Feature Management & Experimentation

Learn Feature Management & Experimentation through Harness University

Helps you manage feature releases, monitor performance, and run experiments for data-driven development.

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Self-Paced Training Instructor-Led Training Certifications

Self-Paced Training

Free self-paced courses that you can consume on your own time.

Harness Platform Fundamentals

Self-paced video course introducing the Harness Platform.

Product version: Free Plans of any module

Free

Introduction to AI Agents

Self-paced tidbit introducing the Custom AI Agents.

Product version: Paid Plans of any module

Free

Feature Management & Experimentation

Self-paced courses introducing Feature Management & Experimentation.

Product version: FME Paid Plans

Free

Instructor-Led Training

Intensive two-day courses are designed for engineers looking to deepen their understanding and expertise in Harness.

Introduction to the Harness Platform

Self-paced hands-on, prerequisite course to all module-specific ILT courses.

Product version: Paid Plans of any module

Paid

Feature Management & Experimentation

Deep dive into Feature Management & Experimentation use cases and concepts.

Product version: Harness Paid Plans

Paid

Certifications

Test and validate your knowledge of Harness by becoming a Harness Certified Expert.

For Developer

Feature Management & Experimentation - Developer

Feature Management & Experimentation - Developer badge

Product version: Harness FME Paid Plans

Assesses the fundamental skills to manage your applications with FME projects.

Review Study Guide

Topic	Material
1. Release Monitoring & Impact Detection
Purpose of Release Monitoring in FME	Release Monitoring Overview
Percentage-based rollouts and statistical validity	Rollouts
Configuration and monitoring impact on outcomes	Configure Release Monitoring
2. Metrics, Impressions & Attribution
How impressions and events generate metrics	Impressions, Events, and Metrics
When impressions are generated	Impression Tracking Behavior
Issues caused by disabled impressions or SDK limitations	SDK Limitations and Impression Tracking
3. Feature Flags, SDKs & Performance
Organizing feature flags for SDK performance	Harness FME SDKs and Customer-Deployed Components
Impression metadata and targeting rule labels	Impressions
Control, fallback, and evaluation failure behavior	Fallback Treatment
4. Experiment Design & Statistical Analysis
Experiment lifecycle and phases	Manage the Feature Flag Lifecycle
Hypothesis-driven experimentation	Create an Expirement
Statistical tradeoffs in analysis	Create an Expirement
5. Metrics Design & Measurement Strategy
Selecting metric types	Metrics
Configuring metrics correctly	Create and Configure Metrics
Metrics for alerting and monitoring	Metrics-Based Alerting
6. Alerting & Troubleshooting
Alert prerequisites	Alert Policy Requirements
Troubleshooting alerts not firing	Troubleshoot Metric and Significance Alerts
Alert lifecycle and auto-resolution	Alert Lifecycle and Resolution
7. Attribution, Rule Changes & Exclusions
Event timing and attribution	Event efficiency
Attribution behavior when rules change	Targeting Rule Changes and Attribution
User exclusion from metrics	Metric Exclusions and Filtering
8. Platform Navigation, Governance & RBAC
Core FME platform concepts	Harness Feature Management Overview
Dashboards and customer-level data	FME Dashboards
Governance, users, and RBAC	FME Governance and RBAC

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Exam Details

The Feature Management & Experimentation(FME) Developer exam tests your knowledge and skills of the Harness Feature Management & Experimentation module.

Prerequisites

• Basic terminal skills

• Basic understanding of on-premise or cloud architecture

Exam Details

Exam Type	Duration
Knowledge Exam	90 minutes

Covered Domain	Coverage
Release Monitoring & Impact Detection	12%
Metrics, Impressions & Attribution	15%
Feature Flags, SDKs & Performance	13%
Experiment Design & Statistical Analysis	15%
Metrics Design & Measurement Strategy	15%
Alerting & Troubleshooting	15%
Attribution, Rule Changes & Exclusions	8%
Platform Navigation, Governance & RBAC	7%

Exam Objectives

List of Objectives

The following is a detailed list of exam objectives:

#	Objective
1	Release Monitoring & Impact Detection
1.1	Explain the purpose of Release Monitoring in Harness FME.
1.2	Describe why percentage-based rollouts are required for statistically valid release monitoring.
1.3	Identify how configuration and monitoring choices affect release monitoring outcomes.
2	Metrics, Impressions & Attribution
2.1	Explain how impressions and events work together to calculate metrics.
2.2	Determine when impressions are generated and how impression tracking affects metric visibility.
2.3	Diagnose issues caused by disabling impression tracking or unsupported SDK behavior.
3	Feature Flags, SDKs & Performance
3.1	Apply best practices for organizing feature flags to optimize SDK performance.
3.2	Explain the role of impression metadata and targeting rule labels in debugging.
3.3	Differentiate control treatments, fallback treatments, and evaluation failure behavior.
4	Experiment Design & Statistical Analysis
4.1	Identify the correct lifecycle and phases of experimentation.
4.2	Explain the importance of hypothesis-driven experimentation and goal alignment.
4.3	Evaluate statistical tradeoffs in experiment analysis.
5	Metrics Design & Measurement Strategy
5.1	Select appropriate metric types based on experiment goals.
5.2	Configure metrics correctly to measure performance changes.
5.3	Explain how key metrics enable alerting and monitoring.
6	Alerting & Troubleshooting
6.1	Identify prerequisites required for metric alert policies to fire.
6.2	Diagnose why metric or significance alerts fail to fire.
6.3	Explain alert lifecycle behavior, including auto-resolution.
7	Attribution, Rule Changes & Exclusions
7.1	Explain how event timing affects metric attribution.
7.2	Analyze attribution behavior when targeting rules change.
7.3	Identify when user data is excluded from metric calculations.
8	Platform Navigation, Governance & RBAC
8.1	Describe core Harness FME platform concepts and structure.
8.2	Navigate operational dashboards and customer-level data.
8.3	Apply governance rules for users, groups, naming, and traffic types.

Next Steps

The Feature Management & Experimentation Developer exam can start immediately after registering. Please allow up to 90 mins to complete the knowledge exam.

1. Create an account in Harness University

2. Review the Study Guide above.

3. Register for an exam.

4. Take the exam.

Register for Exam

For Administrator

Feature Management & Experimentation - Administrator (BETA COMING SOON)

Feature Management & Experimentation - Administrator (BETA COMING SOON) badge

Product version: Harness FME Paid Plans

Coming soon

This certification is in beta and not yet open for registration.

Assesses the fundamental skills to deploy and maintain FME projects and the overall Harness Platform.

For Architect

Feature Management & Experimentation - Architect (BETA COMING SOON)

Feature Management & Experimentation - Architect (BETA COMING SOON) badge

Product version: Harness FME Paid Plans

Coming soon

This certification is in beta and not yet open for registration.

Assess key technical job functions and advanced skills in design, implementation and management of FME.