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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing secure applications 1. Implementing authentication and authorization mechanisms 2. Securing data in transit and at rest 3. Applying security best practices and compliance requirements - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting - Designing high-performing applications and APIs 1. Optimizing for latency and throughput 2. Designing for high availability and fault tolerance 3. Selecting appropriate compute and deployment platforms
Topic 2: Integrating applications with Google Cloud services	21%	- Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - Consuming Google Cloud APIs and services 1. Using client libraries, REST, and gRPC 2. Integrating with AI/ML and generative AI services 3. Enabling and managing APIs - Integrating with data and storage services 1. Using object storage and file storage solutions 2. Implementing messaging and event streaming 3. Working with relational, NoSQL, and in-memory databases
Topic 3: Configuring cloud-native applications for deployment	24%	- Deploying to App Engine or Cloud Functions 1. Managing versions and traffic splitting 2. Configuring serverless deployments - Deploying applications to Cloud Run 1. Integrating with event-driven services 2. Configuring scaling, networking, and security 3. Deploying from source code or containers - Deploying applications to Google Kubernetes Engine 1. Configuring scaling, auto-scaling, and updates 2. Creating and managing workloads and services 3. Managing configurations and secrets
Topic 4: Building and testing applications	23%	- Setting up development environments 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations 2. Emulating Google Cloud services locally - Building applications and containers 1. Using Cloud Build and Artifact Registry 2. Implementing containerization best practices 3. Configuring build pipelines and provenance - Testing applications 1. Automating testing in CI/CD workflows 2. Writing unit, integration, and end-to-end tests 3. Using AI-assisted development tools

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Google Certified Professional - Cloud Developer Sample Questions (Q98-Q103):

NEW QUESTION # 98
You need to containerize a web application that will be hosted on Google Cloud behind a global load balancer with SSL certificates. You don't have the time to develop authentication at the application level, and you want to offload SSL encryption and management from your application.
You want to configure the architecture using managed services where possible. What should you do?

A. Host the application on Google Kubernetes Engine, and use Identity-Aware Proxy (IAP) with Cloud Load Balancing and Google-managed certificates.
B. Host the application on Compute Engine, and configure Cloud Endpoints for your application.
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.

Answer: A

Explanation:
IAP provides a way to control access to applications running on GCP without the need for traditional VPNs. It works by verifying a user's identity and determining if that user should be allowed access to the application. This is especially useful since you do not have the time to develop authentication at the application level. IAP can handle this for you.

NEW QUESTION # 99
You have an application deployed in Google Kubernetes Engine (GKE). You need to update the application to make authorized requests to Google Cloud managed services. You want this to be a one-time setup, and you need to follow security best practices of auto-rotating your security keys and storing them in an encrypted store. You already created a service account with appropriate access to the Google Cloud service. What should you do next?

A. Assign the Google Cloud service account to your GKE Pod using Workload Identity.
B. Export the Google Cloud service account, and upload it to HashiCorp Vault to generate a dynamic service account for your application.
C. Export the Google Cloud service account, and share it with the Pod as a Kubernetes Secret.
D. Export the Google Cloud service account, and embed it in the source code of the application.

Answer: A

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity Applications running on GKE might need access to Google Cloud APIs such as Compute Engine API, BigQuery Storage API, or Machine Learning APIs.
Workload Identity allows a Kubernetes service account in your GKE cluster to act as an IAM service account.
Pods that use the configured Kubernetes service account automatically authenticate as the IAM service account when accessing Google Cloud APIs. Using Workload Identity allows you to assign distinct, fine-grained identities and authorization for each application in your cluster.

NEW QUESTION # 100
You have decided to migrate your Compute Engine application to Google Kubernetes Engine. You need to build a container image and push it to Artifact Registry using Cloud Build. What should you do? (Choose two.) A) Run gcloud builds submit in the directory that contains the application source code.
B)
Run gcloud run deploy app-name --image gcr.io/$PROJECT_ID/app-name in the directory that contains the application source code.
C)
Run gcloud container images add-tag gcr.io/$PROJECT_ID/app-name gcr.io/$PROJECT_ID/app-name:latest in the directory that contains the application source code.
D)
In the application source directory, create a file named cloudbuild.yaml that contains the following contents:

E)
In the application source directory, create a file named cloudbuild.yaml that contains the following contents:

A. Option D
B. Option E
C. Option B
D. Option A
E. Option C

Answer: A,D

Explanation:
https://cloud.google.com/sdk/gcloud/reference/builds/submit
https://cloud.google.com/artifact-registry/docs/configure-cloud-build

NEW QUESTION # 101
You are working on a new application that is deployed on Cloud Run and uses Cloud Functions Each time new features are added, new Cloud Functions and Cloud Run services are deployed You use ENV variables to keep track of the services and enable interservice communication but the maintenance of the ENV variables has become difficult. You want to implement dynamic discovery in a scalable way. What should you do?

A. Create a Service Directory Namespace Use API calls to register the services during deployment, and query during runtime.
B. Configure your microservices to use the Cloud Run Admin and Cloud Functions APIs to query for deployed Cloud Run services and Cloud Functions in the Google Cloud project.
C. Rename the Cloud Functions and Cloud Run services endpoints using a well-documented naming convention
D. Deploy Hashicorp Consul on a single Compute Engine Instance Register the services with Consul during deployment and query during runtime

Answer: B

NEW QUESTION # 102
You have an analytics application that runs hundreds of queries on BigQuery every few minutes using BigQuery API. You want to find out how much time these queries take to execute.
What should you do?

A. Use Stackdriver Trace to plot query execution time.
B. Use Stackdriver Monitoring to plot slot usage.
C. Use Stackdriver Monitoring to plot query execution times.
D. Use Stackdriver Trace to plot API execution time.

Answer: C

Explanation:
Use Cloud Monitoring to view BigQuery metrics and create charts and alert.
https://cloud.google.com/bigquery/docs/monitoring

NEW QUESTION # 103
......

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