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Google Associate-Cloud-Engineer Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF
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Google Associate-Cloud-Engineer Sample Questions – Free Practice Test & Real Exam Prep
Question #1
You are planning to migrate your on-premises VMs to Google Cloud. You need to set up a landing zone in Google Cloud before migrating the VMs. You must ensure that all VMs in your production environment can communicate with each other through private IP addresses. You need to allow all VMs in your Google Cloud organization to accept connections on specific TCP ports. You want to follow Google-recommended practices, and you need to minimize your operational costs. What should you do?
A. Create individual VPCs per Google Cloud project. Peer all the VPCs together. Apply organization policies on the organization level.
B. Create individual VPCs for each Google Cloud project. Peer all the VPCs together. Apply hierarchical firewall policies on the organization level.
C. Create a host VPC project with each production project as its service project. Apply organization policies on the organization level.
D. Create a host VPC project with each production project as its service project. Apply hierarchical firewall policies on the organization level.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The goal is to create a landing zone facilitating private IP communication across production projects
and apply organization-wide firewall rules, following best practices and minimizing operational costs.
Network Structure:Individual VPCs with Peering (A, B): While VPC Peering allows private
connectivity, managing a full mesh or complex peering topology across many projects becomes
operationally complex and can hit peering limits. It's not the recommended pattern for centralized
connectivity in a landing zone.
Shared VPC (C, D): This is the Google-recommended practice for scenarios where resources from
multiple projects need to communicate privately within a common VPC network. A central host
project owns the network, and service projects use it. This simplifies network administration and
Google Cloud security foundations guide: Often recommends Shared VPC and centralized firewall
management (using Hierarchical Firewalls or traditional firewalls with tags in the host project) as part
of a secure landing zone. - (Conceptual reference, specific document may vary)
Question #2
You are deploying an application to Google Kubernetes Engine (GKE) that needs to call an external third-party API. You need to provide the external API vendor with a list of IP addresses for their firewall to allow traffic from your application. You want to follow Google-recommended practices and avoid any risk of interrupting traffic to the API due to IP address changes. What should you do?
A. Configure your GKE cluster with one node, and set the node to have a static external IP address. Ensure that the GKE cluster autoscaler is off. Send the external IP address of the node to the vendor to be added to the allowlist.
B. Configure your GKE cluster with private nodes. Configure a Cloud NAT instance with static IP addresses. Provide these IP addresses to the vendor to be added to the allowlist.
C. Configure your GKE cluster with public nodes. Write a Cloud Function that pulls the public IP addresses of each node in the cluster. Trigger the function to run every day with Cloud Scheduler. Send the list to the vendor by email every day.
D. Configure your GKE cluster with private nodes. Configure a Cloud NAT instance with dynamic IP addresses. Provide these IP addresses to the vendor to be added to the allowlist.
Answer: B
Explanation:
The requirement is for a stable set of egress IP addresses from a GKE cluster for allowlisting by a third
party, following best practices.
Option A is not recommended: Using a single node lacks scalability and high availability. Relying on a
single node's static IP creates a single point of failure and doesn't align with GKE's design principles.
Disabling autoscaling hinders elasticity.
Option C is complex and unreliable: Public nodes typically have ephemeral external IPs (unless
manually configured per node, which is difficult to manage with autoscaling). Dynamically tracking
and emailing IPs daily is operationally burdensome and prone to race conditions where the allowlist
might lag behind IP changes.
Option D uses Cloud NAT but with dynamic IPs. Dynamic IPs change over time, making them
unsuitable for stable firewall allowlists.
Option B is the Google-recommended practice: Configuring the GKE cluster with private nodes
enhances security as nodes don't have direct external IPs. Cloud NAT provides managed network
address translation for these private nodes to access the internet. By configuring Cloud NAT with a
static allocation of external IP addresses, all egress traffic from the private GKE nodes will appear to
originate from this stable, predictable set of IPs. This set can be given to the vendor for allowlisting
without worrying about node IP changes due to scaling or maintenance.
This approach decouples the application's egress IP from the individual nodes, providing stability and
adhering to the principle of least privilege (private nodes).
Reference:
Cloud NAT Overview: "Cloud NAT lets certain resources without external IP addresses create
You have an application that is currently processing transactions by using a group of managed VM instances. You need to migrate the application so that it is serverless and scalable. You want to implement an asynchronous transaction processing system, while minimizing management overhead. What should you do?
A. Install Kafka on VM instances to acknowledge incoming transactions. Use Cloud Run to process transactions.
B. Install Kafka on VM Instances to acknowledge incoming transactions. Use VM Instances to process transactions.
C. Use Pub/Sub to acknowledge incoming transactions. Use VM instances to process transactions.
D. Use Pub/Sub to acknowledge incoming transactions. Use Cloud Run to process transactions.
Answer: D
Explanation:
The goal is to create a serverless, scalable, and asynchronous transaction processing system with
minimal management overhead.
Serverless Requirement:Options involving installing Kafka on VMs (A, B) or using VM instances for
processing (B, C) introduce management overhead associated with VMs (patching, scaling
configuration, OS management) and Kafka cluster management, violating the serverless and minimal
management criteria.
Asynchronous Requirement:Both Kafka and Pub/Sub can handle asynchronous messaging. However,
Pub/Sub is Google Cloud's fully managed, serverless messaging service, inherently minimizing
management overhead compared to self-managed Kafka on VMs.
Scalability and Processing:Cloud Run is a fully managed, serverless platform that automatically scales
based on traffic, suitable for processing transactions without managing underlying infrastructure. VM
(You host your website on Compute Engine. The number of global users visiting your website is rapidly expanding. You need to minimize latency and support user growth in multiple geographical regions. You also want to follow Google-recommended practices and minimize operational costs. Which two actions should you take? Choose 2 answers)
A. Deploy all of your VMs in a single Google Cloud region with the largest available CIDR range.
B. Deploy your VMs in multiple Google Cloud regions closest to your users geographical locations.
C. Use an external Application Load Balancer in Regional mode.
D. Use an external Application Load Balancer in Global mode.
E. Use a Network Load Balancer.
Answer: BD
Explanation:
To minimize latency for a global user base, it's crucial to serve users from regions geographically
close to them. Deploying VMs in multiple Google Cloud regions (Option B) achieves this by reducing
the network distance and thus the round-trip time for requests.
To support user growth and provide a single point of entry with global reach, a global external
Application Load Balancer (Option D) is the recommended choice for web applications. It distributes
traffic to backend instances across multiple regions based on user proximity, capacity, and health.
Application Load Balancers also offer features like SSL termination, content-based routing, and
security policies, which are important for modern web applications.
* Option A: Deploying in a single region, regardless of the CIDR range, will result in high latency for
users far from that region.
* Option C: A regional external Application Load Balancer only distributes traffic within a single
region, not across multiple global regions, thus not effectively minimizing latency for all global users.
* Option E: Network Load Balancers operate at Layer 4 and don't offer the application-level routing
and features of an Application Load Balancer, which are generally preferred for web applications.
While they can be global, Application Load Balancers are better suited for this scenario.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The concepts of multi-region deployments for low latency and the use of global load balancers
(specifically Application Load Balancers for web traffic) for global reach and traffic management are
core topics in the Compute Engine and Load Balancing sections of the Google Cloud documentation,
which are essential for the Associate Cloud Engineer certification. The best practices for global
application deployment are emphasized.
Question #5
(You are managing a stateful application deployed on Google Kubernetes Engine (GKE) that can only have one replic a. You recently discovered that the application becomes unstable at peak times. You have identified that the application needs more CPU than what has been configured in the manifest at these peak times. You want Kubernetes to allocate the application sufficient CPU resources during these peak times, while ensuring cost efficiency during off-peak periods. What should you do?)
A. Enable cluster autoscaling on the GKE cluster.
B. Configure a Vertical Pod Autoscaler on the Deployment.
C. Configure a Horizontal Pod Autoscaler on the Deployment.
D. Enable node auto-provisioning on the GKE cluster.
Answer: B
Explanation:
The Vertical Pod Autoscaler (VPA) in Kubernetes automatically adjusts the CPU and memory requests
and limits of the containers within a pod based on historical and real-time resource usage. In this
scenario, where a single-replica stateful application needs more CPU during peak times, VPA can
dynamically increase the CPU allocated to the pod when needed and potentially decrease it during
off-peak periods to optimize resource utilization and cost efficiency.
Option A: Cluster autoscaling adds or removes nodes in your GKE cluster based on the resource
requests of your pods. While it can help with overall cluster capacity, it oesn't directly address the
need for more CPU for a specific pod.
Option C: Horizontal Pod Autoscaler (HPA) scales the number of pod replicas based on observed CPU
utilization or other select metrics. Since the application can only have one replica, HPA is not
suitable.
Option D: Node auto-provisioning is similar to cluster autoscaling, automatically creating and
deleting node pools based on workload demands. It doesn't directly manage the resources of
individual pods.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The functionality and use cases of the Vertical Pod Autoscaler (VPA) are detailed in the Google
Kubernetes Engine documentation, specifically within the resource management and autoscaling
sections. Understanding how VPA can dynamically adjust pod resources is relevant to the Associate
Cloud Engineer certification.
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