Professional-Cloud-Developer Practice Exam Questions and Answers

A.  

The folder structure inside the bucket and object paths have changed.

B.  

The permissions of the service account’s predefined role have changed.

C.  

The service account key has been rotated but not updated on the application server.

D.  

The Interconnect link from the on-premises data center to Google Cloud is experiencing a temporary outage.

A.  

Write a shutdown script that uses inter-process signals to notify the application process to disconnect from the database.

B.  

Write a shutdown script that broadcasts a message to all signed-in users that the Compute Engine instance is going down and instructs them to save current work and sign out.

C.  

Write a shutdown script that writes a file in a location that is being polled by the application once every five minutes. After the file is read, the application disconnects from the database.

D.  

Write a shutdown script that publishes a message to the Pub/Sub topic announcing that a shutdown is in progress. After the application reads the message, it disconnects from the database.

A.  

Use BigQuery federated queries to query data from Cloud Storage.

B.  

Create a dataset in the EU region that will keep information about EU users only.

C.  

Create a Cloud Storage bucket in the EU region to store information for EU users only.

D.  

Re-upload your data using to a Cloud Dataflow pipeline by filtering your user records out.

E.  

Use DML statements in BigQuery to update/delete user records based on their requests.

A.  

Host the application on Compute Engine, and configure Cloud Endpoints for your application.

B.  

Host the application on Google Kubernetes Engine and use Identity-Aware Proxy (IAP) with Cloud Load Balancing and Google-managed certificates.

C.  

Host the application on Google Kubernetes Engine, and deploy an NGINX Ingress Controller to handle authentication.

D.  

Host the application on Google Kubernetes Engine, and deploy cert-manager to manage SSL certificates.

A.  

Store the credentials in a sidecar container proxy, and use it to connect to the third-party database.

B.  

Configure a service mesh to allow or restrict traffic from the Pods in your microservice to the database.

C.  

Store the credentials in an encrypted volume mount, and associate a Persistent Volume Claim with the client Pod.

D.  

Store the credentials as a Kubernetes Secret, and use the Cloud Key Management Service plugin to handle encryption and decryption.

A.  

Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.

B.  

Create Compute Engine instances with HDD instead of SSD to save costs. Then perform a full migration of your existing environment into the new one in Compute Engine instances.

C.  

Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop environment to the new Cloud Dataproc cluster. Move your HDFS data into larger HDD disks to save on storage costs.

D.  

Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.

A.  

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

B.  

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

C.  

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

D.  

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.

A.  

Create a Cloud Function that consumes the Monitoring API. Create a schedule to trigger the Cloud Function hourly and alert you if the average memory consumption is outside the defined range

B.  

In Cloud Monitoring, create an alerting policy to notify you if the average memory consumption is outside the

defined range

C.  

Create a Cloud Function that runs on a schedule, executes kubect1 top on all the workloads on the cluster, and sends an email alert if the average memory consumption is outside the defined range

D.  

Write a script that pulls the memory consumption of the instance at the OS level and sends an email alert if the average memory consumption is outside the defined range

A.  

Modernize and deploy the code on App Engine flexible environment.

B.  

Modernize and deploy the code on App Engine standard environment.

C.  

Deploy the modernized application to an n1-standard-1 Compute Engine instance.

D.  

Ask the development team to re-write the application to run as a Docker container on Google Kubernetes Engine.

A.  

Terraform and kubeadm

B.  

Docker Compose and dockerd

C.  

Minikube and Skaffold

D.  

kaniko and Tekton