AIOps Foundation (AIOF) Certification
Course 3697
2 DAY COURSE

The AIOps Foundation (AIOF) Certification course introduces the concepts, principles, and practices of Artificial Intelligence for IT Operations (AIOps). Participants learn how AIOps leverages data, machine learning, and automation to improve operational performance, reduce incident impact, and enable proactive, resilient IT operations. This course provides a practical understanding of AIOps use cases, core technologies, metrics, and implementation considerations. It prepares attendees for the AIOps Foundation (AIOF) certification exam, administered by PeopleCert on behalf of the DevOps Institute.

This course positions learners to successfully complete the AIOps Foundation certification exam.

AIOps Foundation (AIOF) Certification Benefits

• In this course, you will:

  • Official PeopleCert training materials and AIOps exam included
  • Take2 exam retake option included
  • Exam-focused coverage of all syllabus learning outcomes

  Audience & Prerequisites

  • Who Should Attend: IT operations, infrastructure, and service management professionals; Site Reliability Engineers (SREs) and DevOps practitioners; IT leaders and managers responsible for operational performance; Professionals exploring AI-driven operations and automation.
  • Prerequisites: There are no formal prerequisites for the course or exam.
  • Recommended Knowledge: Familiarity with IT operations, DevOps, or service management concepts is recommended.

  Certification & Exam Information

  • Exam Name: AIOps Foundation (AIOF)
  • Duration: 60 minutes
  • Questions: 40
  • Pass Mark: 26/40 (65%)
  • Format: Multiple choice, open book
  • Exam Delivery: Online via PeopleCert web proctoring

  What to Expect

  • Exam Prep Course: This course is designed to help you prepare for both the foundation and practitioner certification exams. You can expect coverage of all exam learning objectives, with a focus on understanding key concepts, terminology, and how they’re assessed.
  • Content Volume: Certification courses cover a large amount of material in a short time. Instructors prioritize content tied to exam objectives. Supplementary topics will be addressed as time allows.
  • Class Pace: Certification courses are fast-paced and adhere to a strict schedule, ensuring all exam objectives are covered. While discussions and examples are included, the focus remains on certification readiness.
  • Outside of Class Study Time: Plan for self-study outside of class, especially if the concepts are new to you. Passing the exam is not guaranteed.

AIOps Foundation Certification Course Outline

Learning Objectives

Required Pre-Course Preparation

• Review all pre-course communication from Learning Tree before class to understand how to access all your course materials.
• Ensure you have access to your My Learning Tree portals before the start of class. Reach out to Learning Tree customer service if you are having trouble.

Module 1: Establish the AIOps Foundation

• Trace the history and predecessors of AIOps
• Define AIOps and distinguish it from IT operations analytics
• Understand core technologies and concepts
• Explore the stages of an AIOps system
• Identify overlapping practices

Module 2: AIOps Transforms the Organization

• Understand the drivers for AIOps adoption
• Bridge AIOps and DevOps
• Support Site Reliability Engineering with AIOps
• Integrate AIOps with security practices
• Address data, telemetry, and system complexity
• Understand a new paradigm for system state awareness

Module 3: Master Core Technologies – Data

• Understand big data fundamentals and the five V’s
• Examine characteristics of operational data
• Identify AIOps data sources and data types
• Navigate diverse and complex data environments

Module 4: Leverage Core Technologies – Machine Learning (ML)

• Understand AI and machine learning fundamentals
• Compare supervised and unsupervised learning
• Distinguish machine learning from analytics
• Understand model training and lifecycle concepts
• Explore the role of AIOps in the future of AI

Module 5: AIOps and Operational Metrics

• Understand operational performance metrics
• Track key metrics across systems and services
• Define objectives, indicators, and agreements
• Measure incident-related metrics (MTTD, MTBF, MTTA, MTTR)
• Support service level objectives and agreements

Module 6: AIOps Use Cases and Shifting the Organizational Mindset

• Shift from reactive to proactive operations
• Understand deterministic vs. probabilistic approaches
• Explore common AIOps use cases
• Assess the organizational impact of AIOps adoption
• Understand emerging trends and future direction

Module 7: Evaluating the Impact of AIOps

• Evaluate AIOps impact using operational metrics
• Align AIOps with DevOps and SRE practices
• Improve AI accuracy and system visibility
• Measure outcomes using incident and DORA metrics

Module 8: Implementing AIOps in the Organization

• Identify and mitigate common implementation challenges
• Address ethics, bias, and governance in machine learning
• Plan for AIOps adoption and rollout
• Ensure data quality and process readiness
• Address privacy, regulation, and compliance considerations