Google Certified Professional - Cloud Developer Pass Cert & Professional-Cloud-Developer Actual Questions & Google Certified Professional - Cloud Developer Training Vce

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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Ensuring application performance and reliability	- Performance optimization - Fault tolerance and incident response - Monitoring and logging (Cloud Monitoring, Cloud Logging)
Deploying applications	- Release management strategies - Containerization and deployment (Cloud Run, GKE) - CI/CD pipelines
Designing cloud-native applications	- Scalability and resiliency patterns - Microservices design principles - Application architecture design
Building and testing applications	- Debugging and troubleshooting applications - Unit and integration testing strategies - Application development on Google Cloud
Integrating Google Cloud services	- API integration and service-to-service communication - Using managed services (Pub/Sub, Firestore, Cloud SQL)

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Google Certified Professional - Cloud Developer Sample Questions (Q116-Q121):

NEW QUESTION # 116
You have a Cloud Run service that connects to a Cloud SQL Enterprise edition database instance using the default Cloud SQL connection. You need to enable more than 100 connections per Cloud Run instance. What should you do?

A. Upgrade to Cloud SQL Enterprise Plus edition.
B. Increase the queries per minute quota limit of the Cloud SQL Admin API.
C. Increase the max_connection flag in Cloud SQL for PostgresSQL.
D. Use Cloud SQL Auth Proxy in a sidecar.

Answer: D

Explanation:
Using the Cloud SQL Auth Proxy in a sidecar allows each Cloud Run instance to multiplex and manage more than 100 concurrent connections to Cloud SQL, overcoming the default connection limit imposed by the built-in Cloud SQL connector.

NEW QUESTION # 117
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 exponential backoff.
B. Optimize database queries to reduce read/write operations, and modify the application code to retry the Firestore API call with fixed backoff.
C. Request a quota increase, 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.

Answer: D

Explanation:
https://cloud.google.com/knowledge/kb/resource-exhausted-errors-found-while-accessing- firestore-or-datastore-via-api-000004804

NEW QUESTION # 118
You have an application running in App Engine. Your application is instrumented with Stackdriver Trace. The
/product-details request reports details about four known unique products at /sku-details as shown below. You want to reduce the time it takes for the request to complete. What should you do?

A. Change /product-details to perform the requests in parallel.
B. Change the Persistent Disk type to SSD.
C. Increase the size of the instance class.
D. Store the /sku-details information in a database, and replace the webservice call with a database query.

Answer: A

NEW QUESTION # 119
You are developing an online chat application where users can upload profile pictures. Uploaded profile pictures must comply with content policies. You need to detect inappropriate images and label those images automatically when they are uploaded. In the future, this process will need to be expanded to include additional processing tasks such as watermarking and image compression.
You want to simplify orchestration and minimize operational overhead of the image scanning and labeling steps while also ensuring that additional steps can be added and removed easily later on. What should you do?

A. Save user-uploaded images to a temporary Cloud Storage bucket. Implement code on the backend server to retrieve the image content and call the Vision API to process each new uploaded image.
B. Save user-uploaded images to a Cloud Storage bucket. Create an Eventarc trigger that connects the bucket to the Workflows event receiver when a new image is uploaded. Create a workflow in Workflows with multiple Cloud Run functions that call the Vision API to process each new uploaded image.
C. Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and publishes a message to a Pub/Sub topic. Deploy microservices in GKE that subscribe to the Pub/Sub topic and call the Vision API to process each new uploaded image.
D. Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and calls one or more Cloud Run services. Create additional Cloud Run services that call the Vision API to process each new uploaded image.

Answer: B

Explanation:
Using Eventarc to trigger Workflows when a new image is uploaded provides a scalable and low- overhead solution for orchestrating multiple steps in the image processing pipeline. Workflows allows you to define a sequence of tasks (e.g., content moderation, watermarking, compression) that can be easily modified or extended as requirements evolve. Each task can be managed by Cloud Functions within the workflow, making it straightforward to add or remove steps in the future.
This approach minimizes operational overhead by using a serverless workflow for orchestration rather than managing infrastructure directly. The other options either involve more complex orchestration setups or lack the flexibility to easily expand to additional processing tasks as needed.

NEW QUESTION # 120
Your operations team has asked you to create a script that lists the Cloud Bigtable, Memorystore, and Cloud SQL databases running within a project. The script should allow users to submit a filter expression to limit the results presented. How should you retrieve the data?

A. Use the HBase API, Redis API, and MySQL connection to retrieve database lists. Combine the results, and then apply the filter to display the results
B. Run gcloud bigtable instances list, gcloud redis instances list, and gcloud sql databases list. Use --filter flag with each command, and then display the results
C. Use the HBase API, Redis API, and MySQL connection to retrieve database lists. Filter the results individually, and then combine them to display the results
D. Run gcloud bigtable instances list, gcloud redis instances list, and gcloud sql databases list. Use a filter within the application, and then display the results

Answer: B

Explanation:
Explanation
https://cloud.google.com/sdk/gcloud/reference/topic/filters
Most gcloud commands return a list of resources on success. By default they are pretty-printed on the standard output. The --format=NAME[ATTRIBUTES](PROJECTION) and --filter=EXPRESSION flags along with projections can be used to format and change the default output to a more meaningful result. Use the --format flag to change the default output format of a command. For details run $ gcloud topic formats.

NEW QUESTION # 121
......

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