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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Digital Experience Management (DEM) | 16% | - User session analysis - Mobile monitoring (iOS & Android) - Real User Monitoring (RUM) - Synthetic monitoring |
| Topic 2: Components & Architecture | 8% | - Grail data lakehouse - OneAgent, ActiveGate, Dynatrace Platform - Dynatrace structure and components - Integration with 3rd party systems - System communication and data reporting |
| Topic 3: Problems & Resolution | 14% | - Alerts and notifications - Problem lifecycle and root cause analysis - Davis AI anomaly detection - Event correlation |
| Topic 4: Reporting & Analysis | 18% | - Business analytics and KPIs - Log management and analytics - Notebooks and investigation - Dashboards and visualization - DQL (Dynatrace Query Language) |
| Topic 5: Capabilities & Monitoring | 24% | - Data gathering and analysis - Full-stack performance monitoring of applications - Smartscape real-time topology - PurePath distributed tracing - How Dynatrace operates in your environment |
| Topic 6: Installation & Configuration | 20% | - OneAgent deployment - Application instrumentation - Tags and management zones - ActiveGate setup - Infrastructure monitoring configuration |
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NEW QUESTION # 41
Which of these data sources can I utilize when a Kubernetes cluster is being monitored?
Answer: A,C,D
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 # 42
Which of the following is the default threshold type used for anomaly detection on Hosts (CPU saturation, memory saturation, disk utilization...)?
Answer: D
Explanation:
Dynatrace uses Automated Multi-Dimensional Baselining as the default anomaly detection method for host metrics such as CPU, memory, and disk utilization.
This approach:
* Automatically learns normal behavior patterns over time
* Adjusts thresholds dynamically based on historical data
* Considers multiple dimensions such as time of day and workload patterns This allows Dynatrace to detect anomalies more accurately compared to fixed thresholds.
Static thresholds are manually defined and not the default.
Other options are unrelated to anomaly detection mechanisms.
Reference: Based on official Dynatrace University training materials covering anomaly detection and Davis AI.
NEW QUESTION # 43
Which of the following data points can be found within a User Session?
Answer: A,B,C,E
Explanation:
A User Session in Dynatrace contains detailed information about user interactions and experience:
* User Tags - custom identifiers assigned to users
* Session and Action Properties - metadata describing session behavior
* JavaScript and Request Errors - frontend and backend errors impacting the session
* User Actions - all interactions such as clicks, page loads, and XHR calls Server-side code-level insights are not directly part of the user session view; they belong to service-level and distributed tracing analysis.
Reference: Based on official Dynatrace University training materials on Real User Monitoring and user sessions.
NEW QUESTION # 44
Which of the following properties are added to your ingested log data when trace enrichment for logs is enabled? (Select all that apply)
Answer: B,D,E
Explanation:
When trace enrichment for logs is enabled in Dynatrace, additional metadata is automatically added to logs to correlate them with distributed traces.
The key properties added include:
* dt.trace_id - uniquely identifies the distributed trace
* dt.span_id - identifies the specific span within the trace
* dt.entity.process_group_instance - links the log to the specific monitored process instance These attributes enable:
* Seamless correlation between logs and traces
* End-to-end observability across services
* Faster root cause analysis by linking logs directly to transactions
The other options are not part of trace enrichment:
* dt.security_context is related to log access control, not trace enrichment
* DT_RELEASE_VERSION is used for versioning metadata, not tracing
Reference: Based on official Dynatrace University training materials covering log monitoring and trace-log correlation.
NEW QUESTION # 45
What sort of outage occurs when all Synthetic Locations simultaneously experience 1-5 consecutive failures?
Answer: D
Explanation:
A Global outage is detected when all configured Synthetic locations report failures at the same time within a defined number of consecutive failed executions. This indicates that the issue is not location-specific but instead affects the application or service globally.
Dynatrace Synthetic Monitoring differentiates between:
* Local outages , where only a subset of locations fail (indicating regional or network-specific issues)
* Global outages , where all locations fail simultaneously, indicating a widespread availability problem Options such as Public Synthetics and Private Synthetics refer to types of Synthetic locations, not outage classifications.
Reference: Based on official Dynatrace University training materials covering Synthetic Monitoring and outage detection.
NEW QUESTION # 46
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