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Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Developer Sample Questions (Q201-Q206):

NEW QUESTION # 201
Your application is built as a custom machine image. You have multiple unique deployments of the machine image. Each deployment is a separate managed instance group with its own template. Each deployment requires a unique set of configuration values. You want to provide these unique values to each deployment but use the same custom machine image in all deployments. You want to use out-of-the-box features of Compute Engine. What should you do?

A. Place the unique configuration values in a Cloud Bigtable table.
B. Place the unique configuration values in the instance template instance metadata.
C. Place the unique configuration values in the instance template startup script.
D. Place the unique configuration values in the persistent disk.

Answer: D

NEW QUESTION # 202
You have an application running on a GKE cluster. Your application has a stateless web frontend, and has a high-availability requirement. Your cluster is set to automatically upgrade, and some of your nodes need to be drained. You need to ensure that the application has a serving capacity of
10% of the Pods prior to the drain. What should you do?

A. Configure the Horizontal Pod Autoscaler (HPA) maxReplicas value to 10% more than the current replica count.
B. Configure a Vertical Pod Autoscaler (VPA) to increase the memory and CPU by 10% and set the updateMode to Auto.
C. Configure a Pod Disruption Budget (PDB) value to have a minAvailable value of 10%.
D. Configure the Pod replica count to be 10% more than the current replica count.

Answer: D

Explanation:
In this scenario, you need to ensure that there is an additional 10% capacity of Pods available to handle traffic when nodes are drained. By increasing the replica count by 10%, you provide a buffer to maintain high availability during node drains. This approach meets the high-availability requirement by ensuring sufficient Pods remain available during upgrades or disruptions.
Options like Pod Disruption Budget (PDB) with a minAvailable value of 10% do not directly increase the Pod count but rather set a threshold for the minimum number of available Pods, which could result in reduced serving capacity during node drains if the replica count itself is not increased.

NEW QUESTION # 203
Your App Engine standard configuration is as follows:
service: production
instance_class: B1
You want to limit the application to 5 instances. Which code snippet should you include in your configuration?
manual_scaling:

A. max_instances: 5
idle_timeout: 10m
B. instances: 5
min_pending_latency: 30ms
basic_scaling:
C. max_instances: 5
idle_timeout: 10m
basic_scaling:
D. instances: 5
min_pending_latency: 30ms
manual_scaling:

Answer: B

NEW QUESTION # 204
You are deploying a microservices application to Google Kubernetes Engine (GKE) that will broadcast livestreams. You expect unpredictable traffic patterns and large variations in the number of concurrent users. Your application must meet the following requirements:
- Scales automatically during popular events and maintains high
availability
- Is resilient in the event of hardware failures
How should you configure the deployment parameters? (Choose two.)

A. Create alerting policies in Cloud Monitoring based on GKE CPU and memory utilization. Ask an on-duty engineer to scale the workload by executing a script when CPU and memory usage exceed predefined thresholds.
B. Distribute your workload evenly using multiple zonal node pools.
C. Create a managed instance group for Compute Engine with the cluster nodes. Configure autoscaling rules for the managed instance group.
D. Use cluster autoscaler to resize the number of nodes in the node pool, and use a Horizontal Pod Autoscaler to scale the workload.
E. Distribute your workload evenly using a multi-zonal node pool.

Answer: D,E

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/planning-scalability#choosing_multi- zonal_or_single-zone_node_pools

NEW QUESTION # 205
One of your deployed applications in Google Kubernetes Engine (GKE) is having intermittent performance issues. Your team uses a third-party logging solution. You want to install this solution on each node in your GKE cluster so you can view the logs. What should you do?

A. Use SSH to connect to the GKE node, and install the software manually
B. Modify your container image to include the monitoring software
C. Deploy the third-party solution as a DaemonSet
D. Deploy the third-party solution using Terraform and deploy the logging Pod as a Kubernetes Deployment

Answer: C

Explanation:
Explanation
https://cloud.google.com/kubernetes-engine/docs/concepts/daemonset#usage_patterns DaemonSets are useful for deploying ongoing background tasks that you need to run on all or certain nodes, and which do not require user intervention. Examples of such tasks include storage daemons like ceph, log collection daemons like fluent-bit, and node monitoring daemons like collectd.

NEW QUESTION # 206
......

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