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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.
Question #6
(Your companys developers use an automation that you recently built to provision Linux VMs in Compute Engine within a Google Cloud project to perform various tasks. You need to manage the Linux account lifecycle and access for these users. You want to follow Google-recommended practices to simplify access management while minimizing operational costs. What should you do?)
A. Enable OS Login for all VMs. Use IAM roles to grant user permissions.
B. Enable OS Login for all VMs. Write custom startup scripts to update user permissions.
C. Require your developers to create public SSH keys. Make the owner of the public key the root user.
D. Require your developers to create public SSH keys. Write custom startup scripts to update user permissions.
Answer: A
Explanation:
OS Login is a Google-recommended practice for managing access to Linux VMs in Compute Engine. It
centralizes user account management by linking the Linux user accounts on the VMs to Google Cloud
identities. You then use IAM roles to grant users the necessary permissions to access the VMs (e.g.,
roles/compute.osLogin or roles/compute.osAdminLogin). This simplifies management as you control
access through IAM policies rather than managing individual SSH keys on each VM, thus minimizing
operational costs.
Option B: While enabling OS Login is a good first step, writing custom startup scripts to manage user
permissions adds complexity and operational overhead, contradicting the goal of simplification and
minimizing costs.
Option C: Requiring developers to manage their own SSH keys and making the owner root is a
significant security risk and not a recommended practice. It also doesn't centralize management.
Option D: This approach also involves managing individual SSH keys and custom scripts, which
increases operational overhead and doesn't leverage the centralized management benefits of OS
Login.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
OS Login and its benefits for simplified and secure Linux VM access management are detailed in the
Compute Engine documentation, which is a key area for the Associate Cloud Engineer certification.
The integration with IAM for permission control is a central aspect of this service.
Question #7
(You are migrating your on-premises workload to Google Cloud. Your company is implementing its Cloud Billing configuration and requires access to a granular breakdown of its Google Cloud costs. You need to ensure that the Cloud Billing datasets are available in BigQuery so you can conduct a detailed analysis of costs. What should you do?)
A. Enable the BigQuery API and ensure that the BigQuery User IAM role is selected. Change the BigQuery dataset to select a data location.
B. Create a Cloud Billing account. Enable the BigQuery Data Transfer Service API to export pricing data.
C. Enable Cloud Billing data export to BigQuery when you create a Cloud Billing account.
D. Enable Cloud Billing on the project and link a Cloud Billing account. Then view the billing data table in the BigQuery dataset.
Answer: C
Explanation:
The most direct and recommended way to get a granular breakdown of your Google Cloud costs in
BigQuery is to enable Cloud Billing data export to BigQuery when you create or manage your Cloud
Billing account. This automatically sets up a daily export of your billing data to a BigQuery dataset
you specify.
Option A: Enabling the BigQuery API and managing IAM roles are necessary for interacting with
BigQuery, but they don't automatically populate it with Cloud Billing data. Selecting a data location is
also important for BigQuery datasets but is a separate step from enabling billing export.
Option B: The BigQuery Data Transfer Service is used for transferring data from various sources into
BigQuery, but for Cloud Billing data, the direct export feature is the standard and simpler method.
Option D: Enabling Cloud Billing and linking an account makes billing data available in the Cloud
Billing console, but it doesn't automatically export it to BigQuery for detailed analysis. You need to
explicitly configure the BigQuery export.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The process of setting up Cloud Billing export to BigQuery is clearly documented in the Google Cloud
Billing documentation, which is a fundamental area for the Associate Cloud Engineer certification.
Understanding how to access and analyze billing data is crucial for cost management.
Question #8
(You are deploying a web application using Compute Engine. You created a managed instance group (MIG) to host the application. You want to follow Google-recommended practices to implement a secure and highly available solution. What should you do?)
A. Use a proxy Network Load Balancer for the MIG and an A record in your DNS private zone with the load balancer's IP address.
B. Use a proxy Network Load Balancer for the MIG and a CNAME record in your DNS public zone with the load balancer's IP address.
C. Use an Application Load Balancer for the MIG and a CNAME record in your DNS private zone with the load balancer's IP address.
D. Use an Application Load Balancer for the MIG and an A record in your DNS public zone with the load balancer's IP address.
Answer: D
Explanation:
For a web application (typically using HTTP/HTTPS), an Application Load Balancer is the
recommended choice as it operates at Layer 7, providing features like content-based routing, SSL
termination, and improved security. To expose the application publicly, you would need to use a
public DNS zone. An A record in a public DNS zone maps a domain name to the public IP address of
the Application Load Balancer. Using a CNAME record would also work but is generally
recommended for aliasing one domain name to another, not directly to an IP address.
Option A & B: Network Load Balancers operate at Layer 4 (TCP/UDP) and lack the application-level
features of an Application Load Balancer. Private DNS zones are for internal name resolution within
your VPC, not for public access.
Option C: While an Application Load Balancer is the correct type, using a private DNS zone wouldn't
make the web application publicly accessible.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The best practices for load balancing web applications on Google Cloud, including the use of
Application Load Balancers for Layer 7 traffic and the configuration of public DNS records (A or
CNAME) for public access, are detailed in the Google Cloud Load Balancing and Cloud DNS
documentation, both important for the Associate Cloud Engineer certification.
Question #9
(Your company is modernizing its applications and refactoring them to containerized microservices. You need to deploy the infrastructure on Google Cloud so that teams can deploy their applications. The applications cannot be exposed publicly. You want to minimize management and operational overhead. What should you do?)
A. Provision a Standard zonal Google Kubernetes Engine (GKE) cluster.
B. Provision a fleet of Compute Engine instances and install Kubernetes.
C. Provision a Google Kubernetes Engine (GKE) Autopilot cluster.
D. Provision a Standard regional Google Kubernetes Engine (GKE) cluster.
Answer: C
Explanation:
GKE Autopilot is a mode of operation in GKE where Google manages the underlying infrastructure,
including nodes, node pools, and their upgrades. This significantly reduces the management and
operational overhead for the user, allowing teams to focus solely on deploying and managing their
containerized applications. Since the applications are not exposed publicly, the zonal or regional
nature of the cluster primarily impacts availability within Google Cloud, and Autopilot is available for
both. Autopilot minimizes the operational burden, which is a key requirement.
Option A: A Standard zonal GKE cluster requires you to manage the nodes yourself, including sizing,
scaling, and upgrades, increasing operational overhead compared to Autopilot.
Option B: Manually installing and managing Kubernetes on a fleet of Compute Engine instances
involves the highest level of management overhead, which contradicts the requirement to minimize
it.
Option D: A Standard regional GKE cluster provides higher availability than a zonal cluster by
replicating the control plane and nodes across multiple zones within a region. However, it still
requires you to manage the underlying nodes, unlike Autopilot.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The different modes of GKE operation, including Standard and Autopilot, and their respective
management responsibilities and benefits, are clearly outlined in the Google Kubernetes Engine
documentation, a core topic for the Associate Cloud Engineer certification. The emphasis on reduced
operational overhead with Autopilot is a key differentiator.
Question #10
(You are migrating your companys on-premises compute resources to Google Cloud. You need to deploy batch processing jobs that run every night. The jobs require significant CPU and memory for several hours but can tolerate interruptions. You must ensure that the deployment is cost-effective. What should you do?)
A. Containerize the batch processing jobs and deploy them on Compute Engine.
B. Use custom machine types on Compute Engine.
C. Use the M1 machine series on Compute Engine.
D. Use Spot VMs on Compute Engine.
Answer: D
Explanation:
Spot VMs (formerly known as preemptible VMs) are Compute Engine virtual machine instances that
are available at a much lower price than standard Compute Engine instances. However, Compute
Engine might preempt (stop) these instances if it needs to reclaim those resources for other tasks.
This makes Spot VMs ideal for batch processing jobs that are fault-tolerant and can handle
interruptions, as they can be restarted when resources become available again. This directly
addresses the requirement for a cost-effective solution for interruptible workloads.
Option A: While containerization offers portability and consistency, it doesn't inherently provide cost
savings for compute resources. You would still need to choose a cost-effective underlying compute
option.
Option B: Custom machine types allow you to tailor the CPU and memory configuration of your VMs,
which can optimize costs to some extent by avoiding over-provisioning. However, they don't offer
the significant cost reduction that Spot VMs provide.
Option C: The M1 machine series is a specific family of Compute Engine instances optimized for
memory-intensive workloads. While potentially suitable for the job's requirements, it doesn't
inherently address the cost-effectiveness requirement as directly as Spot VMs, which are priced
lower regardless of the machine series.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The concept and use cases for Spot VMs are explicitly covered in the Compute Engine section of the
Google Cloud documentation, which is a key area for the Associate Cloud Engineer certification. The
cost savings and suitability for fault-tolerant workloads are highlighted as primary benefits.
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