Cloud Native Observability GCSA Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Which platform is specifically designed for distributed tracing and visualizing request flows across services?

   • AElasticsearch
   • BJaeger
   • CPrometheus
   • DGrafana
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2. 7expert · hard

   A platform team is responsible for a fleet of microservices written in different languages. They want to implement distributed tracing across all services, but they have limited engineering resources and cannot modify every service immediately. They also need to ensure that trace context is propagated across service boundaries that use both HTTP and a message queue. Which strategy best balances effort and completeness?

   • AManually add trace context headers to every HTTP request and message in each service.
   • BDeploy a service mesh and rely on it to propagate trace context for all traffic.
   • CUse a single centralized logging system and correlate logs by request ID without implementing distributed tracing.
   • DUse OpenTelemetry instrumentation libraries and agents for each language, and configure context propagation for both HTTP and messaging systems.
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4. 8foundation · easy

   What is a key best practice for structured logging in cloud native applications?

   • AUse human-readable messages only to keep logs simple.
   • BInclude key-value pairs in a consistent format to enable automated parsing and querying.
   • CLog all data, including sensitive information, to ensure completeness.
   • DWrite logs to the standard output only for the application, not for infrastructure.
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5. 9expert · hard

   A team is designing an observability strategy for a serverless application. They need to monitor function invocations, including duration, errors, and cold starts. They also want to trace requests across multiple functions. Which combination of telemetry types is most appropriate?

   • AUse only metrics for invocation count and duration, and rely on the cloud provider's built-in monitoring for everything else.
   • BUse metrics for invocation count, duration, and errors; use traces to follow requests across functions; use structured logs for detailed error messages.
   • CUse only logs and parse them to extract invocation duration and errors.
   • DUse only traces to capture every invocation and its duration, and derive all other telemetry from traces.
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6. 10application · medium

   A retail platform runs a microservices-based checkout service on Kubernetes. Users report intermittent checkout failures. The team has metrics showing elevated error rates and traces showing spans with high latency, but the logs are unstructured and lack a common request identifier. Which approach best enables the team to pinpoint the root cause of the failures?

   • AAdd a correlation ID to all logs and propagate it through trace spans, then use the trace ID to link logs to the failing spans.
   • BEnable sampling on the tracing backend to reduce the volume of trace data collected.
   • CIncrease the metric collection frequency to capture more data points around the failure window.
   • DSwitch all services to use a single centralized log aggregator to simplify log searching.
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