ACE — Google Cloud Associate Cloud Engineer Cheat Sheet
Last revised: September 16, 2026
Compact Google Cloud ACE Cheat sheet for IAM, compute, storage, networking, deployment, operations, and exam decision points.
Use the tables for a quick pre-exam check. Expand a topic’s notes for explanations, examples, and additional distinctions.
Scope and study context
The ACE exam is practical. Expect scenario-style decisions about setting up cloud environments, deploying workloads, managing access, operating resources, and choosing appropriate Google Cloud services. The key is not just knowing product names; it is knowing what to do first, what is safest, what is managed, and what avoids unnecessary operational work.
After reviewing the tables above, move directly into IT Mastery practice:
Start with topic drills for IAM, networking, compute, and storage.
Review every missed question using detailed explanations.
Convert each miss into a rule: service choice, IAM scope, network path, or operational sequence.
Take mixed sets from the question bank to practice switching topics quickly.
Use mock exams only after your weak-topic accuracy improves.
A practical next step: complete a focused set of original practice questions on IAM and networking first, because those concepts appear across many Google Cloud ACE scenarios.
ACE mental model
Exam task pattern
Know how to do it
Common exam cue
Set up a cloud environment
Create/select project, link billing, enable APIs, set gcloud config, use Cloud Shell
“A new project needs to use Compute Engine”
Plan and configure resources
Pick region/zone, VPC/subnet, IAM roles, service account, storage/database
Predefined role scoped to the narrowest resource; custom role only if needed
Owner, Editor, broad project-level grants
Reduce operations
Managed/serverless service that satisfies requirements
Self-managed VMs when managed service fits
VM has no external IP but needs outbound internet
Cloud NAT
Assigning external IPs to every VM
VM has no external IP but needs Google APIs
Private Google Access on the subnet, plus IAM
Cloud NAT as the only answer when it only needs Google APIs
Workload needs Google Cloud credentials
Attached service account, impersonation, or Workload Identity Federation
Downloaded long-lived service account keys
App needs secrets
Secret Manager, sometimes Cloud KMS for encryption keys
Hardcoded secrets, VM metadata, source control
Asynchronous decoupling
Pub/Sub
Synchronous direct calls between every service
Analytics over large datasets
BigQuery
Cloud SQL for analytical scans
Relational OLTP
Cloud SQL or Spanner depending scale/global needs
BigQuery or Bigtable
Cost grouping
Labels, billing export, budgets/alerts
Treating labels as IAM or hierarchy controls
Environment setup: projects, billing, APIs, and CLI
Resource setup checklist
Step
What to verify
Why it matters
Select project
Correct PROJECT_ID in Console or gcloud config
Many errors are wrong-project errors
Billing
Project is linked to an active billing account
APIs/resources may fail without billing
APIs
Required service APIs are enabled
Permissions are not enough if API is disabled
Region/zone
Defaults match intended deployment location
Avoid accidental cross-region resources
IAM
User or service account has required role
Console visibility and deployment both depend on IAM
Quotas
Resource quota is available
Quota failures are not fixed by IAM alone
Notes and examples
Essential gcloud setup commands
gcloud init
gcloud auth list
gcloud config configurations list
gcloud config set project PROJECT_ID
gcloud config set compute/region REGION
gcloud config set compute/zone ZONE
gcloud services list --enabled
gcloud services enable compute.googleapis.com run.googleapis.com container.googleapis.com
Command pattern
Use when
Trap
gcloud auth login
Authenticate the CLI as a user
Does not automatically provide application credentials to local code
gcloud auth application-default login
Test local code using Application Default Credentials
Not the same as a service account attached to a deployed workload
gcloud config set project
Set default project for commands
Some commands still need explicit region/zone
gcloud services enable SERVICE
Enable an API before using a product
API enabled does not grant IAM permission
Cloud Shell
Quick admin tasks in browser
Cloud Shell still operates in the selected project/config
Resource hierarchy, IAM, and service accounts
Resource hierarchy
Level
Purpose
Exam notes
Organization
Root for company-owned Google Cloud resources
Central IAM, org policies, folders
Folder
Group projects by team, app, environment, or business unit
IAM and policies can inherit down
Project
Main boundary for APIs, billing linkage, IAM grants, quotas, resources
Most ACE tasks happen at project scope
Resource
VM, bucket, dataset, cluster, topic, etc.
Some resources support resource-level IAM
Notes and examples
IAM allow policies inherit downward: organization → folder → project → resource. A broad role at a high level can unintentionally grant access to many resources.
IAM decision table
Need
Use
Avoid / trap
Grant standard product access
Predefined role, such as storage object viewer/admin, compute admin, logs viewer
Basic roles unless explicitly appropriate
Grant only a few permissions
Custom role
Custom roles add maintenance overhead
Grant temporary/conditional access
IAM Conditions when supported
Relying on manual cleanup
Let a user deploy a VM using a service account
Grant service account attachment permission, commonly Service Account User, on that service account
Granting the user all permissions the service account has
Let a workload access Google Cloud APIs
Attach a service account to the workload and grant that service account target permissions
Embedding user credentials or key files
Investigate who changed something
Cloud Audit Logs
Application logs alone
Block allowed access in specific cases
IAM deny policy or organization policy, if configured
Assuming an allow grant always wins
Role types
Role type
Scope
Use for ACE scenarios
Basic roles: Owner, Editor, Viewer
Very broad project-level roles
Rarely the best answer for least privilege
Predefined roles
Google-managed roles for specific products/tasks
Default choice for exam answers
Custom roles
User-defined permission sets
When predefined roles are too broad and exact permissions are known
Service account traps
Concept
Correct interpretation
Service account as identity
A workload can run as a service account. That service account needs permissions on target resources.
Service account as resource
A user may need permission to attach, impersonate, or manage the service account itself.
Service Account User
Lets a principal run/attach resources as the service account, depending on context. It does not automatically grant all target-resource permissions to the human user.
Service Account Token Creator
Used for impersonation/token creation scenarios. More sensitive than simple viewing.
Key files
Long-lived credentials. Prefer attached service accounts, impersonation, or Workload Identity Federation where possible.
Default service accounts
Convenient, but do not assume they are least-privilege or safe for production.
Common IAM troubleshooting path
Confirm active identity: user, group, service account, or workload identity.
Confirm correct project, folder, or resource.
Confirm API is enabled.
Check allow policy at resource and inherited levels.
Check deny policies or organization policies if access still fails.
For VMs/GKE/serverless, check the runtime service account, not only the deployer’s account.
For BigQuery, check both job permissions on the project and data permissions on datasets/tables.
Google Cloud resource hierarchy
Google Cloud resources are organized hierarchically. Many ACE questions test where to apply permissions or policies.
Level
What to remember
Organization
Top-level resource for a company using Google Cloud
Folder
Optional grouping under an organization
Project
Main boundary for resources, APIs, IAM, billing association, and quotas
Resource
VM, bucket, dataset, service account, cluster, database, etc.
Decision rules
Use projects to separate environments, applications, teams, or billing boundaries.
IAM policies are commonly inherited down the hierarchy.
Grant access at the lowest practical level.
Avoid granting broad access at the organization or folder level unless there is a clear administrative need.
A project must generally have required APIs enabled before services can be used.
A project must be associated with billing for billable resources.
Candidate traps
Trap
Better thinking
Granting Owner or Editor because something “does not work”
Identify the missing permission and use a predefined or custom least-privilege role
Applying project-level access when only one bucket/dataset is needed
Grant access on the specific resource if possible
Forgetting API enablement
If deployment fails because a service is unavailable, check whether the API is enabled
Confusing billing account with project ownership
Billing pays for resources; IAM controls access
IAM and service accounts
IAM is one of the most important ACE areas. You need to distinguish who is acting, what they are accessing, and what role is required.
IAM essentials
Concept
Review point
Principal
User, group, service account, domain, or other identity
Role
Collection of permissions
Permission
Specific action, such as compute.instances.get
Policy
Bindings of principals to roles on a resource
Basic roles
Owner, Editor, Viewer; broad and usually not best practice
Predefined roles
Google-managed roles for specific services/tasks
Custom roles
User-defined permissions for stricter least privilege
Service account
Identity used by workloads and automation
Service account decision rules
Need
Use
VM or app needs to call Google APIs
Attach a service account with required IAM roles
User needs to deploy a VM using a service account
Grant Service Account User on that service account
User or automation needs to impersonate a service account
Grant appropriate impersonation permission, such as Service Account Token Creator where required
GKE workload needs Google Cloud API access
Prefer Workload Identity / workload identity-based access rather than long-lived keys
External system needs access without service account keys
Prefer Workload Identity Federation where suitable
Application needs a secret
Use Secret Manager, not hardcoded environment variables or source code
IAM traps
Roles are not the same as OAuth scopes. For Compute Engine access to Google APIs, both IAM permissions and access scopes can matter. A common modern pattern is broad API scope with least-privilege IAM.
Service account keys are risky. Prefer attached service accounts, impersonation, or federation instead of downloading long-lived keys.
Default service accounts are often overused. Create purpose-specific service accounts where practical.
Viewer does not mean “safe for all data.” Some viewer roles can expose sensitive configuration or metadata.
Compute and deployment selection
Compute service selection matrix
Requirement
Choose
Why
Watch for
Full OS control, custom agents, custom networking
Compute Engine
Infrastructure as a Service VMs
You manage patching, scaling design, OS config
Identical VM fleet with autoscaling/autohealing
Managed instance group
Uses instance template, health checks, rolling updates
Instance template changes require rollout
Run stateless container without managing cluster
Cloud Run
Serverless containers, scales based on traffic/events
Container must fit Cloud Run execution model
Event-driven function
Cloud Functions
Deploy function code triggered by events or HTTP
Less control than full container/VM
PaaS app from source with built-in scaling
App Engine
Managed application platform
App Engine app location is an important early choice
Kubernetes orchestration
Google Kubernetes Engine
Pods, services, deployments, cluster ecosystem
More Kubernetes concepts and operational responsibility
Fault-tolerant batch or interruptible work
Spot/preemptible VMs, Batch, or managed autoscaled workers
Lower-cost compute for restartable work
Do not use for stateful critical workloads without recovery
Notes and examples
Compute Engine quick reference
Feature
Use
Exam trap
Machine type
CPU/memory sizing
Resize may require stop/start depending change
Boot disk
OS disk for VM
Deleting VM may delete boot disk depending setting
Persistent Disk
Durable block storage for VMs
Not shared POSIX file storage
Local SSD
Very high performance ephemeral storage
Data is not durable through all lifecycle events
Snapshot
Point-in-time disk backup
Snapshot is not a bootable image by itself in the same way an image is
Custom image
Reusable VM boot disk image
Good for consistent VM creation
Instance template
Defines VM configuration for MIGs
Immutable; create a new template for changes
Startup script
Bootstrap VM on boot/create
Not a full configuration management system
Metadata
VM/project metadata
Do not store secrets in plain metadata
Shielded VM
Integrity protections for VMs
Security feature, not an IAM substitute
Managed instance group decisions
Need
MIG feature
Replace unhealthy VMs
Autohealing with health check
Add/remove VMs based on demand
Autoscaling
Update a fleet gradually
Rolling update
Serve traffic through load balancer
Backend service uses instance group
Keep consistent VM config
Instance template
Serverless deployment comparison
Service
Deployable unit
Common trigger
Best fit
Cloud Run
Container image
HTTP, events, jobs depending configuration
Portable stateless services and APIs
Cloud Functions
Function source/code
HTTP or event trigger
Small event-driven units of logic
App Engine
Application source
HTTP app traffic
Managed web apps with minimal infrastructure control
GKE essentials
Kubernetes/GKE concept
What to know for ACE
Cluster
Control plane plus worker capacity. Regional/zonal choice affects availability and latency.
Node pool
Group of nodes with similar machine/config. Standard mode exposes more node management.
Autopilot
More Google-managed cluster/node operations. Less node-level control.
Pod
Smallest deployable Kubernetes unit. Usually managed by higher-level controllers.
Deployment
Manages replica rollout/rollback for stateless pods.
Service
Stable virtual endpoint for pods. Types include internal and external exposure patterns.
Ingress / Gateway
HTTP(S) routing into services. Often integrates with load balancing.
ConfigMap
Non-secret configuration.
Kubernetes Secret
Kubernetes-native secret object; not the same as Secret Manager.
Workload Identity Federation for GKE
Preferred way for pods to access Google Cloud APIs without service account key files.
Useful GKE commands:
gcloud container clusters get-credentials CLUSTER_NAME --region REGION --project PROJECT_ID
kubectl get pods -A
kubectl get services -A
kubectl describe pod POD_NAME -n NAMESPACE
kubectl logs POD_NAME -n NAMESPACE
kubectl rollout status deployment/DEPLOYMENT_NAME -n NAMESPACE
Choosing the right compute service is a core ACE skill.
flowchart TD
A[Need to run application code] --> B{Need full OS control?}
B -->|Yes| C[Compute Engine]
B -->|No| D{Containerized?}
D -->|Yes| E{Need Kubernetes API/control?}
E -->|Yes| F[Google Kubernetes Engine]
E -->|No| G[Cloud Run]
D -->|No| H{Event-driven function?}
H -->|Yes| I[Cloud Functions]
H -->|No| J[App Engine or managed platform option]
Compute Engine
Use Compute Engine when you need VM-level control.
Feature
Review point
Machine type
CPU/memory shape; choose appropriately for workload
Boot disk
Persistent disk used for OS
Persistent Disk
Durable block storage; zonal or regional options
Local SSD
High performance but ephemeral
Instance template
Reusable VM configuration for managed instance groups
Managed instance group
Autoscaling, autohealing, rolling updates
Startup script
Boot-time configuration via metadata
Shielded VM
Security hardening features
Spot VMs
Lower-cost, interruptible workloads
Managed instance group traps
Trap
Correction
Using unmanaged groups for autohealing/autoscaling
Use managed instance groups
Forgetting health check firewall rules
Allow health check ranges where required
Manually changing instances in a MIG
Update template or rolling update strategy
Expecting zonal MIG to survive zone failure
Use regional MIG for multi-zone resilience
GKE
Use Google Kubernetes Engine when the scenario requires Kubernetes orchestration.
Concept
Review point
Cluster
Kubernetes control plane plus nodes or managed capacity
Node pool
Group of nodes with common configuration
Pod
Smallest deployable workload unit
Service
Stable network endpoint for pods
Ingress / Gateway
HTTP(S) routing into cluster workloads
Autopilot
More managed GKE mode; less node management
Standard
More control over nodes and cluster configuration
Cloud Run
Use Cloud Run for stateless containers with minimal operations.
High-yield points:
Runs containers.
Scales based on requests/events.
Can scale to zero.
Uses revisions and traffic splitting.
Good for HTTP APIs, web apps, background services, and containerized jobs.
Store images in Artifact Registry.
Use environment variables for non-secret config and Secret Manager for secrets.
App Engine and Cloud Functions
Service
Best fit
App Engine
Managed web app platform with versions and traffic splitting
Cloud Functions
Event-driven functions for lightweight code triggered by events
Cloud Run
More flexible container-based serverless runtime
Storage, databases, and analytics
Storage service selection
Requirement
Choose
Why
Avoid / trap
Object storage for images, backups, static assets
Cloud Storage
Durable object storage with buckets and lifecycle rules
Not a mounted POSIX file system by default
Block disk for VM
Persistent Disk
VM-attached durable block storage
Not independent object storage
Shared file system for applications
Filestore
Managed NFS file storage
Not a relational database
Ephemeral high-speed scratch disk
Local SSD
High I/O temporary storage
Data loss risk on certain VM events
Long-term object retention / archive
Cloud Storage lifecycle + colder storage classes
Lower storage cost for infrequent access
Retrieval/access patterns matter
Static website assets
Cloud Storage, often with load balancing/CDN pattern
Simple object hosting
Dynamic application logic needs compute
Notes and examples
Cloud Storage quick reference
Feature
Use
Exam trap
Bucket
Container for objects
Bucket names are globally unique
Object
Stored file/blob
Objects are not edited in-place like normal files
Location
Region, dual-region, or multi-region
Choose based on latency, availability, data locality
Storage class
Cost/access optimization
Do not choose archive class for frequently accessed data
Lifecycle rule
Automatically transition/delete objects
Good for cost control and retention workflows
Object versioning
Keep older versions
Can increase storage usage
Uniform bucket-level access
IAM-based bucket/object access model
Avoid mixing legacy ACL expectations
Signed URL
Temporary access to an object
Does not require making bucket public
Retention policy
Prevent deletion/modification for retention period
ACE candidates should be comfortable with common administrative setup tasks.
gcloud review
Task
Command pattern to recognize
Authenticate user
gcloud auth login
Use application default credentials locally
gcloud auth application-default login
Set project
gcloud config set project PROJECT_ID
Set default region
gcloud config set compute/region REGION
Set default zone
gcloud config set compute/zone ZONE
List active config
gcloud config list
Manage configurations
gcloud config configurations ...
Enable an API
gcloud services enable SERVICE_NAME
View IAM policy
gcloud projects get-iam-policy PROJECT_ID
Add IAM binding
gcloud projects add-iam-policy-binding ...
Quick setup checklist
Create or select the correct project.
Confirm billing association if resources will be created.
Enable required APIs.
Set gcloud project, region, and zone.
Create least-privilege service accounts.
Grant only required IAM roles.
Deploy resources.
Configure monitoring, logging, and alerts.
Deployment and release management
ACE candidates should recognize common deployment paths and operationally safe release patterns.
Container deployment flow
Build container image.
Store image in Artifact Registry.
Deploy to Cloud Run, GKE, or another runtime.
Configure service account and environment settings.
Set traffic routing, scaling, and networking.
Monitor logs, metrics, and errors.
Common deployment tools
Tool / service
Review point
gcloud CLI
Direct command-line deployment and administration
Cloud Shell
Browser-based shell with Google Cloud tools
Cloud Build
Build and CI automation
Artifact Registry
Store container images and packages
Cloud Deploy
Delivery pipeline support for selected deployment workflows
Terraform / IaC
Repeatable infrastructure provisioning
kubectl
Kubernetes operations against GKE clusters
Notes and examples
Safe release patterns
Pattern
Purpose
Rolling update
Gradually replace old instances/pods
Canary
Send small traffic portion to new version
Blue/green
Switch between two production-ready environments
Traffic splitting
Route percentages across revisions/versions
Rollback
Return to known-good version quickly
Deployment traps
Do not manually patch one VM in a managed instance group and expect it to persist.
Do not store secrets in source code or container images.
Do not grant deployment pipelines broad Owner permissions unless explicitly justified.
If a service cannot pull an image, check Artifact Registry permissions and image path.
If Kubernetes deployment fails, check namespace, image, service account, pod events, and logs.
Security review
Least privilege checklist
Use groups for human access where possible.
Use service accounts for workloads.
Grant roles at the lowest suitable level.
Prefer predefined roles over basic roles.
Use custom roles only when predefined roles are too broad.
Avoid service account keys unless no safer option fits.
Rotate and manage secrets in Secret Manager.
Use Cloud KMS for encryption key management where customer-managed keys are required.
Use audit logs and monitoring for sensitive actions.
Notes and examples
Common security services
Need
Service / feature
Manage secrets
Secret Manager
Manage encryption keys
Cloud KMS
Protect web apps
Cloud Armor
Access apps based on identity
Identity-Aware Proxy
Manage Linux login with IAM
OS Login
Isolate private workloads
VPC, firewall rules, private IPs
Reduce data exfiltration risk
VPC Service Controls where appropriate
Scan/secure artifacts
Artifact-related security features where configured
Security traps
Trap
Better approach
Put credentials in startup scripts
Use service accounts and Secret Manager
Give users service account keys
Prefer impersonation or federation
Make bucket public for temporary sharing
Use signed URLs or scoped IAM when possible
Use Editor for deployment automation
Grant specific deployment and resource roles
Assume encryption requires manual setup
Google Cloud encrypts data by default; use CMEK only when customer-managed control is required
Cost and billing review
ACE cost questions usually test operational awareness, not deep financial modeling.
Cost control tools and behaviors
Need
Review point
Track spend
Billing reports
Notify on spend
Budgets and alerts
Attribute cost
Labels and project structure
Reduce VM cost for predictable usage
Committed use discounts where appropriate
Reduce cost for fault-tolerant workloads
Spot VMs
Avoid idle spend
Stop/delete unused resources
Reduce storage cost
Lifecycle policies and correct storage class
Avoid surprise egress
Understand data movement across regions/internet
Cost traps
Budgets and alerts notify; they do not automatically stop all spending unless you build automation.
Stopped VMs may still incur disk/IP-related charges.
Cold storage can cost more if accessed frequently.
Overprovisioned VMs and unused disks are common waste sources.
Cross-region and internet egress can matter in architecture questions.
Reliability and backup review
Availability concepts
Requirement
Design direction
Survive instance failure
Managed instance group with autohealing
Survive zone failure
Regional managed instance group or regional service design
Durable object storage
Cloud Storage
Block storage replicated across zones
Regional Persistent Disk where appropriate
Database HA
Use managed HA/replication options for the selected database
Disaster recovery
Backups, replicas, tested restore procedures
Global user performance
Global load balancing/CDN patterns where suitable
Backup and restore traps
A snapshot is not the same as a machine image.
Backups are only useful if restore is tested.
High availability is not a substitute for backup.
Replication can copy accidental deletes or bad writes.
Regional/multi-region choices affect resilience, latency, and cost.
High-yield command and console tasks
You do not need to memorize every command, but you should recognize task-oriented command patterns.
Task
What to know
Create VM
project, zone, machine type, image, network, service account
SSH to VM
firewall, IAM/OS Login, external IP or IAP path
Create bucket
globally unique name, location, storage class
Deploy Cloud Run
image, region, service account, ingress/auth settings
Create GKE cluster
mode, region/zone, node pools or Autopilot
Configure IAM
add/remove policy bindings at correct scope
Enable monitoring
metrics, logs, dashboards, alerts
Export logs
create sink and destination permissions
Use Cloud Shell
preconfigured browser shell for administration
Common ACE mistakes to eliminate
Mistake checklist
Choosing the most powerful service instead of the simplest managed fit.
Granting Owner/Editor instead of least-privilege roles.
Forgetting service accounts are identities, not just configuration objects.
Confusing Cloud Storage, Persistent Disk, and Filestore.
Treating BigQuery as an OLTP database.
Assuming private VMs can reach the internet without Cloud NAT or another egress path.
Ignoring firewall rules when troubleshooting connectivity.
Using public access when signed URLs, IAM, IAP, or private networking would be safer.
Choosing Compute Engine when Cloud Run or App Engine would reduce operations.
Choosing serverless when the scenario requires OS-level control.
Ignoring region/zone scope.
Forgetting to enable required APIs.
Assuming monitoring exists without alert policies or uptime checks.
Not checking logs before changing infrastructure.
Rapid scenario drills
Use these prompts to test your decision speed before moving into full original practice questions.
Scenario 1: Private VM needs updates
A VM has no external IP address. It must download operating system updates from the internet.
Best direction: Cloud NAT for outbound internet access, with appropriate routes/firewall behavior. Do not assign a public IP unless the scenario requires inbound public access.
Scenario 2: User needs to deploy using a service account
A developer can create VMs but cannot attach the required service account.
Best direction: Grant the user permission to use that service account, commonly via Service Account User on the service account, while keeping resource permissions least-privilege.
Scenario 3: Static website assets
A team needs to store and serve static objects.
Best direction: Cloud Storage for objects. Consider bucket IAM, public access requirements, signed URLs, lifecycle rules, and caching/CDN depending scenario.
Scenario 4: Stateless container API
A containerized HTTP API must scale down when idle and requires minimal infrastructure management.
Best direction: Cloud Run, with image in Artifact Registry, appropriate service account, ingress/auth configuration, and monitoring.
Scenario 5: Kubernetes-specific platform
The application team requires Kubernetes APIs, custom controllers, and Kubernetes-native deployment workflows.
Best direction: GKE, selecting Autopilot or Standard based on control requirements.
Scenario 6: Analytics over terabytes
A business team wants SQL analysis over very large datasets.
Best direction: BigQuery, not Cloud SQL.
Scenario 7: VM group must self-heal
A web tier runs on Compute Engine and must replace unhealthy instances automatically.
Best direction: Managed instance group with health check, instance template, autoscaling/autohealing as needed, and firewall rules allowing health checks.
Practice focus map
Use this map to guide topic drills in a question bank.
Practice area
What to drill
IAM
Role scope, service accounts, impersonation, least privilege