Datadog APM and Distributed Tracing Fundamentals 온라인 연습
최종 업데이트 시간: 2025년11월17일
당신은 온라인 연습 문제를 통해 Datadog Datadog APM and Distributed Tracing Fundamentals 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 Datadog APM and Distributed Tracing Fundamentals 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 70개의 시험 문제와 답을 포함하십시오.
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Explanation:
Deployment tracking supports all the mentioned deployment strategies.
For more information, see: https://docs.datadoghq.com/tracing/services/deployment_tracking/#deployment-strategies
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Explanation:
Shadow deployment is when a release candidate version is deployed alongside the production version. Incoming traffic is sent to both. Shadow deploys test a potential release against real production traffic. Tagging shadows with a version tag lets you compare error rates, traces, and service behavior between the two versions to determine if the shadow version should be released.
See: https://docs.datadoghq.com/tracing/services/deployment_tracking/#shadow-deploys
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Explanation:
p100 latency or the 100th percentile latency is the largest value in the ordered list of latencies observed.
More information on Service Catalog here: https://docs.datadoghq.com/tracing/service_catalog/#performance-view
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Explanation:
Slowness of an endpoint would correspond to latency. More information here: https://docs.datadoghq.com/tracing/services/service_page/#resources
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Explanation:
p94 means that assuming 100 spans, this span would be the 94th largest. More information on Trace View here: https://docs.datadoghq.com/tracing/trace_explorer/trace_view/?tab=spantags
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Explanation:
Error Tracking enables you to set alerts to remain informed of fatal issues that may occur, group similar errors into issues to easily identify important errors and reduce noise, and follow issues over time to know when they first started, if they are still ongoing, and how often they are occurring.
See: https://docs.datadoghq.com/tracing/error_tracking/
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Explanation:
Error Tracking enables you to group errors into issues, and follow issues over time to know when they first started, if they are still ongoing, and how often they are occurring.
See: https://docs.datadoghq.com/tracing/error_tracking/
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Explanation:
The error rate will tell you which part of the trace encounters a lot of errors.
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Explanation:
Trace retention corresponds to indexed spans. The default retention for indexed spans depends on the plan agreed upon in your Datadog contract.
See: https://docs.datadoghq.com/developers/guide/data-collection-resolution-retention/
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Explanation:
Watchdog AI has features to automatically detect unanticipated outliers and resolve incidents faster with instant root cause analyses.
See: https://www.datadoghq.com/dg/apm/benefits-os-b/
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Explanation:
The tracer debug logs contain information for diagnosing tracer issues or audit trace data. It does not include information about the load on the Datadog Agent nor the application being monitored. The best answer is whether or not traces were sent to the Datadog Agent.
See: https://docs.datadoghq.com/tracing/troubleshooting/tracer_debug_logs/
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Explanation:
The tracer debug logs would be with the application, not the Agent. These logs are not sent to the Datadog Agent in the flare and are stored in a separate path depending on your logging configuration.
See: https://docs.datadoghq.com/tracing/troubleshooting/tracer_debug_logs/
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Explanation:
Services are the building blocks of modern microservice architectures - broadly a service groups together endpoints, queries, or jobs for the purposes of building your application.
See: https://docs.datadoghq.com/tracing/glossary/#services