Professional-Cloud-Network-Engineer Google Cloud Certified - Professional Cloud Network Engineer Free Practice Exam Questions (2025 Updated)

If the VPN logs show a no-proposal-chosen error, this error indicates that Cloud VPN and your peer VPN gateway were unable to agree on a set of ciphers. For IKEv1, the set of ciphers must match exactly. For IKEv2, there must be at least one common cipher proposed by each gateway. Make sure that you use supported ciphers to configure your peer VPN gateway. https://cloud.google.com/network- connectivity/docs/vpn/support/troubleshooting#:~:text=If%20the%20VPN%20logs%20show,of%20ciphers%20must%20match%20exactly.&text=Make%20sure%20that%20you%20use,configure%20your%20peer%20VPN%20gateway.

To configure Cloud CDN for an application hosted outside of Google Cloud (e.g., in AWS), you need to use an internet network endpoint group (NEG). An internet NEG allows you to point to external endpoints using their FQDN or IP address. Cloud CDN works with external HTTP(S) Load Balancers, and you enable CDN on the backend service associated with the load balancer. A Network Load Balancer (passthrough) does not support Cloud CDN.

Exact Extract:

"To enable Cloud CDN for content hosted outside of Google Cloud, you must use an external HTTP(S) Load Balancer with an internet network endpoint group (NEG)."

"An internet NEG specifies one or more external endpoints that can be reached by an external HTTP(S) Load Balancer. You can specify endpoints using an IP address and port, or a fully qualified domain name (FQDN) and port."

"Cloud CDN is enabled on the backend service of an external HTTP(S) Load Balancer."Reference: Google Cloud CDN Documentation - Caching external content, Internet NEGs overview

Comprehensive and Detailed In Depth Explanation:

Let's analyze each option to find the one that meets the requirements of no startup time for new traffic, two idle instances, and minimal administrative overhead:

A. Unchecking "Serve this revision immediately" and using a traffic simulation tool: Unchecking "Serve this revision immediately" does prevent the new revision from receiving traffic immediately. However, manually using a traffic simulation tool adds administrative overhead. It also doesn't guarantee that two idle instances will be ready before traffic is shifted; you would need to monitor and adjust traffic manually based on the simulation.

B. Configuring service autoscaling and setting the minimum number of instances to 2: Service-level autoscaling applies to all revisions of the service. Setting the minimum instances at the service level would ensure at least two instances are running across all active revisions, not specifically for the new revision before traffic shift.

C. Configuring revision autoscaling for the new revision and setting the minimum number of instances to 2: This is the correct approach. By configuring revision autoscaling specifically for the new revision and setting the minimum number of instances to 2, Cloud Run will ensure that at least two instances of the new version are running and ready to serve traffic before you redirect any traffic to it. This eliminates startup latency when you do shift traffic. It also minimizes administrative overhead as Cloud Run manages the instance scaling based on this configuration.

D. Configuring revision autoscaling for the existing revision and setting the minimum number of instances to 2: This would ensure the existing version has at least two idle instances, which doesn't directly address the requirement of having idle instances ready for the new version before traffic redirection.

Google Cloud Documentation References:

Cloud Run Autoscaling: https://cloud.google.com/run/docs/configuring/min-instances - This document explains how to configure minimum and maximum instances for Cloud Run services and revisions. It clarifies that you can set minimum instances at the revision level to ensure instances are always ready.

Cloud Run Traffic Management: https://cloud.google.com/run/docs/managing/traffic - This describes how to deploy new revisions and gradually shift traffic between them. Combining minimum instances on the new revision with traffic splitting allows for zero-downtime deployments with pre-warmed instances.

===========

The correct answer is D. Create privately used public IP primary and secondary subnet ranges for the clusters. Create a private GKE cluster with the following options selected: --disable-default-snat, --enable-ip-alias, and --enable-private-nodes.

This answer is based on the following facts:

Privately used public IP (PUPI) addresses are any public IP addresses not owned by Google that a customer can use privately on Google Cloud1. You can use PUPI addresses for GKE pods and services in private clusters to mitigate address exhaustion.

A private GKE cluster is a cluster that has no public IP addresses on the nodes2. You can use private clusters to isolate your workloads from the public internet and enhance security.

The --disable-default-snat option disables source network address translation (SNAT) for the cluster3. This option allows you to use PUPI addresses without conflicting with other public IP addresses on the internet.

The --enable-ip-alias option enables alias IP ranges for the cluster4. This option allows you to use separate subnet ranges for nodes, pods, and services, and to specify the size of those ranges.

The --enable-private-nodes option enables private nodes for the cluster5. This option ensures that the nodes have no public IP addresses and can only communicate with other Google Cloud resources in the same VPC network or peered networks.

The other options are not correct because:

Option A is not suitable. Creating RFC 1918 primary and secondary subnet IP ranges for the clusters does not solve the problem of address exhaustion. Re-using the secondary address range for pods across multiple private GKE clusters can cause IP conflicts and routing issues.

Option B is also not suitable. Creating RFC 1918 primary and secondary subnet IP ranges for the clusters does not solve the problem of address exhaustion. Re-using the secondary address range for services across multiple private GKE clusters can cause IP conflicts and routing issues.

Option C is not feasible. Creating privately used public IP primary and secondary subnet ranges for the clusters is a valid step, but creating a private GKE cluster with only --enable-ip-alias and --enable-private-nodes options is not enough. You also need to disable default SNAT to avoid IP conflicts with other public IP addresses on the internet.

For GKE pods that need to egress directly to the internet for most of their communications, the most cost-efficient and straightforward approach is to deploy a GKE cluster with public cluster nodes. Public nodes have external IP addresses, allowing pods to directly reach the internet. This eliminates the need for additional services like Cloud NAT or Secure Web Proxy for outbound internet access, which would incur extra costs and management overhead.

Exact Extract:

"Public clusters have nodes with external IP addresses, allowing them to directly initiate connections to the internet. This is the simplest configuration for clusters that require direct internet egress for their workloads."

"When using public clusters, Cloud NAT is not required for outbound internet connectivity from the nodes or pods, as they can use their external IP addresses. This can reduce operational overhead and cost compared to private clusters that need NAT."Reference: Google Kubernetes Engine Documentation - Cluster network configuration, Public clusters vs Private clusters

Comprehensive and Detailed In Depth Explanation:

Let's analyze each option:

A. Using gsutil: While gsutil can transfer data to Cloud Storage, for 150 TB of infrequently accessed data, direct transfer over the network might be slow and consume significant bandwidth, potentially impacting other network operations. It also lacks built-in mechanisms for filtering files based on age.

B. Using Cloud Interconnect and Filestore: Cloud Interconnect provides a dedicated connection, but Filestore is a fully managed NFS service primarily designed for high-performance file sharing for applications running in Google Cloud. Migrating 150 TB of infrequently accessed data to Filestore would be cost-inefficient compared to Cloud Storage and doesn't directly address the requirement of moving older than one year files.

C. Using Transfer Appliance: Transfer Appliance is suitable for very large datasets (petabytes) or when network connectivity is poor or unreliable. While it addresses bandwidth concerns, it involves a physical appliance and might be an overkill for 150 TB of data, especially if network connectivity is reasonable.

D. Using Storage Transfer Service: Storage Transfer Service is specifically designed for moving large amounts of data between online storage systems, including on-premises file systems and Cloud Storage. It offers features like filtering by file age, scheduling transfers, and bandwidth control, directly addressing all the requirements of the question: migrating infrequently accessed files older than one year to Cloud Storage, minimizing costs (by using appropriate Cloud Storage classes for infrequent access), and controlling bandwidth usage.

Google Cloud Documentation References:

Storage Transfer Service Overview: https://cloud.google.com/storage-transfer-service/docs/overview - This page details the capabilities and use cases of Storage Transfer Service, including transferring from on-premises.

Storage Transfer Service for on-premises data: https://cloud.google.com/storage-transfer-service/docs/on-prem-overview - This specifically covers transferring data from on-premises file systems.

Cloud Storage Classes: https://cloud.google.com/storage/docs/storage-classes - Understanding the different storage classes (Standard, Nearline, Coldline, Archive) is crucial for cost optimization of infrequently accessed data. Storage Transfer Service can be configured to move data to a cost-effective class like Nearline or Coldline.

===========

https://cloud.google.com/network-connectivity/docs/router/support/troubleshooting#ecmp

Cloud NGFW Enterprise provides TLS inspection to detect and manage threats within encrypted traffic. Configuring firewall rules for TLS inspection enables granular monitoring and filtering, ensuring secure internet traffic.

Explanation: The most efficient way to resolve the conflict is to temporarily remove the conflicting vpc-pre-prod spoke, add the vpc-dev spoke, and then re-add vpc-pre-prod. This ensures that the migration happens quickly without the need to change IP ranges or delete resources.

To control internet access on a per-URL basis (including hostname and path), you should deploy Secure Web Proxy with global access enabled. The Secure Web Proxy will allow policy-based filtering of web traffic, allowing control over which URLs can be accessed based on the URL list defined in the policy. Unlike Cloud NAT, which does not support FQDN filtering, Secure Web Proxy is designed to provide such control, especially for scenarios with sensitive or controlled internet access requirements.

For Dedicated Interconnects: At least four Dedicated Interconnect connections, two connections in one metropolitan area (metro) and two connections in another metro. Connections that are in the same metro must be placed in different edge availability domains (metro availability zones) to achieve 99.99% availability.

For HA VPN:

HA VPN to peer VPN gateways Connect an HA VPN gateway to one or two separate peer VPN devices 99.99%

HA VPN between two Google Cloud networks Connect two Google Cloud VPC networks in a single region by using an HA VPN gateway in each network 99.99%

If responses served by Cloud CDN are not compressed but should be, check that the web server software running on your instances is configured to compress responses. By default, some web server software will automatically disable compression for requests that include a Via header. The presence of a Via header indicates the request was forwarded by a proxy. HTTP proxies such as HTTP(S) load balancing add a Via header to each request as required by the HTTP specification. To enable compression, you may have to override your web server's default configuration to tell it to compress responses even if the request had a Via header.

Explanation: Associating the private zone to "vpc-a" and creating an outbound forwarding policy allows DNS queries to be forwarded from on-premises to Google Cloud DNS. The on-premises DNS servers will forward queries to the entry points created when the forwarding policy was applied to "vpc-a," enabling proper name resolution.

https://cloud.google.com/kubernetes-engine/docs/how-to/alias-ips#cluster_sizing_secondary_range_pods