I85PREP
⌘ K
Dynatrace Professional study materials
Dynatrace Professional study resources

Dynatrace Professional practice questions

One question at a time. Build your Dynatrace Professional understanding.

First 50 of 200 questions. Each one lists the choices, the correct answer, and an explanation. The checker at the top of this page saves progress in this browser when it is available.

Continue in the practice tool above to load more questions, 50 at a time.

Question 1AI Observability

In Dynatrace, which capability is responsible for tracing a single user request across services, queues, and third-party APIs so that latency can be attributed to each hop?

  1. Host resource detection
  2. Synthetic browser monitors
  3. Log ingestion rules
  4. PurePath distributed tracing Correct answer.

Explanation

Distributed tracing in Dynatrace is delivered through PurePaths, which stitch together service calls, database statements, and remote calls belonging to one transaction. Each node in the trace carries timing so latency can be attributed to a specific tier or call.

Question 2AI Observability

A team wants Dynatrace to automatically discover and monitor a new Java service deployed to Kubernetes without editing code. Which mechanism provides this out-of-the-box instrumentation?

  1. A synthetic clickpath executed after each deployment
  2. Manual OpenTelemetry SDK initialization in the application source
  3. OneAgent code-module injection at the process level Correct answer.
  4. A Prometheus exporter sidecar configured per pod

Explanation

OneAgent injects language-specific code modules into supported runtimes at process start, so services are instrumented without source changes. This is the basis of automatic full-stack monitoring for Java, .NET, Node.js, and similar runtimes.

Question 3AI Observability

Which Dynatrace signal type is best suited for answering whether a REST endpoint's 95th-percentile response time degraded after a release?

  1. A host CPU saturation metric
  2. A vulnerability finding
  3. A service-level metric such as response time percentiles Correct answer.
  4. A deployment event annotation

Explanation

Service metrics aggregate request timings and expose percentile series such as the 95th percentile response time, which directly answers the latency question. Events annotate the timeline but do not themselves measure latency distributions.

Question 4AI Observability

What is the primary role of a Davis anomaly detector in Dynatrace AI observability?

  1. To store raw trace data for later export
  2. To encrypt communication between OneAgents and the cluster
  3. To define which users may view dashboards
  4. To establish a learned baseline for a metric or event and flag statistically unusual behavior Correct answer.

Explanation

Davis builds baselines from historical behavior and raises problems when observed values deviate significantly, reducing reliance on static thresholds. Detectors can be tuned per metric or entity when automatic baselining is unsuitable.

Question 5AI Observability

A problem card in Dynatrace lists a root cause entity and several impacted entities. What does this relationship express?

  1. The impacted entities are always downstream of the root cause in the same causal chain
  2. Impacted entities are unrelated alerts merged for convenience
  3. The root cause is chosen randomly among affected entities
  4. The root cause entity is the one Davis identified as the origin, while impacted entities show downstream or related effect Correct answer.

Explanation

Davis correlates related anomalies into a single problem, designating the entity where the causal chain starts as root cause and listing entities that show resulting symptoms as impacted. This lets responders focus on the origin rather than every symptom.

Question 6AI Observability

Which Dynatrace feature lets you ask natural-language questions about your observability data and receive generated answers grounded in your environment?

  1. Management zones
  2. Davis CoPilot Correct answer.
  3. Data privacy settings
  4. Maintenance windows

Explanation

Davis CoPilot is the generative-AI assistant that interprets natural-language prompts and produces explanations, queries, and summaries using the tenant's observability context. It is distinct from the anomaly-detection engine itself.

Question 7AI Observability

Which statement correctly distinguishes a metric from a log in Dynatrace observability?

  1. Metrics are numeric time series suited to aggregation and alerting, while logs are timestamped text records suited to detailed event context Correct answer.
  2. Metrics are unstructured text records, while logs are numeric time series
  3. Metrics cannot be used in dashboards, while logs can
  4. Logs are always sampled, while metrics are always complete

Explanation

Metrics are numeric measurements over time that aggregate well for trending and alerting, whereas logs capture discrete textual events with rich context. Both can be visualized, but they serve different diagnostic purposes.

Question 8AI Observability

A service shows elevated failure rate only for requests originating from a specific geographic region. Which Dynatrace analysis approach most directly isolates the cause?

  1. Increase the anomaly detector sensitivity for the whole service
  2. Filter the service's traces by request attribute or dimension such as client geography and compare failing versus successful requests Correct answer.
  3. Disable alerting for the service until the issue recurs
  4. Review host disk utilization across all hosts

Explanation

Segmenting traces by a request dimension isolates the subset exhibiting failures and allows comparison against healthy requests, revealing whether a specific dependency or code path is involved. Broad detector changes or alert suppression would obscure rather than localize the cause.

Question 9AI Observability

An application team reports that Davis raised a problem for slow response time, but the underlying service shows normal CPU and memory. Which explanation is most plausible?

  1. Slow response time problems cannot occur without host saturation
  2. Davis only evaluates infrastructure metrics, so the problem must be misconfigured
  3. The latency may originate in a downstream dependency such as a database or third-party API called by the service Correct answer.
  4. The service must be excluded from monitoring

Explanation

Service response time includes time spent waiting on downstream calls, so a dependency bottleneck can degrade latency while local resource usage stays low. Trace inspection of the slowest requests identifies which call dominates the time.

Question 10AI Observability

You want to alert when a custom business metric, such as orders per minute, drops below its expected seasonal pattern. Which Dynatrace configuration best fits?

  1. A custom metric ingested via the metrics API with a Davis anomaly detector using automatic baselining Correct answer.
  2. A synthetic monitor on the checkout page
  3. A static threshold alert on host CPU
  4. A log processing rule that drops order logs

Explanation

Ingesting the business metric through the metrics API makes it available to Davis, and automatic baselining accounts for seasonal patterns better than a fixed threshold. This ties alerting to the actual business signal.

Question 11AI Observability

Which OpenTelemetry ingestion path allows Dynatrace to receive traces from services instrumented with the OpenTelemetry SDK without deploying OneAgent on those hosts?

  1. Enabling a maintenance window for the host
  2. Configuring a synthetic monitor to call the service
  3. Sending syslog messages to the OneAgent log endpoint
  4. Exporting OTLP traces to the Dynatrace trace ingest endpoint Correct answer.

Explanation

Dynatrace accepts OTLP trace data through its trace ingest API, enabling OpenTelemetry-instrumented services to report spans without a OneAgent. The spans are then correlated into distributed traces within the tenant.

Question 12AI Observability

A problem's impact analysis shows 40 percent of users affected. What does this figure represent in Dynatrace?

  1. The percentage of hosts in the environment experiencing the anomaly
  2. The estimated share of real user traffic impacted by the problem based on observed user sessions and request volume Correct answer.
  3. The percentage of dashboards that display the affected service
  4. The ratio of resolved to open problems

Explanation

Impact is estimated from real user monitoring and request data to express how much actual traffic is affected, which helps prioritize response. It is a business-relevant measure rather than an infrastructure count.

Question 13AI Observability

When configuring a Davis anomaly detector for a metric with strong weekly seasonality, which setting most reduces false positives?

  1. Using automatic baselining that learns seasonal patterns, or a seasonal reference period if offered Correct answer.
  2. Disabling the detector entirely
  3. Setting a static threshold equal to the all-time maximum
  4. Removing the metric from all dashboards

Explanation

Seasonality-aware baselining compares current values against the same phase of the seasonal cycle, avoiding alerts caused by normal weekly swings. Static thresholds cannot capture recurring patterns and generate noise.

Question 14AI Observability

Which Dynatrace concept groups related services and hosts so that a problem on a shared backend is correlated with the front-end services that depend on it?

  1. The dependency or call graph built from observed communication between entities Correct answer.
  2. The alerting profile severity rules
  3. Management zone membership only
  4. The maintenance window schedule

Explanation

Dynatrace builds a topology from observed calls between services, hosts, and processes, and uses that graph to correlate anomalies into a single problem with a root cause. Management zones and alerting profiles govern access and notification, not causal correlation.

Question 15AI Observability

A team needs to know whether a newly released version increased error rates compared with the previous version. Which approach uses Dynatrace capabilities most directly?

  1. Check host memory usage trends for the cluster
  2. Review the vulnerability report for the service
  3. Inspect the Kubernetes node count
  4. Compare service error-rate metrics segmented by release version before and after the deployment event Correct answer.

Explanation

Segmenting error-rate metrics by a version dimension and aligning them with the deployment event isolates the release's effect on errors. This controlled comparison is more informative than unrelated infrastructure trends.

Question 16AI Observability

What is the purpose of a request attribute in Dynatrace when analyzing application behavior?

  1. To control which users can access the environment
  2. To set the retention period for log data
  3. To define which hosts are monitored by OneAgent
  4. To capture a value from a request, such as a customer tier or order ID, so traces and metrics can be filtered and segmented by it Correct answer.

Explanation

Request attributes extract business-relevant values from requests, enabling filtering, segmentation, and metric creation based on those values. They are a core tool for tying observability data to business context.

Question 17AI Observability

An SLO for checkout availability is defined as 99.9 percent over 30 days. Which Dynatrace capability tracks progress toward that objective and shows error budget consumption?

  1. Synthetic monitors alone
  2. Service-level objectives configured on a metric with error-budget reporting Correct answer.
  3. Log ingestion rules
  4. Maintenance windows applied to the checkout service

Explanation

Dynatrace SLOs evaluate a chosen metric against a target over a rolling window and report remaining error budget, linking reliability to business commitments. Other features support operations but do not compute SLO attainment.

Question 18AI Observability

A trace shows a span for a database call consuming most of the request duration, but the database host shows low CPU. Which conclusion is best supported?

  1. The application service is not instrumented
  2. The call may be waiting on locks, network round-trip, or query inefficiency rather than CPU-bound work Correct answer.
  3. The database host must be under-provisioned and needs more CPU
  4. The trace data must be corrupted because CPU is low

Explanation

High span duration with low database CPU points to non-CPU bottlenecks such as lock contention, slow queries, or network latency. Examining the statement and connection details in the trace narrows the specific cause.

Question 19AI Observability

Two services report anomalies within the same minute, but only one is designated root cause. Which factor most influences that designation in Dynatrace?

  1. The causal dependency direction and timing of anomalies along the observed call graph Correct answer.
  2. The alphabetical order of service names
  3. The number of dashboards referencing each service
  4. The age of each service entity

Explanation

Davis uses the observed dependency graph and the temporal order of anomalies to infer which entity is upstream and therefore causal. Downstream anomalies that follow an upstream one are treated as impact rather than origin.

Question 20AI Observability

A custom metric is ingested with high cardinality dimensions such as user ID, causing storage and query cost to rise sharply. Which remediation best preserves analytical value while controlling cost?

  1. Remove all dimensions from the metric
  2. Increase the metric's ingestion frequency to compensate
  3. Replace high-cardinality identifiers with bounded dimensions such as region or tier, and keep per-user detail in logs or traces Correct answer.
  4. Disable Davis for the entire environment

Explanation

High-cardinality identifiers explode metric series and inflate cost, while bounded dimensions retain most analytical value for aggregation. Per-request identifiers belong in traces or logs, which are better suited to individual-level detail.

Question 21AI Observability

An organization wants generative-AI assistance to explain a problem using its own environment data while preventing sensitive fields from being exposed. Which combination of controls is most appropriate?

  1. Disable all log ingestion and rely on metrics only
  2. Enable Davis CoPilot and apply data-privacy masking settings to sensitive request and log fields Correct answer.
  3. Turn off anomaly detection for services handling sensitive data
  4. Grant every user environment-admin permissions

Explanation

CoPilot answers are grounded in tenant data, so masking sensitive fields through data-privacy settings limits exposure while retaining assistance. Broad disabling of signals or over-permissioning does not address the privacy requirement.

Question 22AI Observability

A synthetic monitor reports healthy checkout responses, yet real-user data shows a high failure rate for mobile users only. Which explanation is most consistent with these signals?

  1. Real-user monitoring data is always unreliable compared with synthetic results
  2. The synthetic monitor is broken and should be deleted
  3. The synthetic script likely exercises a desktop-like path that does not reproduce the mobile-specific failure Correct answer.
  4. Mobile users are excluded from monitoring by default

Explanation

Synthetic monitors validate scripted paths from fixed locations and devices, so they can miss client-specific failures such as mobile-only rendering or API behavior. Combining synthetic and real-user signals localizes the gap to the mobile path.

Question 23Application Observability

In Dynatrace, which entity type represents a single monitored process instance, such as one JVM running on a host, and is the parent of that process's services?

  1. PROCESS_GROUP_INSTANCE Correct answer.
  2. APPLICATION
  3. HOST
  4. SERVICE

Explanation

A PROCESS_GROUP_INSTANCE models one concrete runtime process, for example a specific JVM, and sits above the services that execute inside it. The process group aggregates instances of the same deployment, while services are detected technologies running within the process.

Question 24Application Observability

A service's response time in Dynatrace is composed of which main contributors?

  1. Only the network latency between the browser and the web server
  2. Only the time spent waiting on database calls
  3. The service's own code execution plus time spent in called downstream services and databases Correct answer.
  4. The CPU throttling percentage of the host only

Explanation

Response time is attributed to self time of the service plus time consumed by downstream calls such as other services, databases, and queues. This breakdown lets you see whether slowness originates in the service's own code or in a dependency.

Question 25Application Observability

Which Dynatrace capability automatically discovers and instruments application processes without manual code changes, using agents and OneAgent technology?

  1. Manual tagging via the API
  2. Dashboard JSON export
  3. Synthetic monitors
  4. Auto-injection and deep monitoring by OneAgent Correct answer.

Explanation

OneAgent performs automatic process discovery and code-level instrumentation, injecting deep monitoring for supported technologies. This removes the need to modify application code to gain method-level visibility.

Question 26Application Observability

A team wants to know which user actions in a web application are slowest for real users. Which Dynatrace data source should they examine?

  1. Real User Monitoring user action data Correct answer.
  2. PurePath distributed traces of backend services only
  3. Kubernetes event feed
  4. Host process list

Explanation

Real User Monitoring captures user actions in the browser, including their duration and contributing front-end and back-end timings. It reflects actual user experience rather than only server-side execution.

Question 27Application Observability

Which statement best describes a PurePath in Dynatrace?

  1. A snapshot of host CPU utilization over time
  2. A list of all deployed application versions
  3. A distributed trace capturing the call sequence of a single transaction across services and tiers Correct answer.
  4. A configuration file that defines alerting thresholds

Explanation

A PurePath is a transaction-level distributed trace that records the timing and sequence of calls across processes and services. It is used to pinpoint where time is spent within an individual request.

Question 28Application Observability

What is the primary purpose of a Dynatrace service-level request naming rule?

  1. To rename hosts for easier identification
  2. To encrypt traffic between services
  3. To group requests into meaningful service request names based on URL or method patterns Correct answer.
  4. To schedule synthetic executions

Explanation

Request naming rules define how incoming requests are grouped into named request types, which drives meaningful service metrics and analysis. Without good naming, requests collapse into generic buckets and lose analytical value.

Question 29Application Observability

Which Dynatrace feature lets you follow a single request from the browser through the web server, application service, and database in one view?

  1. The anomaly detection settings page
  2. The deployment status page
  3. The host overview page
  4. Distributed tracing with PurePath correlation Correct answer.

Explanation

Distributed tracing correlates spans from the browser through backend tiers into a single trace, letting you follow one request end to end. This correlation is what makes cross-tier latency attribution possible.

Question 30Application Observability

A service shows high response time, but its self time is low and a downstream database call dominates. What is the most accurate conclusion?

  1. The service's own code is the bottleneck and should be optimized first
  2. The latency originates in the database dependency and should be investigated there Correct answer.
  3. The service is not instrumented and data is unreliable
  4. The host is out of memory and must be restarted

Explanation

When self time is low and a downstream call dominates, the delay is being introduced by the dependency rather than the service's own execution. Investigation should focus on the database tier, its queries, and its resource contention.

Question 31Application Observability

A developer wants method-level timing for a custom internal library that OneAgent does not instrument by default. Which approach is most appropriate?

  1. Disable deep monitoring to reduce overhead
  2. Add the library to deep monitoring via custom instrumentation settings Correct answer.
  3. Increase the host's CPU allocation
  4. Create a synthetic monitor for the library

Explanation

Deep monitoring can be extended to additional packages or classes through custom instrumentation configuration so OneAgent captures method-level timings. This is the supported mechanism for adding visibility to unsupported or custom code.

Question 32Application Observability

A service's failure rate spikes only for requests to a specific endpoint, while overall service health looks normal. Which analysis step best isolates the cause?

  1. Review the deployment status of unrelated services
  2. Restart all hosts running the service
  3. Disable anomaly detection for the service
  4. Filter the service's request-level metrics and traces to that endpoint and compare its error rate and exceptions Correct answer.

Explanation

Request-level filtering narrows analysis to the affected endpoint, where error rate, exceptions, and traces can be compared against healthy requests. This isolates whether the problem is endpoint-specific code or a shared dependency.

Question 33Application Observability

Why would a team configure a maintenance window in Dynatrace before a planned deployment?

  1. To disable OneAgent on all hosts permanently
  2. To permanently delete historical metrics
  3. To suppress alerting during expected disruption and avoid false anomaly notifications Correct answer.
  4. To force all services into a stopped state

Explanation

Maintenance windows suppress problem notifications during planned work so expected restarts or errors do not generate false alerts. Monitoring data continues to be collected, preserving history for later analysis.

Question 34Application Observability

A service request shows a large gap between its total response time and the sum of captured PurePath node times. What is the most likely explanation?

  1. The service has no dependencies
  2. Dynatrace always under-reports response time by half
  3. The PurePath was captured on the wrong host
  4. The request includes time in uninstrumented code or asynchronous work not captured as nodes Correct answer.

Explanation

Time not attributed to instrumented nodes typically represents uninstrumented code, asynchronous processing, or waiting that is not represented as a span. This gap is a signal to extend instrumentation or examine async behavior.

Question 35Application Observability

Which configuration determines whether OneAgent captures SQL statement details for database calls?

  1. The database capture setting within deep monitoring for the relevant technology Correct answer.
  2. The application's dashboard layout
  3. The synthetic monitor's script timeout
  4. The host's DNS resolver configuration

Explanation

Database capture settings within deep monitoring control whether SQL statements and their parameters are recorded for supported database technologies. These settings balance diagnostic depth against data sensitivity and overhead.

Question 36Application Observability

A team needs to alert only when the 95th percentile response time of a key service exceeds a threshold for five consecutive minutes. Which Dynatrace mechanism supports this?

  1. A host availability check
  2. A dashboard tile refresh interval
  3. A synthetic browser clickpath
  4. A custom metric event with a percentile aggregation and a sustained-duration condition Correct answer.

Explanation

Custom metric events evaluate a metric such as a response time percentile against a threshold and can require the condition to hold for a defined duration. This produces alerting aligned with the team's SLO-style criteria.

Question 37Application Observability

What does the Dynatrace Davis anomaly detection engine primarily do for application observability?

  1. It schedules synthetic monitors automatically
  2. It compiles application source code into binaries
  3. It replaces the need for any instrumentation
  4. It correlates events and metrics across entities to identify probable root causes of problems Correct answer.

Explanation

Davis analyzes correlated events, metrics, and topology to determine probable root causes and impact for detected problems. It reduces alert noise by grouping related symptoms under a single causal problem.

Question 38Application Observability

A service is marked as a key request but shows no data under its request-level view. Which cause is most plausible?

  1. The dashboard was recently edited
  2. The host has too many CPU cores
  3. The request naming rule does not match the actual request pattern Correct answer.
  4. The synthetic monitor is disabled

Explanation

If request naming rules do not match the real URL or method pattern, requests fall into other buckets and the intended key request shows no data. Verifying rule matching against actual requests is the first diagnostic step.

Question 39Application Observability

A team observes that errors in one service propagate as failed user actions in the browser. Which Dynatrace linkage makes this visible?

  1. Host-level disk metrics
  2. Correlation of backend service errors with the user actions that triggered them via RUM-to-service linking Correct answer.
  3. Manual log parsing only
  4. Kubernetes namespace quotas

Explanation

RUM links browser user actions to the backend services that handled them, so backend failures can be traced to the affected user actions. This end-to-end correlation shows business impact of service errors.

Question 40Application Observability

When analyzing a slow transaction, an architect sees repeated identical database calls within a single PurePath. What is the most useful next step?

  1. Check for an N+1 query pattern or missing caching in the application code Correct answer.
  2. Increase the database server's memory immediately
  3. Assume the trace is corrupted
  4. Disable database monitoring for the service

Explanation

Repeated identical queries in one trace commonly indicate an N+1 access pattern or absent caching, which is an application-level inefficiency. Fixing the code path reduces call volume and latency more effectively than scaling the database.

Question 41Application Observability

A service's response time baseline shifted upward gradually over two weeks without any deployment. Which Dynatrace analysis approach best identifies the cause?

  1. Disable anomaly detection for the service
  2. Compare the service's response time and throughput trends against dependency metrics and host resource trends over the same period Correct answer.
  3. Roll back the last deployment
  4. Delete the service and re-create it

Explanation

A gradual shift without deployment suggests environmental drift such as growing data volume, dependency degradation, or resource saturation. Correlating service trends with dependency and host trends over the same window reveals the correlated factor.

Question 42Application Observability

An application's user actions show high front-end time but low backend service time. Where should optimization effort be directed?

  1. Browser rendering, resource loading, and third-party scripts on the client side Correct answer.
  2. Backend garbage collection tuning
  3. Database query tuning on the backend
  4. Backend thread pool sizing

Explanation

When front-end time dominates and backend time is low, the delay occurs in the browser, such as rendering, asset loading, or third-party scripts. Backend tuning would not address the measured client-side cost.

Question 43Application Observability

A team wants to ensure that a critical service's problems are routed to the on-call channel with a specific severity while lower-priority services only create tickets. Which Dynatrace capability supports this?

  1. Alerting profiles that filter by severity and entity and route to configured integrations Correct answer.
  2. Host group naming conventions
  3. Maintenance windows
  4. Request naming rules

Explanation

Alerting profiles define which problems trigger notifications based on severity and entity scope, and they route those notifications to configured integrations. This allows differentiated response by service criticality.

Question 44Application Observability

A service shows intermittent latency spikes that correlate with garbage collection pauses on its host. Which conclusion is most defensible?

  1. The service's business logic is inherently slow and needs rewriting
  2. The network between tiers is consistently saturated
  3. The database is the sole cause of the spikes
  4. JVM memory pressure is contributing to response time and heap sizing or allocation patterns should be reviewed Correct answer.

Explanation

Latency spikes that align with GC pauses indicate that memory pressure and collection behavior are affecting request timing. Reviewing heap sizing and allocation patterns is the appropriate response rather than rewriting business logic.

Question 45Application Security

In Dynatrace Application Security, which capability is responsible for identifying vulnerable open-source libraries and their transitive dependencies at runtime?

  1. Runtime Vulnerability Analytics
  2. Software Composition Analysis Correct answer.
  3. Code-level vulnerability detection
  4. Security Investigator

Explanation

Software Composition Analysis (SCA) inventories third-party libraries loaded by your processes, including transitive dependencies, and matches them against known vulnerability sources. It is distinct from code-level analytics, which inspects first-party code execution. SCA is the component that answers which open-source packages are present and vulnerable.

Question 46Application Security

A process is running in a Kubernetes pod and Dynatrace Application Security shows no vulnerability findings for it. Which condition most directly explains missing coverage for that workload?

  1. OneAgent is not injected into the container running the process Correct answer.
  2. The cluster has more than one node pool configured
  3. The host has a high number of CPU cores
  4. The environment uses a managed Kubernetes distribution

Explanation

Runtime vulnerability detection depends on OneAgent code modules being present inside the process being monitored. If the container lacks OneAgent injection, the process produces no runtime events and therefore no findings. Environment size or distribution type does not by itself remove coverage.

Question 47Application Security

Which statement describes the role of attack signatures in Dynatrace Application Security?

  1. They define which third-party libraries are permitted to load
  2. They generate SBOM documents for compliance reporting
  3. They detect attempted exploitation of vulnerabilities in incoming requests at runtime Correct answer.
  4. They encrypt sensitive request parameters before storage

Explanation

Attack signatures are evaluated against runtime request data to detect exploitation attempts such as injection payloads. When a signature matches, Dynatrace raises a security event that can be correlated with a vulnerable code location. They do not govern library loading or produce SBOMs.

Question 48Application Security

Which Dynatrace entity type is the primary attachment point for runtime vulnerability findings in Application Security?

  1. The process group instance Correct answer.
  2. The synthetic monitor
  3. The management zone
  4. The dashboard

Explanation

Runtime vulnerability findings are attached to the process group instance where the vulnerable code executed, because detection is based on observed runtime behavior. From that entity you can pivot to the related service and host. Dashboards and management zones are organizational constructs rather than finding owners.

Question 49Application Security

What is the purpose of a security notification workflow in Dynatrace Application Security?

  1. To automatically remediate vulnerable libraries in the source repository
  2. To disable attack detection during maintenance windows
  3. To route alerts about new or changed security problems to the right teams Correct answer.
  4. To replace OneAgent with a static code scanner

Explanation

Security notifications use alerting profiles and integrations to deliver information about new, reopened, or escalated security problems to responsible teams. They do not modify source code or change instrumentation. Routing ensures the correct owners receive actionable security events.

Question 50Application Security

Which data source does Dynatrace Runtime Vulnerability Analytics primarily rely on to detect vulnerabilities in first-party code?

  1. Runtime events captured from instrumented processes Correct answer.
  2. Firewall logs from the network layer
  3. Static analysis of repository commits
  4. Manual penetration test uploads

Explanation

Runtime Vulnerability Analytics evaluates actual code execution observed by OneAgent, so it can confirm that a vulnerable code path was reached. This runtime evidence reduces noise compared with purely static findings. Repository commits and firewall logs are not the primary feed for this capability.