Your code is running on Cloud Functions in projectA . It is supposed to write an object in a Cloud Storage bucket owned by project B.
However, the write call is failing with the error "403 Forbidden".
What should you do to correct the problem?

• A.Grant your user account the roles/iam.serviceAccountUser role for the service-PROJECTA@gcf-adminrobot.
  iam.gserviceaccount.com service account.
• B.Grant your user account the roles/storage.objectCreator role for the Cloud Storage bucket.
• C.Enable the Cloud Storage API in project B.
• D.Grant the [email protected] service account the roles/ storage.objectCreator role for the Cloud Storage bucket.

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Your company uses Cloud Logging to manage large volumes of log data. You need to build a real-time log analysis architecture that pushes logs to a third-party application for processing.
What should you do?

• A.Create a Cloud Logging log export to Pub/Sub.
• B.Create a Cloud Logging log export to Cloud Storage.
• C.Create a Cloud Logging log export to BigQuery.
• D.Create a Cloud Function to read Cloud Logging log entries and send them to the third-party application.

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Your analytics system executes queries against a BigQuery dataset. The SQL query is executed in batch and passes the contents of a SQL file to the BigQuery CLI. Then it redirects the BigQuery CLI output to another process. However, you are getting a permission error from the BigQuery CLI when the queries are executed.
You want to resolve the issue. What should you do?

• A.Create a view in BigQuery from the SQL query and SELECT*from the view in the CLI.
• B.Grant the service account BigQuery Data Viewer and BigQuery Job User roles.
• C.Grant the service account BigQuery Data Editor and BigQuery Data Viewer roles.
• D.Create a new dataset in BigQuery, and copy the source table to the new dataset Query the new dataset and table from the CLI.

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You recently developed an application that will be hosted on Cloud Run. You need to conduct a load test. You want to analyze the load test logs second by second to understand your Cloud Run service's response to rapid traffic spikes. You want to minimize effort. How should you analyze the logs?

• A.Use estimation to extrapolate performance from summary monitoring charts.
• B.Analyze the log data in BigQuery by configuring a BigQuery log sink with the appropriate inclusion filter for your application.
• C.Use Cloud Monitoring's default log console for analysis.
• D.Analyze the log data in Cloud SQL for PostgreSQL by pushing logs to a Pub/Sub topic. Use Dataflow to process and ingest the logs.

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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.Configure GKE workload metrics using kubectl. 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.
• 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.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.
• D.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.

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