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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Ensuring solution and operations reliability	- Assisting with support 1. Incident response and troubleshooting 2. Capacity planning and scaling - Monitoring/logging/profiling/alerting solution 1. Error reporting and alerting 2. Cloud Monitoring and Cloud Logging 3. Performance profiling and tracing - Deployment and release management 1. Deployment strategies and rollback 2. Release management and versioning - Evaluating quality control measures
Designing and planning a cloud solution architecture	- Enabling future cloud technology 1. Implementing future-proof architectures 2. Designing for scalability and elasticity - Creating a migration plan 1. Refactoring and rearchitecting applications 2. Identifying workloads for migration 3. Planning data migration - Designing solution infrastructure that meets business requirements 1. Cost optimization 2. Data and storage architecture 3. Integration with existing systems 4. Migration planning 5. Networking and compute resources 6. Business use cases and product strategy - Designing network, storage, and compute resources 1. Network design including load balancing and firewall rules 2. Compute engine selection and configuration 3. Storage options and data lifecycle management 4. GKE cluster and workload design
Analyzing and optimizing technical and business processes	- Analyzing and defining technical processes 1. Software development lifecycle (SDLC) 2. Incident management and troubleshooting 3. CI/CD pipeline design - Analyzing and defining business processes 1. Cost management and optimization 2. Stakeholder communication 3. Business KPIs and metrics
Managing and provisioning a solution infrastructure	- Configuring individual storage systems 1. Datastore and Bigtable 2. Cloud SQL and Spanner 3. Cloud Storage configuration - Configuring compute systems 1. Compute Engine configuration 2. GKE configuration and management 3. Cloud Run and Cloud Functions - Configuring network topologies 1. Private access and VPN configuration 2. Hybrid and multi-cloud connectivity 3. VPC design and peering
Designing for security and compliance	- Designing for security 1. Network security 2. Identity and Access Management (IAM) 3. Application security 4. Data security and encryption - Designing for compliance 1. Audit logging and monitoring 2. Data residency and sovereignty 3. Regulatory and compliance requirements
Managing implementation	- Interacting with Google Cloud programmatically 1. Infrastructure as Code (IaC) 2. APIs and client libraries 3. Cloud SDK and gcloud CLI - Advising development/operation team(s) to ensure successful deployment 1. Deployment strategies 2. Application development best practices 3. Testing and validation

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q74-Q79):

NEW QUESTION # 74
For this question, refer to the Dress4Win case study. Which of the compute services should be migrated as -is and would still be an optimized architecture for performance in the cloud?

A. Jenkins, monitoring, bastion hosts, security scanners services deployed on custom machine types
B. Web applications deployed using App Engine standard environment
C. Hadoop/Spark deployed using Cloud Dataproc Regional in High Availability mode
D. RabbitMQ deployed using an unmanaged instance group

Answer: B

NEW QUESTION # 75
You are working at an institution that processes medical dat
a. You are migrating several workloads onto Google Cloud. Company policies require all workloads to run on physically separated hardware, and workloads from different clients must also be separated You created a sole-tenant node group and added a node for each client. You need to deploy the workloads on these dedicated hosts. What should you do?

A. Add the node group name as a network tag when creating Compute Engine instances in order to host each workload on the correct node group.
B. Use node affinity labels based on the node name when creating Compute Engine instances in order to host each workload on the correct node.
C. Add the node name as a network tag when creating Compute Engine instances in order to host each workload on the correct node.
D. Use node affinity labels based on the node group name when creating Compute Engine instances in order to host each workload on the correct node group

Answer: D

Explanation:
https://cloud.google.com/compute/docs/nodes/provisioning-sole-tenant-vms#provision_a_sole-tenant_vm
https://cloud.google.com/compute/docs/nodes/provisioning-sole-tenant-vms#gcloud_2 When you create a VM, you request sole-tenancy by specifying node affinity or anti-affinity, referencing one or more node affinity labels. You specify custom node affinity labels when you create a node template, and Compute Engine automatically includes some default affinity labels on each node. By specifying affinity when you create a VM, you can schedule VMs together on a specific node or nodes in a node group. By specifying anti-affinity when you create a VM, you can ensure that certain VMs are not scheduled together on the same node or nodes in a node group.

NEW QUESTION # 76
Your team will start developing a new application using microservices architecture on Kubernetes Engine. As part of the development lifecycle, any code change that has been pushed to the remote develop branch on your GitHub repository should be built and tested automatically. When the build and test are successful, the relevant microservice will be deployed automatically in the development environment. You want to ensure that all code deployed in the development environment follows this process. What should you do?

A. Install a post-commit hook on the remote git repository that tests the code and builds the container when code is pushed to the development branch. After a successful commit, have the developer deploy the newly built container image on the development cluster.
B. Have each developer install a pre-commit hook on their workstation that tests the code and builds the container when committing on the development branch. After a successful commit, have the developer deploy the newly built container image on the development cluster.
C. Create a Cloud Build trigger based on the development branch that tests the code, builds the container, and stores it in Container Registry. Create a deployment pipeline that watches for new images and deploys the new image on the development cluster. Ensure only the deployment tool has access to deploy new versions.
D. Create a Cloud Build trigger based on the development branch to build a new container image and store it in Container Registry. Rely on Vulnerability Scanning to ensure the code tests succeed. As the final step of the Cloud Build process, deploy the new container image on the development cluster. Ensure only Cloud Build has access to deploy new versions.

Answer: C

Explanation:
Explanation
https://cloud.google.com/container-registry/docs/overview
Create a Cloud Build trigger based on the development branch that tests the code, builds the container, and stores it in Container Registry. Create a deployment pipeline that watches for new images and deploys the new image on the development cluster. Ensure only the deployment tool has access to deploy new versions.

NEW QUESTION # 77
For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi- Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
B. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
C. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.
D. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a Multi-Regional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google Data Studio for analysis and reporting.

Answer: B

NEW QUESTION # 78
To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines (VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department. Which two steps should you take? Choose 2 answers

A. Use the -auto-delete flag on all persistent disks and terminate the VM.
B. Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM.
C. Apply VM CPU utilization label and include it in the BigQuery billing export.
D. Store all state into local SSD, snapshot the persistent disks, and terminate the VM.
E. Use the --no-auto-delete flag on all persistent disks and stop the VM.
F. Use Google BigQuery billing export and labels to associate cost to groups.

Answer: C,D

Explanation:
C: Billing export to BigQuery enables you to export your daily usage and cost estimates automatically throughout the day to a BigQuery dataset you specify.
Labels applied to resources that generate usage metrics are forwarded to the billing system so that you can break down your billing charges based upon label criteria. For example, the Compute Engine service reports metrics on VM instances. If you deploy a project with 2,000 VMs, each of which is labeled distinctly, then only the first 1,000 label maps seen within the 1 hour window will be preserved.
E: You cannot stop an instance that has a local SSD attached. Instead, you must migrate your critical data off of the local SSD to a persistent disk or to another instance before you delete the instance completely.
You can stop an instance temporarily so you can come back to it at a later time. A stopped instance does not incur charges, but all of the resources that are attached to the instance will still be charged. Alternatively, if you are done using an instance, delete the instance and its resources to stop incurring charges.
References:
https://cloud.google.com/billing/docs/how-to/export-data-bigquery
https://cloud.google.com/compute/docs/instances/stopping-or-deleting-an-instance

NEW QUESTION # 79
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