You maintain a popular mobile game deployed on Google Cloud services that include Firebase, Firestore, and Cloud Run functions. Recently, the game experienced a surge in usage, and the application encountered HTTP 429 RESOURCE_EXHAUSTED errors when accessing the Firestore API. The application has now stabilized. You want to quickly fix this issue because your company has a marketing campaign next week and you expect another surge in usage. What should you do?

• A.Request a quota increase, and modify the application code to retry the Firestore API call with fixed backoff.
• B.Request a quota increase, and modify the application code to retry the Firestore API call with exponential backoff.
• C.Optimize database queries to reduce read/write operations, and modify the application code to retry the Firestore API call with fixed backoff.
• D.Optimize database queries to reduce read/write operations, and modify the application code to retry the Firestore API call with exponential backoff.

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You deployed a new application to Google Kubernetes Engine and are experiencing some performance degradation. Your logs are being written to Cloud Logging, and you are using a Prometheus sidecar model for capturing metrics. You need to correlate the metrics and data from the logs to troubleshoot the performance issue and send real-time alerts while minimizing costs.
What should you do?

• A.Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.
• B.Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.
• C.Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.
• D.Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics.Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.

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You are developing an online gaming platform as a microservices application on Google Kubernetes Engine (GKE). Users on social media are complaining about long loading times for certain URL requests to the application. You need to investigate performance bottlenecks in the application and identify. which HTTP requests have a significantly high latency span in user requests What should you do?

• A.Install tcpdump on your GKE nodes. Run tcpdump to capture network traffic over an extended period of time to collect data Analyze the data files using Wireshark to determine the cause of high latency
• B.Update your microservices to log HTTP request methods and URL paths to STDOUT Use the logs router to send container logs to Cloud Logging Create filters in Cloud Logging to evaluate the latency of user requests across different methods and URL paths.
• C.Configure GKE workload metrics using kubect1 Select all Pods to send their metrics to Cloud Monitoring. Create a custom dashboard of application metrics in Cloud Monitoring to determine performance bottlenecks of your GKE cluster
• D.Instrument your microservices by installing the OpenTelemetry tracing package Update your application code to send traces to Trace for inspection and analysis Create an analysis report on Trace to analyze user requests

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You need to containerize a web application that will be hosted on Google Cloud behind a global load balancer with SSL certificates. You don't have the time to develop authentication at the application level, and you want to offload SSL encryption and management from your application.
You want to configure the architecture using managed services where possible. What should you do?

• A.Host the application on Google Kubernetes Engine, and deploy an NGINX Ingress Controller to handle authentication.
• B.Host the application on Google Kubernetes Engine, and deploy cert-manager to manage SSL certificates.
• C.Host the application on Compute Engine, and configure Cloud Endpoints for your application.
• D.Host the application on Google Kubernetes Engine, and use Identity-Aware Proxy (IAP) with Cloud Load Balancing and Google-managed certificates.

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Your data is stored in Cloud Storage buckets. Fellow developers have reported that data downloaded from Cloud Storage is resulting in slow API performance. You want to research the issue to provide details to the GCP support team.
Which command should you run?

• A.gsutil test -o output.json gs://my-bucket
• B.gsutil perfdiag -o output.json gs://my-bucket
• C.gcloud compute scp example-instance:~/test-data -o output.json gs://my-bucket
• D.gcloud services test -o output.json gs://my-bucket

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