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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Topic 1: Building and testing applications	- Unit and integration testing strategies - Application development on Google Cloud - Debugging and troubleshooting applications
Topic 2: Integrating Google Cloud services	- API integration and service-to-service communication - Using managed services (Pub/Sub, Firestore, Cloud SQL)
Topic 3: Ensuring application performance and reliability	- Fault tolerance and incident response - Performance optimization - Monitoring and logging (Cloud Monitoring, Cloud Logging)
Topic 4: Designing cloud-native applications	- Microservices design principles - Application architecture design - Scalability and resiliency patterns
Topic 5: Deploying applications	- Release management strategies - Containerization and deployment (Cloud Run, GKE) - CI/CD pipelines

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Google Certified Professional - Cloud Developer Sample Questions (Q305-Q310):

NEW QUESTION # 305
Your application is deployed on hundreds of Compute Engine instances in a managed instance group (MIG) in multiple zones. You need to deploy a new instance template to fix a critical vulnerability immediately but must avoid impact to your service. What setting should be made to the MIG after updating the instance template?

A. Set the Maximum Unavailable to 100%.
B. Set the Max Surge to 100%.
C. Set the Update mode to Opportunistic.
D. Set the Minimum Wait time to 0 seconds.

Answer: C

Explanation:
https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed-instance-groups#type Alternatively, if an automated update is potentially too disruptive, you can choose to perform an opportunistic update. The MIG applies an opportunistic update only when you manually initiate the update on selected instances or when new instances are created. New instances can be created when you or another service, such as an autoscaler, resizes the MIG. Compute Engine does not actively initiate requests to apply opportunistic updates on existing instances.

NEW QUESTION # 306
For this question, refer to the HipLocal case study.
How should HipLocal redesign their architecture to ensure that the application scales to support a large increase in users?

A. Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.
B. Use multiple Compute Engine instances to run MySQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.
C. Use a Cloud Storage bucket to serve the application as a static website, and use another Cloud Storage bucket to store user state information.
D. Use Google Kubernetes Engine (GKE) to run the application as a microservice. Run the MySQL database on a dedicated GKE node.

Answer: C

NEW QUESTION # 307
You are monitoring a web application that is written in Go and deployed in Google Kubernetes Engine. You notice an increase in CPU and memory utilization. You need to determine which function is consuming the most CPU and memory resources. What should you do?

A. Add print commands to the application source code to log when each function is called, and redeploy the application.
B. Import the Cloud Profiler package into your application, and initialize the Profiler agent. Review the generated flame graph in the Google Cloud console to identify time-intensive functions.
C. Import OpenTelemetry and Trace export packages into your application, and create the trace provider. Review the latency data for your application on the Trace overview page, and identify which functions cause the most latency.
D. Create a Cloud Logging query that gathers the web application s logs. Write a Python script that calculates the difference between the timestamps from the beginning and the end of the application's longest functions to identify time-intensive functions.

Answer: B

Explanation:
Google Cloud Profiler efficiently analyzes cloud-based applications, like those on GKE, using minimal code changes and flame graphs for in-depth insights into CPU and memory usage, thus identifying performance bottlenecks with ease.

NEW QUESTION # 308
Your website is deployed on Compute Engine. Your marketing team wants to test conversion rates between 3 different website designs.
Which approach should you use?

A. Deploy the website on Cloud Functions and use traffic splitting.
B. Deploy the website on Cloud Functions as three separate functions.
C. Deploy the website on App Engine as three separate services.
D. Deploy the website on App Engine and use traffic splitting.

Answer: D

Explanation:
Reference: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic

NEW QUESTION # 309
You want to create "fully baked" or "golden" Compute Engine images for your application. You need to bootstrap your application to connect to the appropriate database according to the environment the application is running on (test, staging, production). What should you do?

A. When creating the Compute Engine instance, add a tag with the name of the database to be connected.
In your application, query the Compute Engine API to pull the tags for the current instance, and use the tag to construct the appropriate database connection string.
B. When creating the Compute Engine instance, create a metadata item with a key of "DATABASE" and a value for the appropriate database connection string. In your application, read the "DATABASE" environment variable, and use the value to connect to the appropriate database.
C. When creating the Compute Engine instance, create a metadata item with a key of "DATABASE" and a value for the appropriate database connection string. In your application, query the metadata server for the "DATABASE" value, and use the value to connect to the appropriate database.
D. Embed the appropriate database connection string in the image. Create a different image for each environment.

Answer: C

NEW QUESTION # 310
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