Your company has a Google Cloud project that uses BigQuery for data warehousing. They have a VPN tunnel between the on-premises environment and Google Cloud that is configured with Cloud VPN. The security team wants to avoid data exfiltration by malicious insiders, compromised code, and accidental oversharing. What should they do?
Answer(s): C
The correct answer is C. Configure VPC Service Controls and configure Private Google Access. Here's why:VPC Service Controls (VPC SC) is a powerful security feature that establishes a security perimeter around Google Cloud resources, including BigQuery. It prevents data exfiltration by restricting access to services based on network and identity. By configuring VPC SC, you can limit how data can be accessed, including from authorized Google Cloud projects, limiting access even if IAM permissions were compromised. This protects against data loss via malicious insider attacks or oversharing. https://cloud.google.com/vpc-service-controls/docs/overviewPrivate Google Access allows on-premises systems to access Google Cloud APIs through private IP addresses, bypassing the public internet. Combined with VPC SC, this ensures that access to BigQuery is restricted to the intended private network. It prevents data access from untrusted networks. https://cloud.google.com/vpc/docs/private-access-optionsOption A, configuring Private Google Access alone, only secures the path to Google Cloud services, not the data itself. It doesn’t prevent a compromised account within the private network from exporting data.Option B, while employing a service account with restricted permissions, does not mitigate the risk of data exfiltration. A compromised service account, regardless of permissions granted, could still move data from BigQuery if not constrained by a perimeter. Removing all other IAM access from the project could disrupt legitimate business operations.Option D is similar to option A and is also insufficient by itself. Private Google Access only provides private network connectivity; it does not establish a perimeter around data.Therefore, option C provides the most comprehensive solution by restricting data access and egress to only intended and permitted users and networks, which is the most secure approach. This combination protects against accidental and malicious data leaks.
You are working at an institution that processes medical data. 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?
Answer(s): D
The correct answer is D because it leverages the power of node affinity within Google Compute Engine to ensure workloads are deployed on specific physical hosts (nodes) within a sole-tenant node group. Sole-tenant nodes are designed to provide physical separation for security and compliance. Options A and B, which propose using network tags, are incorrect because network tags are for identifying instances within a network and don't affect host placement. Option C, while utilizing labels, incorrectly suggests using the node group name. The goal is to control placement on specific nodes, not node groups. Sole-tenant nodes are organized within node groups, and each client's workload must be on a specific node. Node affinity labels allow you to define rules that dictate where an instance should run based on the metadata of the node. By labeling each node with a unique identifier (e.g., corresponding to a client or workload type) and then using those same labels in node affinity rules during instance creation, you can ensure instances are placed on their desired dedicated hosts. This approach adheres to the policy of physical separation, and using node names rather than node group names guarantees that workloads from different clients are isolated on distinct physical hardware. It's a fine-grained control mechanism ensuring full control of instance placement on dedicated hardware, therefore, it satisfies the requirements of the scenario.For further research, consult the following Google Cloud documentation:Sole-tenancy: https://cloud.google.com/compute/docs/nodes/sole-tenant-nodes Node Affinity: https://cloud.google.com/compute/docs/instances/configuring-min-cpu-platform#node_affinity Creating Instances with Sole Tenancy: https://cloud.google.com/compute/docs/instances/create-sole-tenant-instances
Your company's test suite is a custom C++ application that runs tests throughout each day on Linux virtual machines. The full test suite takes several hours to complete, running on a limited number of on-premises servers reserved for testing. Your company wants to move the testing infrastructure to the cloud, to reduce the amount of time it takes to fully test a change to the system, while changing the tests as little as possible. Which cloud infrastructure should you recommend?
Answer(s): B
The optimal solution is B. Google Compute Engine managed instance groups with auto-scaling. Here's why:Managed instance groups (MIGs) are ideal for this scenario because they provide a way to automatically manage and scale a collection of identical virtual machine instances. This directly addresses the company's need to reduce testing time by adding more resources on demand. Auto-scaling, a key feature of MIGs, dynamically adjusts the number of VM instances based on metrics like CPU utilization or custom metrics related to the test suite workload. This means the infrastructure automatically scales up during peak testing hours and scales down when demand is low, optimizing costs. MIGs also offer high availability by automatically restarting VMs that fail or become unresponsive, ensuring the test suite runs reliably.Option A, while offering load balancing, lacks the automatic scaling feature crucial for addressing the need to speed up testing. Option C uses Dataproc for data processing, which is not suitable for running a C++ test suite. Option D uses App Engine, a platform as a service that is not designed for directly running custom executables, specifically a C++ application that requires direct access to the VM environment. Therefore, managed instance groups with auto-scaling (Option B) offer the best balance of flexibility, performance, and cost-efficiency for migrating the C++ test suite.Further research:Google Cloud Managed Instance Groups: https://cloud.google.com/compute/docs/instance-groups/ Google Cloud Autoscaling: https://cloud.google.com/compute/docs/autoscaling/
A lead software engineer tells you that his new application design uses websockets and HTTP sessions that are not distributed across the web servers. You want to help him ensure his application will run properly on Google Cloud Platform. What should you do?
The core issue is the application's reliance on non-distributed websockets and HTTP sessions, which creates a single point of failure and hinders scalability in a cloud environment like Google Cloud Platform (GCP). Option C, "Meet with the cloud operations team and the engineer to discuss load balancer options," is the most appropriate first step. Load balancers in GCP, especially those offering session affinity (also called sticky sessions), can route requests from the same client to the same backend server, effectively maintaining the illusion of a persistent session. While not a true distributed solution, it addresses the immediate problem without requiring a complete application redesign. Options A and D suggest major code changes or architectural shifts, which are more complex and should be considered later if load balancer session affinity proves inadequate. Option B, focusing on encryption requirements, is important but doesn't directly solve the scalability and session management problems.A discussion involving the cloud operations team is vital because they have expertise in configuring and managing GCP's load balancing services, enabling them to advise on the optimal load balancer type, session affinity settings, and other related parameters to suit the application's needs. This collaborative approach allows for a practical solution that can be implemented relatively quickly, and provides the engineer with an understanding of how infrastructure can be used to address application design issues.Supporting Links:Google Cloud Load Balancing Overview: https://cloud.google.com/load-balancing/docs/load-balancing-overview Session Affinity: https://cloud.google.com/load-balancing/docs/session-affinity
The application reliability team at your company this added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most 15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss. Which process should you implement?
The correct answer is D . Here's why:Option D prioritizes minimizing data loss and efficient handling of the event stream. Appending metadata directly to the file body ensures that crucial information about the event is always tied to the event data itself, avoiding potential inconsistencies or loss if metadata is handled separately. Compressing individual files reduces storage costs and potentially speeds up the upload process to Cloud Storage. Using a random prefix pattern for filenames helps distribute writes across the Cloud Storage infrastructure, preventing hot spots and potential performance bottlenecks, crucial with a high write volume of 3000 events per second. Saving all files to a single bucket simplifies management and avoids unnecessary complexity with bucket creation and rotation.Options A, B, and C have drawbacks. Option A’s bucket rotation every hour is excessive and adds unnecessary overhead. Option B’s batching introduces potential latency and complexity, also, relying on a single archive increases the risk of data loss in case of failure. While option C uses compression and metadata, it lacks the crucial element of filename randomization to evenly distribute writes, thus increasing the risk of performance issues. The use of custom metadata headers, suggested in C, would not resolve the issue of performance and could be harder to maintain as metadata. Cloud Storage’s automatic metadata mechanism would make custom headers redundant. In summary, option D is the optimal solution because it is designed for reliability, performance, and simple management.Further research:Google Cloud Storage Performance Guidelines: https://cloud.google.com/storage/docs/performance Cloud Storage Naming Conventions: https://cloud.google.com/storage/docs/naming Google Cloud Storage Metadata: https://cloud.google.com/storage/docs/metadata
Post your Comments and Discuss Google Google Cloud Architect Professional exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the Google Cloud Architect Professional content, but please register or login to continue.