Examkin

Question

Difficulty: MediumMonitoring, Logging, and Observability Integration

A digital banking organization operates a hybrid event-driven microservices platform across Google Kubernetes Engine (GKE) and Cloud Functions. The cloud operations team must establish an end-to-end observability and telemetry architecture to trace multi-service payment transactions in real time, aggregate critical logs, and protect against loss of high-severity application errors while managing costs. Which TWO architectural decisions should the cloud architect implement to fulfill these requirements? (Select TWO.)

  1. Instrument microservices using OpenTelemetry SDKs to propagate trace context across GKE pods and Cloud Functions, exporting trace spans directly to Cloud Trace.Answer
  2. B
    Configure an aggregate Cloud Logging exclusion filter for query severity>=WARNING across all production projects to aggressively reduce log ingestion volume.
  3. Establish folder-level Log Router sinks with explicit inclusion filters to continuously stream high-priority application telemetry and error logs to Pub/Sub and BigQuery.Answer
  4. D
    Grant the primitive Owner role to the central telemetry collector service account to prevent permission errors when accessing telemetry across project boundaries.
  5. E
    Rely solely on standard Cloud Storage IAM object permissions to secure exported log storage buckets against data exfiltration across organizational boundaries.

Answer

The optimal solution combines OpenTelemetry context propagation to Cloud Trace for distributed transaction tracing across GKE and Cloud Functions, along with folder-level Log Router sinks using explicit inclusion filters to route critical log entries to BigQuery and Pub/Sub.
Implementing OpenTelemetry SDKs ensures trace context is seamlessly passed between GKE microservices and Cloud Functions, enabling Cloud Trace to map end-to-end request latency. Combining this with folder-level Log Router sinks using explicit inclusion filters allows the organization to reliably capture high-severity operational logs into BigQuery and Pub/Sub for auditing and real-time alerts.

Step-by-Step Solution

1
Analyze trace propagation requirements for multi-service environments
Identify OpenTelemetry as the GCP-recommended open standard for distributed context propagation across GKE and serverless platforms to enable end-to-end latency analysis in Cloud Trace.
Tracing asynchronous calls across GKE microservices and serverless Cloud Functions requires standardized context propagation headers.
2
Design central log aggregation and routing rules
Configure folder-level Log Router sinks using targeted inclusion filters rather than broad exclusion filters.
Inclusion filters guarantee that critical and error severity logs are reliably captured into analytical sinks like BigQuery without accidental dropped logs.

Key Concept

Unified Google Cloud Observability integration through OpenTelemetry distributed tracing and structured Log Router ingestion filtering.
Rate this question

Topics

Monitoring, Logging, and Observability Integration

More questions from this topic