Google Updated Professional-Cloud-Developer Exam Questions and Answers by giovanni

1) Create a managed instance group. Replicate the static content across the virtual machines (VMs)

2) Create an external HTTP(S) load balancer.

3) Enable Cloud CDN, and send traffic to the managed instance group.

1) Create an unmanaged instance group. Replicate the static content across the VMs.

2) Create an external HTTP(S) load balancer

3) Enable Cloud CDN, and send traffic to the unmanaged instance group.

1) Create a Standard storage class, regional Cloud Storage bucket. Put the static content in the bucket

2) Reserve an external IP address, and create an external HTTP(S) load balancer

3) Enable Cloud CDN, and send traffic to your backend bucket

1) Create a Standard storage class, multi-regional Cloud Storage bucket. Put the static content in the bucket.

2) Reserve an external IP address, and create an external HTTP(S) load balancer.

3) Enable Cloud CDN, and send traffic to your backend bucket.

Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.

Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.

Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.

Manually trigger the build for new releases.

Create a build trigger on a Git tag pattern. Use a Git tag convention for new releases.

Create a build trigger on a Git branch name pattern. Use a Git branch naming convention for new releases.

Commit your source code to a second Cloud Source Repositories repository with a second Cloud Build trigger. Use this repository for new releases only.

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

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

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

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.