Free Practice Questions for Google Associate Cloud Engineer Certification

Considerations include:

- Creating a resource hierarchy - Applying organizational policies to the resource hierarchy - Granting members Identity and Access Management (IAM) roles within a project - Managing users and groups in Cloud Identity (manually and automated) - Enabling APIs within projects - Provisioning and setting up products in Google Cloud Observability - Assessing quotas and requesting increases - Setting up standalone organizations - Setting up cloud networking - Verifying product availability across geographical locations (e.g., regions, zones) - Configuring Cloud Asset Inventory and using Gemini Cloud Assist to analyze resources - Configuring Workforce Identity Federation

Considerations include:

- Creating one or more billing accounts - Linking projects to a billing account - Establishing billing budgets and alerts - Setting up billing exports

Considerations include:

- Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Google Kubernetes Engine [GKE], Cloud Run, Cloud Run functions, Agent Runtime on Gemini Enterprise Agent Platform [formerly Vertex AI Agent Engine]) - Launching a compute instance (e.g., availability policy, SSH keys) - Choosing the appropriate storage for Compute Engine (e.g., zonal Persistent Disk, regional Persistent Disk, Google Cloud Hyperdisk) - Creating an autoscaled managed instance group by using an instance template - Configuring OS Login - Configuring VM Manager - Using Spot VM instances and custom machine types - Installing and configuring the command-line interface (CLI) for Kubernetes (kubectl) - Deploying a GKE cluster with different configurations (e.g., GKE Autopilot, regional clusters, private clusters) - Deploying a containerized application to GKE - Deploying an application to serverless compute platforms, including for the processing of Google Cloud events (e.g., Pub/Sub events, Cloud Storage object change notification events, Eventarc) - Identifying whether to use GPUs or TPUs

Considerations include:

- Choosing and deploying data products (e.g., Cloud SQL, BigQuery, Firestore, Spanner, Bigtable, AlloyDB, Dataflow, Pub/Sub, Google Cloud Managed Service for Apache Kafka, Memorystore) - Choosing and deploying storage products (e.g., Cloud Storage, Filestore, Google Cloud NetApp Volumes, Google Cloud Managed Lustre) and Cloud Storage options (e.g., Standard, Nearline, Coldline, Archive) - Loading data (e.g., command-line upload, load data from Cloud Storage, Storage Transfer Service) - Maintaining multi-region redundancy across data solutions

Considerations include:

- Creating a VPC with subnets (e.g., custom mode VPC, Shared VPC, VPC Network Peering) - Creating and applying VPC firewalls rules and Cloud Next Generation Firewall (Cloud NGFW) policies with ingress and egress rules and attributes (e.g., action, source, destination, targets, protocols, ports) - Using Tags (e.g., secure Tags) and service accounts in Cloud NGFW policy rules - Establishing network connectivity (e.g., Cloud VPN, VPC Network Peering, Cloud Interconnect) - Choosing and deploying load balancers - Differentiating Network Service Tiers

Considerations include:

- Infrastructure as Code tooling (e.g., Fabric FAST, Config Connector, Terraform, Helm) - AI-assisted planning and implementation (e.g., Gemini CLI, Google Antigravity, Gemini Cloud Assist, Application Design Center)

Considerations include:

- Remotely connecting to a Compute Engine instance - Viewing current running Compute Engine instances - Working with snapshots and images (e.g., create, view, and delete images or snapshots; schedule a snapshot) - Viewing current running GKE cluster inventory (e.g., nodes, Pods, Services) - Configuring GKE to access Artifact Registry - Working with GKE node pools (e.g., add, edit, or remove a node pool; autoscaling node pool) - Working with Kubernetes resources (e.g., Pods, Services, StatefulSets) - Managing horizontal and vertical Pod autoscaling configurations - Managing GKE Autopilot Pod resource requests - Deploying new versions of a Cloud Run application - Adjusting application traffic splitting parameters (e.g., Cloud Run, Cloud Run functions, GKE) - Configuring autoscaling for a Cloud Run application - Attaching GPUs and TPUs - Deploying an agent to Agent Runtime on Gemini Enterprise Agent Platform (formerly Vertex AI Agent Engine) - Managing notebooks in Gemini Enterprise Agent Platform Workbench (formerly Vertex AI Workbench) and BigQuery - Managing developer environments (e.g., Cloud Workstations)

Considerations include:

- Managing and securing objects in Cloud Storage buckets - Setting object lifecycle management policies for Cloud Storage buckets - Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Bigtable, Spanner, Firestore, AlloyDB) - Estimating costs of data storage resources - Backing up and restoring database instances (e.g., Cloud SQL, Firestore, Spanner, AlloyDB, Bigtable) - Reviewing job status (e.g., Dataflow, BigQuery) - Using Database Center to manage the Google Cloud database fleet - Configuring customer-managed encryption keys (CMEK)

Considerations include:

- Resizing a subnet’s IPv4 address range - Reserving static external or internal IP addresses - Adding custom static routes in a VPC - Using Cloud DNS and Cloud NAT - Managing VPC firewall rules and Cloud NGFW policies

Considerations include:

- Creating Cloud Monitoring alerts based on resource metrics - Creating and ingesting Cloud Monitoring custom metrics (e.g., from applications or logs) - Configuring audit logs (e.g., VPC Flow Logs, audit logs, firewall logs) - Exporting logs to external systems (e.g., on-premises, BigQuery) - Configuring log buckets, log analytics, and log routers - Viewing and filtering logs in Cloud Logging - Viewing specific log message details in Cloud Logging - Using cloud diagnostic tools (e.g., Cloud Trace, Cloud Profiler, Query Insights, index advisor) to investigate an application issue - Viewing the Personalized Service Health dashboard - Configuring and deploying Ops Agent - Deploying Google Cloud Managed Service for Prometheus - Using Gemini Cloud Assist for Cloud Monitoring - Using Active Assist to optimize resource utilization - Using Cloud Hub to monitor active events and application health data

Considerations include:

- Viewing and creating IAM policies - Attaching roles and policy inheritance in the Organization hierarchy - Managing the various role types and defining custom IAM roles

Considerations include:

- Creating service accounts, including Google-managed service accounts - Using service accounts in IAM policies with minimum permissions - Assigning service accounts to resources - Managing IAM permissions of a service account - Managing service account impersonation - Creating and managing short-lived service account credentials - Using a Google Cloud service account with a GKE application - Provisioning Workload Identity Federation