Google Associate Cloud Engineer Practice Exams
Last updated on Oct 21,2026- Exam Code: Associate Cloud Engineer
- Exam Name: Google Cloud Certified – Associate Cloud Engineer
- Certification Provider: Google
- Latest update: Oct 21,2026
Your company wants to migrate their on-premises workloads to Google Cloud.
The current on-premises workloads consist of:
• A Flask web application
• AbackendAPI
• A scheduled long-running background job for ETL and reporting.
You need to keep operational costs low You want to follow Google-recommended practices to migrate these workloads to serverless solutions on Google Cloud.
What should you do?
- A . Migrate the web application to App Engine and the backend API to Cloud Run Use Cloud Tasks to run your background job on Compute Engine
- B . Migrate the web application to App Engine and the backend API to Cloud Run. Use Cloud Tasks to run your background job on Cloud Run.
- C . Run the web application on a Cloud Storage bucket and the backend API on Cloud Run Use Cloud Tasks to run your background job on Cloud Run.
- D . Run the web application on a Cloud Storage bucket and the backend API on Cloud Run. Use Cloud Tasks to run your background job on Compute Engine
Your company wants to migrate their on-premises workloads to Google Cloud.
The current on-premises workloads consist of:
• A Flask web application
• AbackendAPI
• A scheduled long-running background job for ETL and reporting.
You need to keep operational costs low You want to follow Google-recommended practices to migrate these workloads to serverless solutions on Google Cloud.
What should you do?
- A . Migrate the web application to App Engine and the backend API to Cloud Run Use Cloud Tasks to run your background job on Compute Engine
- B . Migrate the web application to App Engine and the backend API to Cloud Run. Use Cloud Tasks to run your background job on Cloud Run.
- C . Run the web application on a Cloud Storage bucket and the backend API on Cloud Run Use Cloud Tasks to run your background job on Cloud Run.
- D . Run the web application on a Cloud Storage bucket and the backend API on Cloud Run. Use Cloud Tasks to run your background job on Compute Engine
You want to configure autohealing for network load balancing for a group of Compute Engine instances that run in multiple zones, using the fewest possible steps. You need to configure re-creation of VMs if they are unresponsive after 3 attempts of 10 seconds each.
What should you do?
- A . Create an HTTP load balancer with a backend configuration that references an existing instance group. Set the health check to healthy (HTTP).
- B . Create an HTTP load balancer with a backend configuration that references an existing instance group. Define a balancing mode and set the maximum RPS to 10.
- C . Create a managed instance group. Set the Autohealing health check to healthy (HTTP).
- D . Create a managed instance group. Verify that the autoscaling setting is on.
C
Explanation:
https://cloud.google.com/compute/docs/instance-groups https://cloud.google.com/load-balancing/docs/network/transition-to-backend-services#console
In order to enable auto-healing, you need to group the instances into a managed instance group. Managed instance groups (MIGs) maintain the high availability of your applications by proactively keeping your virtual machine (VM) instances available. An auto-healing policy on the MIG relies on an application-based health check to verify that an application is responding as expected. If the auto-healer determines that an application isnt responding, the managed instance group automatically recreates that instance.
It is important to use separate health checks for load balancing and for auto-healing. Health checks for load balancing can and should be more aggressive because these health checks determine whether an instance receives user traffic. You want to catch non-responsive instances quickly, so you can redirect traffic if necessary. In contrast, health checking for auto-healing causes Compute Engine to proactively replace failing instances, so this health check should be more conservative than a load balancing health check.
You want to configure autohealing for network load balancing for a group of Compute Engine instances that run in multiple zones, using the fewest possible steps. You need to configure re-creation of VMs if they are unresponsive after 3 attempts of 10 seconds each.
What should you do?
- A . Create an HTTP load balancer with a backend configuration that references an existing instance group. Set the health check to healthy (HTTP).
- B . Create an HTTP load balancer with a backend configuration that references an existing instance group. Define a balancing mode and set the maximum RPS to 10.
- C . Create a managed instance group. Set the Autohealing health check to healthy (HTTP).
- D . Create a managed instance group. Verify that the autoscaling setting is on.
C
Explanation:
https://cloud.google.com/compute/docs/instance-groups https://cloud.google.com/load-balancing/docs/network/transition-to-backend-services#console
In order to enable auto-healing, you need to group the instances into a managed instance group. Managed instance groups (MIGs) maintain the high availability of your applications by proactively keeping your virtual machine (VM) instances available. An auto-healing policy on the MIG relies on an application-based health check to verify that an application is responding as expected. If the auto-healer determines that an application isnt responding, the managed instance group automatically recreates that instance.
It is important to use separate health checks for load balancing and for auto-healing. Health checks for load balancing can and should be more aggressive because these health checks determine whether an instance receives user traffic. You want to catch non-responsive instances quickly, so you can redirect traffic if necessary. In contrast, health checking for auto-healing causes Compute Engine to proactively replace failing instances, so this health check should be more conservative than a load balancing health check.
You have been asked to set up Object Lifecycle Management for objects stored in storage buckets. The objects are written once and accessed frequently for 30 days. After 30 days, the objects are not read again unless there is a special need. The object should be kept for three years, and you need to minimize cost.
What should you do?
- A . Set up a policy that uses Nearline storage for 30 days and then moves to Archive storage for three years.
- B . Set up a policy that uses Standard storage for 30 days and then moves to Archive storage for three years.
- C . Set up a policy that uses Nearline storage for 30 days, then moves the Coldline for one year, and then moves to Archive storage for two years.
- D . Set up a policy that uses Standard storage for 30 days, then moves to Coldline for one year, and then moves to Archive storage for two years.
B
Explanation:
The key to understand the requirement is: "The objects are written once and accessed frequently for
30 days" Standard Storage
Standard Storage is best for data that is frequently accessed ("hot" data) and/or stored for only brief periods of time.
Archive Storage
Archive Storage is the lowest-cost, highly durable storage service for data archiving, online backup, and disaster recovery. Unlike the "coldest" storage services offered by other Cloud providers, your data is available within milliseconds, not hours or days. Archive Storage is the best choice for data that you plan to access less than once a year.
https://cloud.google.com/storage/docs/storage-classes#standard
You are building a new version of an application hosted in an App Engine environment. You want to test the new version with 1% of users before you completely switch your application over to the new version.
What should you do?
- A . Deploy a new version of your application in Google Kubernetes Engine instead of App Engine and then use GCP Console to split traffic.
- B . Deploy a new version of your application in a Compute Engine instance instead of App Engine and then use GCP Console to split traffic.
- C . Deploy a new version as a separate app in App Engine. Then configure App Engine using GCP Console to split traffic between the two apps.
- D . Deploy a new version of your application in App Engine. Then go to App Engine settings in GCP Console and split traffic between the current version and newly deployed versions accordingly.
D
Explanation:
GCP App Engine natively offers traffic splitting functionality between versions. You can use traffic splitting to specify a percentage distribution of traffic across two or more of the versions within a service. Splitting traffic allows you to conduct A/B testing between your versions and provides control over the pace when rolling out features.
Ref: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic
You are building a new version of an application hosted in an App Engine environment. You want to test the new version with 1% of users before you completely switch your application over to the new version.
What should you do?
- A . Deploy a new version of your application in Google Kubernetes Engine instead of App Engine and then use GCP Console to split traffic.
- B . Deploy a new version of your application in a Compute Engine instance instead of App Engine and then use GCP Console to split traffic.
- C . Deploy a new version as a separate app in App Engine. Then configure App Engine using GCP Console to split traffic between the two apps.
- D . Deploy a new version of your application in App Engine. Then go to App Engine settings in GCP Console and split traffic between the current version and newly deployed versions accordingly.
D
Explanation:
GCP App Engine natively offers traffic splitting functionality between versions. You can use traffic splitting to specify a percentage distribution of traffic across two or more of the versions within a service. Splitting traffic allows you to conduct A/B testing between your versions and provides control over the pace when rolling out features.
Ref: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic
You are building a new version of an application hosted in an App Engine environment. You want to test the new version with 1% of users before you completely switch your application over to the new version.
What should you do?
- A . Deploy a new version of your application in Google Kubernetes Engine instead of App Engine and then use GCP Console to split traffic.
- B . Deploy a new version of your application in a Compute Engine instance instead of App Engine and then use GCP Console to split traffic.
- C . Deploy a new version as a separate app in App Engine. Then configure App Engine using GCP Console to split traffic between the two apps.
- D . Deploy a new version of your application in App Engine. Then go to App Engine settings in GCP Console and split traffic between the current version and newly deployed versions accordingly.
D
Explanation:
GCP App Engine natively offers traffic splitting functionality between versions. You can use traffic splitting to specify a percentage distribution of traffic across two or more of the versions within a service. Splitting traffic allows you to conduct A/B testing between your versions and provides control over the pace when rolling out features.
Ref: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic
You have developed an application that consists of multiple microservices, with each microservice packaged in its own Docker container image. You want to deploy the entire application on Google Kubernetes Engine so that each microservice can be scaled individually.
What should you do?
- A . Create and deploy a Custom Resource Definition per microservice.
- B . Create and deploy a Docker Compose File.
- C . Create and deploy a Job per microservice.
- D . Create and deploy a Deployment per microservice.
You have developed an application that consists of multiple microservices, with each microservice packaged in its own Docker container image. You want to deploy the entire application on Google Kubernetes Engine so that each microservice can be scaled individually.
What should you do?
- A . Create and deploy a Custom Resource Definition per microservice.
- B . Create and deploy a Docker Compose File.
- C . Create and deploy a Job per microservice.
- D . Create and deploy a Deployment per microservice.