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Agentless RUM enables Real User Monitoring without requiring OneAgent installation on the server. It works by injecting a JavaScript snippet into the web application, which captures user interactions and performance data directly from the browser.

This approach is useful when:

You do not have access to the server

Installing OneAgent is not possible

You still need frontend visibility

Other options do not apply:

Dynatrace Operator is for Kubernetes deployments

ActiveGate is used for routing and extending connectivity

Discovery mode still requires OneAgent installation

To analyze logs in Dynatrace, two essential steps are required:

Ingest logs into Dynatrace – logs must first be collected and stored in the platform (Grail)

Use DQL (Dynatrace Query Language) – to query, filter, and analyze the ingested log data

These steps allow users to extract insights, detect issues, and correlate logs with other observability data.

Other options are not part of log analysis:

RUM is for user experience monitoring

Distributed Traces are for request tracking

Operator is for Kubernetes deployment

Dynatrace Problems can be viewed across key monitored entities:

Processes

Hosts

Applications

Services

These are core topology layers where Davis AI correlates issues.

Other options:

Synthetics and Data Center are not primary topology levels for problem views

Distributed tracing provides deep visibility into application behavior, which leads to multiple business and operational benefits:

Faster time to market through quicker issue resolution

Improved internal collaboration by providing shared visibility across teams

Improved SLA compliance through better monitoring and reliability

Reduced MTTD and MTTR by enabling faster detection and troubleshooting

Protected business outcomes by minimizing downtime and performance issues

Session replay is a Digital Experience feature and not a direct benefit of distributed tracing.

Dynatrace provides multiple methods to ingest OpenTelemetry data, each suited for different deployment scenarios:

Dynatrace ActiveGate API (Option B): ActiveGate serves as a central collection gateway to the Dynatrace backend, providing OTLP endpoints for OpenTelemetry data. It requires an access token for authentication and a secure connection using TLS. This method simplifies and centralizes network traffic and management.

Dynatrace SaaS API (Option C): The SaaS ingest API offers a straightforward approach to ingest OpenTelemetry data. Applications can be configured for standard OTLP export without the need for additional installed components or services. It also requires an access token and a secure TLS connection.

Dynatrace OneAgent (Option D): OneAgent provides a local-only endpoint for trace ingestion. This method offers automatic request enrichment and does not require explicit authentication. However, it only accepts traces, not metrics or logs, and is limited to localhost connections.

Incorrect Options:

Dynatrace RUM (Option A): Real User Monitoring (RUM) is designed to capture and analyze user interactions within web applications. It does not serve as a method to ingest OpenTelemetry data.

Dynatrace Operator (Option E): The Dynatrace Operator is a Kubernetes operator that automates the deployment and management of OneAgent in Kubernetes environments. While it facilitates monitoring within Kubernetes, it is not a direct method for ingesting OpenTelemetry data.

Log Events allow Dynatrace to detect specific patterns (such as error messages) in logs and automatically trigger alerts.

They enable:

Detection of error patterns in logs

Creation of events/problems

Notification of support teams

Other options:

Log Processing rules transform logs, not alert

Anomaly detection rules are metric-based

Log Metrics convert logs into metrics but don’t directly alert

Log Viewer is for manual inspection

Dynatrace recommends selecting the appropriate OneAgent mode depending on the role of the host:

Infrastructure mode is recommended when only host-level monitoring is required with minimal overhead

Full-Stack mode is used when deep monitoring is needed, including process-level visibility and full observability

Options like Full support , Discovery , and Half stack are not valid OneAgent monitoring modes in Dynatrace.

In Dynatrace, “Slowdown” problems are typically triggered by performance degradation detected through anomaly detection on monitored entities.

With default settings, the root cause of slowdown problems is usually identified at:

Services, where response time degradation is detected

Processes, where CPU, memory, or other resource bottlenecks occur

Hosts, where infrastructure-level issues impact performance

Applications are higher-level aggregations and are not typically identified as the root cause entity.

Cloud Providers are not treated as root cause entities in anomaly detection.

Dynatrace’s AI (Davis) correlates these entities to pinpoint the most likely root cause in the topology.

In distributed tracing, Dynatrace follows standard tracing protocols (such as W3C Trace Context), where the trace ID is propagated via HTTP headers between services.

This enables:

End-to-end trace continuity across services

Correlation of requests across distributed systems

Seamless tracking through microservices architectures

The trace context is typically included in headers like traceparent, ensuring each downstream service continues the same trace.

Other options are incorrect because:

XML and JSON payloads are not used for trace propagation

TLS handshake is unrelated to trace context propagation

The dt.security.context attribute enables fine-grained access control over ingested data, including Business Events.

It allows:

Segmentation of data

Restricting access to specific users or groups

Applying security boundaries to sensitive datasets

Other attributes are related to tracing:

dt.traceid and dt.spanid are for distributed tracing

dt.entity.process_group_instance links data to entities

Only dt.security.context provides control over data access and segmentation.