Google Professional-Cloud-Developer Updated CBT, Actual Professional-Cloud-Developer Test
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Our Google Professional-Cloud-Developer practice exam also provides users with a feel for what the real Google Professional-Cloud-Developer exam will be like. Both Google Certified Professional - Cloud Developer (Professional-Cloud-Developer) practice exams are the same as the Actual Professional-Cloud-Developer Test and give candidates the experience of taking the real Google Certified Professional - Cloud Developer (Professional-Cloud-Developer) exam. These Professional-Cloud-Developer practice tests can be customized according to your needs.
To pass the Google Professional-Cloud-Developer Certification Exam, candidates must demonstrate their knowledge of topics such as cloud architecture, application development, deployment, and monitoring. They must also have experience working with common development tools and languages, such as Java, Python, and Node.js. Additionally, candidates should be familiar with GCP services such as Compute Engine, App Engine, Kubernetes Engine, and Cloud Functions.
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Professional-Cloud-Developer Updated CBT | 100% Free Authoritative Actual Google Certified Professional - Cloud Developer Test
The objective of Professional-Cloud-Developer is to assist candidates in preparing for the Google Certified Professional - Cloud Developer (Professional-Cloud-Developer) certification test by equipping them with the actual Google Professional-Cloud-Developer questions PDF and Professional-Cloud-Developer practice exams to attempt the prepare for your Professional-Cloud-Developer Exam successfully. The Google Certified Professional - Cloud Developer (Professional-Cloud-Developer) practice material comes in three formats, desktop Professional-Cloud-Developer practice test software, web-based Professional-Cloud-Developer practice exam, and Professional-Cloud-Developer Dumps PDF that cover all exam topics.
Google Certified Professional - Cloud Developer Sample Questions (Q95-Q100):
NEW QUESTION # 95
Your infrastructure team uses Terraform Cloud and manages Google Cloud resources by using Terraform configuration files. You want to configure an infrastructure as code pipeline that authenticates to Google Cloud APIs. You want to use the most secure approach and minimize changes to the configuration. How should you configure the authentication?
- A. Create a service account that has the required permissions to manage the Google Cloud resources, and import the service account key to Terraform Cloud. Use this service account to authenticate to the Google Cloud APIs.
- B. Use Terraform on GKE. Create a Kubernetes service account to execute the Terraform code. Use workload identity federation to authenticate as the Google service account.
- C. Install Terraform on a Compute Engine VM. Configure the VM by using a service account that has the required permissions to manage the Google Cloud resources.
- D. Configure Terraform Cloud to use Workload Identity Federation to authenticate to the Google Cloud APIs.
Answer: D
Explanation:
Workload identity federation is the most secure and recommended approach for authenticating to Google Cloud APIs without the need to manage long-lived service account keys. By configuring Terraform Cloud to use workload identity federation, you can securely authenticate to Google Cloud APIs without having to export and manage service account keys. This approach minimizes the risk associated with key management and adheres to best practices for secure, short-lived, token-based authentication.
NEW QUESTION # 96
Your team currently uses Bigtable as their database backend. In your application's app profile, you notice that the connection to the Bigtable cluster is specified as single-cluster routing, and the cluster's connection logic is configured to conduct manual failover when the cluster is unavailable.
You want to optimize the application code to have more efficient and highly available Bigtable connectivity. What should you do?
- A. Increase the Bigtable client's connection pool size.
- B. Set up Memcached so that queries hit the cache layer first and automatically get data from Bigtable in the event of a cache miss.
- C. Configure the app profile to use multi-cluster routing.
- D. Configure a Dataflow template, and use a Beam connector to stream data changes.
Answer: C
Explanation:
https://cloud.google.com/bigtable/docs/replication-settings
NEW QUESTION # 97
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. 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. 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. 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. 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.
Answer: A
Explanation:
https://cloud.google.com/trace/docs/setup#when-to-instrument
NEW QUESTION # 98
You want to re-architect a monolithic application so that it follows a microservices model. You want to accomplish this efficiently while minimizing the impact of this change to the business.
Which approach should you take?
- A. Replace the application's features with appropriate microservices in phases.
- B. Deploy the application to Compute Engine and turn on autoscaling.
- C. Refactor the monolithic application with appropriate microservices in a single effort and deploy it.
- D. Build a new application with the appropriate microservices separate from the monolith and replace it when it is complete.
Answer: A
Explanation:
The migration is done feature by feature, avoiding a large-scale migration event and its associated risks.
https://cloud.google.com/architecture/microservices-architecture-refactoring-monoliths
NEW QUESTION # 99
Your teammate has asked you to review the code below. Its purpose is to efficiently add a large number of small rows to a BigQuery table.
Which improvement should you suggest your teammate make?
- A. Perform the inserts in parallel by creating multiple threads.
- B. Write each row to a Cloud Storage object, then load into BigQuery.
- C. Include multiple rows with each request.
- D. Write each row to a Cloud Storage object in parallel, then load into BigQuery.
Answer: A
NEW QUESTION # 100
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