Opleiding: DASA DevAIOps - Including Exam [DASADEV-AIOPS]

OVERVIEW

Adopt DevAIOps practices effectively, and achieve faster time-to-market and time-to-value.

This two-day certification course offers a comprehensive understanding of how to integrate AI into DevOps practices, driving operational efficiency, automation, and innovation. Participants will explore ways to integrate AI into their CI/CD pipelines, reliability, security, and platform engineering practices, while learning how to leverage AI-powered tools for continuous improvement and efficient product delivery.

This certification program equips IT professionals to implement DevAIOps practices effectively, achieving faster time-to-market and time-to-value, and iterative development.

Updated 11/6/2026

OBJECTIVES

After completing this program, you will be able to:

• Build mental models to reason about, configure, and troubleshoot AI-powered tools used in DevOps environments.
• Design and work with intelligent CI/CD pipelines that improve delivery speed, quality, and automation.
• Shift toward predictive operations by interpreting, configuring, and critically evaluating AI-powered observability outputs.
• Design, implement, and operate an MLOps platform on top of existing DevOps infrastructure that governs the full model lifecycle.
• Apply a security framework for AI-powered systems spanning the CI/CD pipeline, runtime environment, and AI-specific attack surface.
• Design and operate a platform that delivers AI infrastructure as self-service developer capabilities with optimized cost, performance, and compliance.
• Implement a governance framework that ensures AI systems meet regulatory, ethical, and organizational accountability requirements throughout the model lifecycle.

AUDIENCE

CERTIFICATION

Certificate: DASA DevAIOps

Exam Details

• Delivery: Online Proctored
• Format: Closed-book
• Proctoring: Web Proctored
• Duration: 60 minutes
• Questions: 40 Multiple Choice
• Pass Grade: 60%

CONTENT

Module 00: Programme Orientation

• Understand the learning path, assessments, and certification requirements.
• Explore DevAIOps maturity, current trends, and the growing need for AI skills in delivery teams.
• Review the FinTech case study covering release delays, rising costs, and AI readiness challenges.

Module 01: AI Fundamentals for DevOps Teams

• Learn key model types such as classification, regression, anomaly detection, and NLP.
• Understand model training, validation, drift, and their impact on reliability.
• Interpret AI outputs including alerts, confidence scores, and code suggestions.

Module 02: Intelligent CI/CD Pipelines

• Apply AI-assisted code review to improve quality and reduce manual effort.
• Use intelligent test prioritisation to balance speed, coverage, and risk.
• Enable predictive deployment gates, rollback logic, and self-healing responses.

Module 03: AI Augmented Observability and AIOps

• Detect issues earlier using anomaly detection and dynamic baselines.
• Reduce alert noise through event correlation and root cause analysis.
• Improve resilience with predictive scaling and faster incident response.

Module 04: MLOps & the AI Model Lifecycle in a DevOps Context

• Build model versioning, promotion workflows, and reproducible delivery processes.
• Monitor model performance, detect drift, and trigger retraining when needed.
• Use feature stores to improve consistency between training and live environments.

Module 05: DevSecOps with AI

• Apply AI-powered SAST, DAST, and dependency scanning across CI/CD pipelines.
• Use anomaly detection for runtime monitoring across containers and networks.
• Address AI-specific risks such as prompt injection, model poisoning, and data exposure.

Module 06: Platform Engineering and AI Structure

• Create self-service platforms and golden paths for AI delivery teams.
• Configure Kubernetes and GPU resources for training and inference workloads.
• Compare cloud and self-hosted options based on cost, latency, and compliance needs.

Module 07: AI Governance, Ethics and Regulatory Compliance

• Apply EU AI Act and GDPR requirements to technical delivery processes.
• Use model cards, lineage tracking, and documentation as code.
• Monitor fairness, define approval models, and manage AI incidents effectively.