AA Master RPA study plan

Map the Automation Anywhere architecture layers (Control Room, Bot Runner, Bot Creator, repositories) and state what each component does without notes

Install or access a development environment and build a bot that opens an application, types text, and saves a file

Identify the core building blocks of a task (actions, variables, triggers) and match each to a use case

Explain the difference between a task, a bot, and a scheduled run in your own words

Build bots using recorders (object and screen) and then edit the generated actions to remove brittleness

Implement variable types, user-defined variables, and data passing between actions

Use loops, conditionals, and string/datetime manipulation to process a list of inputs

Apply the Excel and file-system actions to read, transform, and write tabular data

Build reusable sub-tasks and call them from a parent bot with parameters and return values

Implement try/catch/error-handler blocks around fragile actions and log failures with meaningful context

Use the error handler and recovery actions to retry a failed step a set number of times

Design a small bot with at least two reusable components and document the interface of each

Categorize failure modes (system, business, application, data) and choose the right handling strategy for each

Build a bot that recovers from a transient failure and continues without human intervention

Implement a business-exception path that logs and escalates rather than crashing

Instrument a bot with logging levels and verify the log output tells a coherent story

Deploy a bot to the Control Room and configure its execution environment and credentials

Create and manage a schedule, a queue, and a trigger-driven run

Assign roles and permissions to a user and explain the least-privilege model you applied

Read run history and audit logs to diagnose why a deployment failed

Set up version control / repository practices for bots and explain how bot promotion between environments works

Define a credential and secret-management approach and apply it to an existing bot

Draft governance rules covering bot naming, ownership, review, and change approval

Identify the audit and compliance controls the Control Room provides and where gaps remain

Define the Center of Excellence operating model: intake, prioritization, delivery, and support

Build a bot-opportunity assessment checklist and score three candidate processes against it

Explain how the platform's licensing and infrastructure choices affect scaling decisions

Document a support and monitoring model including escalation paths and SLAs

Design and build an end-to-end bot that combines data processing, sub-tasks, error handling, and a Control Room deployment

Run a full test pass covering happy path, transient failure, and business exception, and record results

Review your weakest domain and close the two biggest gaps with targeted practice

Complete timed practice sets across all five domains and log your accuracy per domain