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| Section | Objectives |
|---|---|
| Application Performance Monitoring (APM) | - Service flow analysis - Distributed tracing |
| Dashboards, Reporting & Automation | - Automation and API integration - Dashboards and metrics visualization |
| Dynatrace Platform Fundamentals | - Architecture and core components - OneAgent and data collection concepts |
| Log Analytics & Troubleshooting | - Problem diagnostics and root cause analysis - Log search and filtering |
| Infrastructure Observability | - Cloud and container monitoring - Host and process monitoring |
| Synthetic Monitoring & User Experience | - Synthetic tests and availability monitoring - Real User Monitoring (RUM) |
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NEW QUESTION # 57
Which of these data sources can I utilize when a Kubernetes cluster is being monitored?
Answer: B,C,E
Explanation:
When monitoring Kubernetes, Dynatrace provides multiple relevant data sources:
* Prometheus metrics - ingested and analyzed alongside other metrics
* Infrastructure-level metrics - CPU, memory, disk usage from nodes
* Kubernetes-specific events and metrics - pods, deployments, namespaces, cluster events Other options:
* Code-level insights relate to application tracing, not Kubernetes data sources directly
* OpenKit is used for custom user session tracking, not Kubernetes monitoring Reference: Based on official Dynatrace University training materials on Kubernetes monitoring.
NEW QUESTION # 58
A problem opened with the event category of "Slowdown". Assuming Dynatrace is using the default anomaly detection settings, what entity types can be the root cause of this problem? (Select all that apply)
Answer: B,C,D
Explanation:
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.
Reference: Based on official Dynatrace University training materials covering problem detection and Davis AI root cause analysis.
NEW QUESTION # 59
Which of the following best describes the functionality of the Security Investigator app?
Answer: D
Explanation:
The Security Investigator app in Dynatrace is designed for advanced security analysis and threat investigation using Dynatrace Query Language (DQL).
It enables users to:
Run and chain multiple queries across logs, metrics, traces, and events Correlate different data types stored in Grail Perform deep investigations into potential security incidents This query-driven approach allows analysts to explore data relationships and uncover threats efficiently.
Other options describe capabilities not related to this app:
Credential vault checks are unrelated
Session investigation is part of RUM and Session Replay
Memory profiling is not a feature of Security Investigator
Reference: Based on official Dynatrace University training materials on Security Investigator and Grail.
NEW QUESTION # 60
I want to reduce the mean-time-to-identify critical security vulnerabilities in my environment by sending a notification to the correct team's Slack channel when detected. How can I achieve this?
Answer: C
Explanation:
Dynatrace enables automated response through Workflows :
* Event triggers detect security issues
* Ownership ensures the correct team is identified
* Slack integration sends notifications automatically
This automation significantly reduces detection and response time.
Manual checking options do not meet the requirement for automation.
Reference: Based on official Dynatrace University training materials on workflows and automation.
NEW QUESTION # 61
For supported technologies, do Kubernetes workloads and pods get monitored differently than traditionally monitored OneAgent processes?
Answer: E
Explanation:
Yes, Kubernetes workloads and pods are monitored differently compared to traditional OneAgent-monitored processes. When Dynatrace is deployed into Kubernetes using the Dynatrace Operator, the OneAgent automatically injects itself into each pod at runtime. This means each container is monitored independently and is shown in Dynatrace as a container entity , not as a traditional process.
This container-based visibility is designed to suit the dynamic and short-lived nature of Kubernetes environments. Traditional OneAgent monitoring associates metrics and topology with processes and hosts, but in Kubernetes, the emphasis shifts toward containers, pods, and workloads. This shift allows Dynatrace to provide accurate and timely data on what's really happening inside containerized apps, aligned with Kubernetes-native objects and metrics.
Other answer options either misstate the behavior (e.g., no service data or requiring OpenTelemetry) or oversimplify the relationship between traditional and Kubernetes monitoring (e.g., "same general information"), making Option E the only correct and fully accurate one.
Reference: Based on official Dynatrace University training content and architecture documentation.
NEW QUESTION # 62
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