: Design a Make scenario with explicit data and error paths.
- Implement the project using scenario model, data handling, reliability, scenario lab.
Practical exerciseDesign a Make scenario with explicit data and error paths.
06Make Automation — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Scenario model · Data handling
Establish the Make Automation scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Reliability · Scenario lab
Apply the core Make Automation techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for design a make scenario with explicit data and error paths. - 04Learning milestone 4 In progress
Deliver the applied project
Complete design a make scenario with explicit data and error paths, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| Automation Developer | ₹5–12 LPA | ₹14–26 LPA | ₹28–48 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗