Study guide
13.2 VMware vSphere, Extensions & Other Agentless Ingestion Paths
Dynatrace Certified Associate Exam
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1 Chapter 1: Platform Overview, Deployment Models & Architecture
1.1 Dynatrace Platform Capabilities, Web UI Navigation & the Dynatrace Hub 1.2 SaaS vs. Dynatrace Managed Architecture & Security 1.3 Core Telemetry Components: OneAgent, ActiveGate & Grail Lakehouse 1.4 Licensing Models: Dynatrace Platform Subscription (DPS) & Davis Data Units (DDUs)
2 Chapter 2: ActiveGate Architecture & Network Topologies
2.1 ActiveGate Types: Environment vs. Cluster ActiveGates 2.2 ActiveGate Capabilities: Proxying, Private Synthetic, Cloud Integrations & Extensions 2.3 Network Zones, Multi-DC Routing & Firewall Traversal
3 Chapter 3: OneAgent Deployment, Configuration & Management
3.1 OneAgent Architecture, Components & Automatic Injection 3.2 Host Installation on Linux, Windows & Containerized Hosts 3.3 Cloud-Native Deployment: Dynatrace Operator on Kubernetes & OpenShift 3.4 Kubernetes Observability: Clusters, Nodes, Namespaces, Workloads & Events
4 Chapter 4: Monitoring Modes, Process Groups & Infrastructure Observability
4.1 Monitoring Modes: Full-Stack vs. Infrastructure-Only vs. Discovery 4.2 Process Groups, Process Instances & Custom Process Group Detection Rules 4.3 Host Groups, Host Naming Rules & Environmental Metadata Extraction 4.4 Infrastructure & Operations: Host Health, Disk, Network & Availability Analysis 4.5 Database Observability: The Databases App, Connection Pools & Storage Latency
5 Chapter 5: Smartscape Topology & Service Flow
5.1 The 5-Layer Smartscape Topology: Datacenter to Application 5.2 Service Detection: Web Services, Background Tasks & Custom Services 5.3 Service Flow, Backtrace Analysis & Horizontal/Vertical Dependency Mapping
6 Chapter 6: PurePath Distributed Tracing & Code-Level Diagnostics
6.1 PurePath Architecture, Distributed Tracing & Request Context Propagation 6.2 Method Hotspots, CPU Profiling & Memory Allocation Thread Analysis 6.3 Database Service Diagnostics, Slow Queries & Exception Analysis
7 Chapter 7: Davis AI Engine, Baselining & Causation
7.1 Davis Deterministic Causation Engine vs. Pure Correlation 7.2 Automated Dynamic Baselining, Percentiles & Traffic Seasonality 7.3 Event Ingestion, Event Types & Event Hierarchy in Problem Evolution
8 Chapter 8: Problem Lifecycle, Root Cause Analysis & Alerting
8.1 Problem Lifecycle, Severity Categories & Root Cause Cards 8.2 Alerting Profiles, Delay Filters & Management Zone Routing 8.3 Problem Notification Integrations: Webhooks, Slack, ServiceNow & ITSM
9 Chapter 9: Digital Experience Monitoring: Synthetic Monitoring
9.1 Synthetic Monitors: HTTP, Single-Step Browser & Multi-Step Clickpaths 9.2 Private Synthetic Locations, ActiveGate Engines & Credential Vault 9.3 Outage Detection, Performance Baselining & Maintenance Suppression
10 Chapter 10: Digital Experience Monitoring: Real User Monitoring (RUM)
10.1 RUM Architecture, OneAgent Script Injection & Mobile/OpenKit Telemetry 10.2 User Actions: Load vs. XHR/Fetch Actions, Visually Complete & Speed Index 10.3 Apdex Performance Ratings, User Sessions & Session Replay Masking
11 Chapter 11: Log Management and Analytics on Grail with DQL
11.1 Log Management on Grail: Storage, Ingestion Pipelines & Log Masking 11.2 Dynatrace Query Language (DQL): Fetch, Filter, Fields & String Operations 11.3 DQL Aggregations: Summarize, MakeTimeseries, Sorting & Visualizations
12 Chapter 12: Platform Governance, OpenTelemetry & Automation
12.1 Management Zones: Scoping Observability, Dashboards & Access Control 12.2 Tagging Strategies: Automated Rule-Based Tags, Manual Tags & AWS/K8s Metadata 12.3 OpenTelemetry Ingestion, OpenPipeline & Dynatrace Workflows Automation
13 Chapter 13: Cloud Integrations & Agentless Infrastructure Ingestion
13.1 AWS, Azure & Google Cloud Integrations 13.2 VMware vSphere, Extensions & Other Agentless Ingestion Paths
14 Chapter 14: Business Analytics & Business Events
14.1 Business Events: Capture, Ingestion & the bizevents Grail Table 14.2 Business Analytics: Connecting Performance to Revenue, Conversion & KPIs
15 Chapter 15: Notebooks, Dashboards & Data Visualization
15.1 Notebooks: Section-Based Investigation & Shareable Analysis 15.2 Dashboards: Tiles, Variables, Segments & Continuous Monitoring 15.3 Choosing Visualizations: Data Explorer, Chart Types & Metric Selection
16 Chapter 16: Reporting, Metrics & User Behavior Analysis
16.1 Metrics on Grail: Built-in Metrics, Granularity & Retention 16.2 Dashboard Reports, Subscriptions & Exporting Data via APIs 16.3 User Behavior Analysis: Bounce Rate, Landing & Exit Pages, Conversion Goals 16.4 Session Segmentation, User Journeys & Funnel Drop-Off Analysis
17 Chapter 17: Security, Vulnerability Analytics & Application Protection
17.1 Platform Security: Data Privacy, Encryption & Access Control 17.2 Third-Party Vulnerabilities, the Davis Security Score & Security Advisor 17.3 Code-Level Vulnerabilities, Attacks & Runtime Application Protection 17.4 Security Investigator, Notifications & the Remediation Workflow
13.2 VMware vSphere, Extensions & Other Agentless Ingestion Paths
Key Takeaways Explain This Topic AI
VMware Monitor Hub connects to vCenter to gather data on clusters, hosts, virtual machines, datastores, and disks, covering the hypervisor layer that an in-guest OneAgent cannot see.
Hypervisor visibility is what turns an unexplained CPU steal-time reading inside a guest into a named contended ESXi host.
Extensions collect from technologies that expose no agent surface — SNMP network devices, WMI, JMX, Prometheus endpoints, and message queues — and are activated from the Dynatrace Hub.
Remote and datasource-based extensions execute on an ActiveGate, which is why ActiveGate placement determines what an extension can reach.
For serverless and agentless workloads where no collector fits, the generic ingest APIs for metrics, logs, events, and business events remain the fallback path.
Last updated: September 2026
The official learning path's Ingestion lesson names three agentless routes explicitly: log ingestion, cloud integrations, and the VMware Monitor Hub . Section 13.1 covered the hyperscalers; this section covers the private-cloud and technology-specific paths that complete the Cloud Integrations and Ingestion portions of the blueprint.
VMware vSphere Monitoring
VMware Monitor Hub enables the ingestion of metrics and logs from VMware vSphere environments. It connects to vCenter to gather data on clusters, hosts, virtual machines, datastores, and disks, helping monitor health and performance.
The mechanics:
An ActiveGate connects to the vCenter Server API over HTTPS using a read-only service account. No software is installed on ESXi hosts.
Dynatrace discovers the vSphere inventory — datacenters, clusters, ESXi hosts, datastores, and virtual machines — and collects performance counters for each.
Where a VM also runs OneAgent, Dynatrace correlates the guest to its hypervisor , so a monitored host entity knows which ESXi host it is running on.
That correlation is the exam-relevant payoff. Section 4.4 established that CPU steal time inside a guest proves the hypervisor is withholding cycles but cannot say why . With vSphere monitoring in place, the picture completes: you can see that the ESXi host is over-committed, identify the noisy neighbour VM sharing it, and act. Without it, the investigation stops at "something outside the VM."
Datastore metrics matter for the same reason at the storage layer. A datastore approaching capacity or exhibiting high latency degrades every VM placed on it, which is the classic explanation for a group of unrelated application hosts all slowing down simultaneously with no shared application dependency.
Extensions
Many technologies expose no place to install an agent — a network switch, a storage array, a mainframe subsystem, a third-party appliance. Extensions are the packaged collectors for these, and they are browsed, installed, and managed from the Dynatrace Hub (Section 1.1).
Common extension datasources:
- Datasource Typical targets
- SNMP Switches, routers, firewalls, UPS units, storage arrays
- WMI Windows infrastructure counters and services
- JMX JVM-based middleware, application servers, brokers
- Prometheus Any workload exposing a /metrics endpoint
- SQL Query-based metrics from relational databases
- Python / scripted Bespoke APIs and vendor-specific protocols
Execution location is the detail exam questions target. Extensions run in one of two places:
On the host, via OneAgent — for datasources local to a monitored machine, such as reading a local JMX endpoint or a local log-derived metric.
Remotely, on an ActiveGate — for datasources polled over the network, such as SNMP devices or a remote SQL endpoint.
The consequence: an extension can only reach what its executing ActiveGate can reach. If a scenario describes SNMP devices in an isolated network segment, the answer is to place an ActiveGate inside that segment — the same network-zone reasoning as Section 2.3, applied to ingestion rather than to agent routing.
OpenTelemetry and Open Standards
Dynatrace ingests OpenTelemetry traces, metrics, and logs through the OTLP protocol, either directly to the platform endpoint or via an ActiveGate. This is the route for technologies OneAgent does not support, for teams with an existing OpenTelemetry investment, and for languages or runtimes outside the automatic-injection matrix. Chapter 12 covers OpenTelemetry and OpenPipeline in depth; the point here is that it belongs to the same agentless family — a way to get data in when automatic instrumentation is not an option.
The Generic Ingest APIs
When no extension, integration, or agent fits, Dynatrace exposes direct HTTP ingest endpoints:
- Metric ingest — line-protocol metric data points
- Log ingest — used in serverless environments or wherever OneAgent installation isn't possible
- Events ingest — deployment markers, configuration changes, custom annotations
- Business events ingest — JSON business records (Chapter 14)
These are the fallback of last resort in exam scenarios, and there is a cost dimension worth remembering: metric data points ingested this way are metered per data point (Section 1.4), so a poorly designed custom metric with high-cardinality dimensions can be dramatically more expensive than an equivalent extension.
Choosing the Right Ingestion Path
Work down this list and take the first match:
Can OneAgent be installed? Use it — it gives full-stack, code-level data automatically.
Is it a managed cloud service? Use the cloud integration for that provider (13.1).
Is it a hypervisor or virtual infrastructure? Use VMware vSphere monitoring.
Is there a Hub extension for the technology? Activate it, and place the executing ActiveGate where it can reach the target.
Does the workload already emit OpenTelemetry? Ingest OTLP.
None of the above? Use the generic ingest APIs, and design the data model carefully for cost.
Exam answers that skip straight to step 6 when step 1 or 4 would work are the most common trap in this topic.