Google Associate Cloud Engineer (ACE) — Questions 601–675

kubectl set image deployment/my-deployment my-app=gcr.io/my-project/my-app:v2 is the correct imperative command to update a container image inside a Deployment. The container name (my-app) must exactly match the container name defined in the Deployment's pod spec, and the command updates the pod template so the Deployment controller creates a new ReplicaSet and performs a rolling update. This is the canonical kubectl syntax for changing an image without editing a manifest.

Cloud Workflows is a fully managed orchestration service that defines steps in a YAML specification, allowing sequential or parallel execution, conditionals (e.g., if/switch), and subworkflows. It provides built-in retry with exponential backoff, timeout policies, and automatic state persistence, so every execution is logged and auditable. Direct connectors for Google Cloud services (like Cloud Run, Cloud Functions, BigQuery) let you call APIs without writing HTTP glue code, making it the correct choice for coordinating dependent multi-step pipelines with conditional logic and centralized error handling.

Dedicated Interconnect provides a direct, private physical connection between an on-premises data center and Google Cloud, typically via a co-location facility with one or more 10 Gbps or 100 Gbps links. This dedicated, high-bandwidth link bypasses the public internet, delivering more consistent latency, higher throughput, and an SLA covering availability (up to 99.99% when configured with redundant connections). It is the correct choice here because it is purpose-built for hybrid cloud connectivity that requires reliable, secure, and dedicated bandwidth between a data center and Google Cloud.

This is the correct drain command because --ignore-daemonsets tells kubectl to skip evicting Pods that are managed by DaemonSets (they would just be recreated on the same node), and --delete-emptydir-data lets the drain proceed even if Pods have emptyDir volumes that will be lost. The drain cordons the node, then gracefully evicts remaining workload Pods while honoring PodDisruptionBudgets, making it safe for planned maintenance. These flags are the standard pair used when a GKE node contains DaemonSets and emptyDir-backed pods.

The USERS column in the console output lists the instances that are currently using the disk as an attached persistent disk. When a disk is attached to an instance, it is in use and cannot be deleted until you either delete the instance or detach the disk first (if the instance is stopped). This is the correct reason the deletion fails in the exhibit.

Committed use discount (1 year) is the correct choice because it aligns with the scenario of running an n2-standard-4 instance consistently over a year. In the Google Cloud Pricing Calculator, selecting a 1-year CUD for compute resources like vCPUs and memory typically yields a discount of approximately 20-30% compared to on-demand pricing. The calculator has a dedicated field to add this commitment, and choosing it directly answers the question by reducing the estimated cost. This is the best option among the list since other discounts either do not apply or are automatically included.

The --source flag tells gcloud to treat the current directory as source code, automatically invoking Cloud Build to containerize it using buildpacks before deploying to Cloud Run in the specified region. This single command combines the build and deploy steps, which is exactly what the requirement of a one-command deployment asks for. The service name 'my-service' is provided, and the region ensures the service is created in us-central1.

gcloud projects add-iam-policy-binding is the precise command to grant an existing service account an IAM role on a project. It performs an additive update to the project's IAM policy, leaving all other bindings intact, which is exactly what is needed when automating service account creation and subsequent access provisioning. The command requires the service account's email as the member and the role name (e.g., roles/storage.objectViewer), and it applies the binding only at the project level, matching the scenario.

This is the correct approach because it leverages Cloud Run's native IAM integration. Disabling unauthenticated invocations makes the service require a valid Google-issued OAuth2/OIDC token for every request. Granting roles/run.invoker to the Cloud Scheduler service account authorizes precisely that identity to invoke the service. When Scheduler is configured with OIDC authentication, it attaches an OIDC token for that service account, which Cloud Run validates against the invoker role—thereby allowing only scheduled calls and blocking all other callers.

Enabling Cloud CDN on a Cloud Storage backend bucket behind a global external load balancer lets video files be cached at Google's edge points of presence (PoPs) around the world. This provides low-latency delivery because users retrieve content from a nearby edge cache rather than from the storage origin, and it offloads egress traffic from Cloud Storage. The global load balancer offers a single anycast IP, so all users are automatically routed to the closest pop, and signed URLs or IAM policies can still enforce access controls. This is the intended solution for globally distributed video delivery.

Setting up VPC Network Peering between VPC-A and the VPC where Cloud SQL is deployed allows private, RFC1918 traffic to flow over Google’s internal backbone without a VPN, NAT gateway, or public IP. Once Cloud SQL is configured with a private IP, its address is allocated from a reserved range in that VPC, so VM instances in the peered VPC-A can connect directly. This satisfies the requirement of avoiding both public exposure and VPN.

A managed instance group (MIG) automatically creates and manages instances from an instance template, and attaching an autoscaler with a CPU utilization target lets the group dynamically add or remove VMs to keep average CPU near the desired threshold. The autoscaler continually polls Cloud Monitoring metrics and can also use load balancing signals, making it appropriate for a Compute Engine web application that needs horizontal scaling. MIGs integrate with Cloud Load Balancing and instance health checks, enabling both scale-out and automatic recovery of unhealthy instances.

Cloud Run is purpose-built for containerized HTTP services with zero-to-scale autoscaling, no infrastructure management, and per-request billing.

Cloud SQL is a fully managed relational database service offering MySQL, PostgreSQL, and SQL Server engines with automated backups, point-in-time recovery, and integrated high availability, making it ideal for OLTP workloads using SQL. It provides strong ACID consistency and a 99.95% SLA at a predictable price point for databases under 10 TB, which fits the stated need without over-provisioning. This combination of managed operations, relational features, and reasonable cost makes Cloud SQL the correct choice.

roles/secretmanager.secretAccessor is the correct and minimal predefined role for service accounts that need to retrieve secret payloads. It includes the `secretmanager.versions.access` permission, which allows the caller to access the encryption-decrypted secret value from a specified version, and `secretmanager.versions.get` for metadata of that version. It does not permit creating, deleting, or modifying secrets, nor changing IAM policies, making it the exact role for a workload that reads a secret at runtime without administrative side effects.

Enabling binary logging is the foundational prerequisite for any MySQL replication topology, including Cloud SQL as an external replica. With binary logging enabled, the on-premises MySQL server records all changes in binary log files, and a replica reads those logs to replay transactions; additionally, the server must set a unique server-id and use row-based binary logging (binlog_format=ROW) for consistency. Without it, the Cloud SQL instance cannot receive the change stream needed to stay synchronized.

Enabling the Compute Engine API requires the project to have a billing account associated with it, because even though Compute Engine offers a free tier, the service's resource management, quotas, and metering are tied to billing to prevent abuse and allow for usage tracking. When a project has no billing account, the Service Usage API returns an error like "Billing account must be linked to project" or "Billing is required for usage", and this occurs regardless of the user's IAM role or authentication state. Associating a billing account at the project level—through the Cloud Console or the gcloud billing projects link command—is the prerequisite that resolves this error.

Choosing a time aggregation function is essential because raw metric samples arrive at irregular intervals and need to be aligned to a fixed time step for a coherent time series. The aggregation function (e.g., mean, max, sum) reduces multiple points within each alignment window into a single value, which determines the chart's shape and sensitivity to spikes. Without this step, the dashboard may render unusable, too-dense data or fail to produce a meaningful trend. For CPU utilization, 'mean' is typical for overall usage, while 'max' can highlight peak behavior.

Cloud Pub/Sub is the fully managed, natively available messaging service on Google Cloud, providing durable, asynchronous message delivery between independently scaled publishers and subscribers. It supports global topic routing, push/pull subscriptions, at-least-once delivery with configurable ack deadlines, and automatic horizontal scaling to millions of messages per second without any broker infrastructure to patch or monitor. As the direct GCP equivalent to self-managed Kafka, it removes operational overhead while adding native IAM integration and dead-letter topics for failure handling.

Cloud Storage is the correct choice because it is a durable, highly available object storage service with a global namespace and public HTTPS ingestion and serving endpoints. It provides strong consistency, configurable permissions, and can integrate with Cloud CDN for low-latency delivery of cached content, making it ideal for user-generated photos, videos, and documents. Lifecycle policies can cost-effectively archive older content, and resumable uploads support large files from mobile and web clients.

Cloud Logging is the correct choice because it is the native, centralized log storage and retrieval service for Google Cloud, and Cloud Run automatically sends both request logs and platform logs to it. Application output written to stdout/stderr is also captured as structured logs. You can view these logs immediately in the Logs Explorer and filter them by resource type, severity, or labels.

Applying organization policies at the folder level is correct because constraints are inherited by all projects and subfolders under that folder. For example, the compute.skipDefaultNetworkCreation constraint can be set at a folder to prevent any project inside it from creating the default VPC network at project creation time. This ensures consistent, centrally managed governance across an entire team or environment without needing to configure each project individually.

This is the correct approach because GKE logs Kubernetes events, including CronJob failures (such as a Job exceeding its deadline or failing to be created), to Cloud Logging when cluster logging is enabled. A log-based metric can count only the error-level or failure-related log entries generated by the CronJob controller, and a Cloud Monitoring alerting policy can then respond to that metric crossing a threshold, sending notifications via email, Pub/Sub, or mobile app. Unlike static limits or cluster-wide notifications, this gives you a targeted, rule-based way to detect exactly when a scheduled job fails, making it the standard solution for CronJob observability on GKE.

HTTP load balancing serving capacity, measured as requests per second per instance, directly reflects the real ingress rate seen by the load balancer. This metric is proactive because it reacts to request count in real time, allowing the managed instance group to scale out before CPU or memory saturation causes latency spikes. Since it is measured at the load balancer, it captures all client traffic destined for the web tier, making it the most accurate and responsive signal for autoscaling stateless HTTP web workloads.

GCS object notifications to Pub/Sub trigger a Cloud Function that launches a Dataflow job. Dataflow handles files up to 5 GB within 10 minutes using parallel workers, and the event-driven architecture processes files exactly when they land.

Secret Manager is the correct choice because it is Google Cloud's purpose-built service for storing sensitive data such as database passwords, API keys, and certificates. It provides fine-grained IAM policies, automatic versioning, audit logging of secret access, and integration with services like Cloud Functions and GKE. Additionally, it supports secret rotation and allows you to enforce retention policies, making it the secure and native solution for managing a database password.

Cloud IAM supports granting a role to any Google account, and a Gmail address is a valid principal identifier. The Viewer role (roles/viewer) is a predefined IAM role that encompasses read-only permissions across all services in the project, such as listing compute instances, viewing Cloud Storage buckets, and reading configurations. By assigning this role to auditor@gmail.com in the project IAM policy, the auditor gets exactly the read-only visibility needed without any write or management capabilities.

Every project that uses paid GCP services must have a billing account linked. Without it, resource creation for paid services will fail.

Cloud Spanner is Google's globally distributed relational database that combines horizontal scalability with full ACID transactions and strong consistency. It uses the TrueTime API with atomic clocks and GPS to order transactions consistently across regions, delivering external consistency. This makes it the only option here that meets both strong consistency and global distribution, so it is the correct choice.

Enabling Cloud CDN on the load balancer backend for the Cloud Storage bucket is the correct approach because it caches static assets at Google's global edge points of presence (PoPs). When an Asian user requests a static file, the CDN serves it from the nearest PoP, avoiding a slow round trip to the us-central1 bucket. This directly reduces latency, lowers origin load, and minimizes egress costs, and it is the purpose-built, scalable solution for static content delivery.

`-target=google_sql_database_instance.main` is the correct way to scope a Terraform apply to a single resource. The `-target` flag accepts a resource address in `TYPE.NAME` form (here `google_sql_database_instance.main`) and will apply that resource plus only the resources it directly or indirectly depends on. This creates the Cloud SQL instance without applying the other 14 unrelated resources in the plan. It is the standard targeted-apply pattern for isolating a specific infrastructure change.

gcloud projects create PROJECT_ID is the correct command because it sends a create request to the Cloud Resource Manager API, which provisions a new project with the specified ID. This command requires the resourcemanager.projects.create permission and the PROJECT_ID must be globally unique across all Google Cloud projects. Once created, the project can be used for development and managed via gcloud.

`gcloud auth application-default revoke` is the dedicated command that both deletes the local ADC file and sends a revocation request to Google so the underlying OAuth token is invalidated server-side. This ensures that any client library already holding the token can no longer use it, which is the only fully secure way to revoke ADC.

Setting `min-instances: 1` prevents Cloud Run from scaling the service to zero instances, so at least one warm container instance remains available to handle incoming requests at all times. The first request after a period of inactivity reaches an already-initialized instance, eliminating the container startup (image pull, runtime boot, and application init) latency that causes a cold start. This incurs idle-instance billing, but for a low-traffic, latency-sensitive service it is the correct cost-versus-performance trade-off.

Data Access audit logs are the only Cloud Audit Logs category that records the actual 'read' operations on user data. When enabled, they capture BigQuery jobs such as SELECT queries, table reads, and tabledata.list calls, including the identity of the caller, the queried dataset/table, and the timestamp. These logs are opt-in because they can generate enormous volume, but they are essential for security forensics to determine who accessed specific BigQuery data.

This is the standard way to create a regional GKE cluster. The --region flag designates a regional cluster where the control plane is replicated across three zones in that region, and nodes are spread across those zones (3 nodes per zone by default with --num-nodes 3). This provides higher availability and is exactly what the engineer needs.

This is the only command that fully adheres to gcloud syntax: it uses --image-family debian-11 with --image-project debian-cloud to fetch the latest image from the public Debian project, --boot-disk-size 50GB to define a 50-gigabyte root disk with an explicit unit, and --subnet my-subnet to place the instance in the desired VPC subnet. The combination of these flags creates the instance successfully without requiring additional prompts or defaults. This is the recommended pattern for creating a VM from a public image family.

This is the correct command for enabling autoscaling on a regional managed instance group. The set-autoscaling verb properly applies the autoscaling policy, and the --region us-central1 flag matches the regional scope of the MIG. The --max-num-replicas 10 sets the upper limit, while --target-cpu-utilization 0.6 correctly specifies a 60% CPU utilization target as a decimal fraction.

Cloud KMS customer-managed encryption keys (CMEK) give the organization direct control over the key material used to encrypt data at rest, including the ability to enable, disable, rotate, and set IAM policies on the key. With CMEK, you create the key in Cloud KMS and then specify that key as the encryption key for the batch job's storage or compute resource, ensuring that no one outside your organization's IAM policy can access the decryption key. This satisfies compliance requirements for customer-managed keys while leveraging Google's managed encryption infrastructure.

App Engine's traffic splitting feature lets you specify a percentage of traffic to route to each version of a service, using either a cookie-based or IP-based (or random) split. You can start by sending, say, 5% of requests to the new version, monitor error rates and latency, then gradually increase that percentage while keeping the old version available for instant rollback.

Instrument the application with a client library such as google-cloud-monitoring or an OpenTelemetry exporter for Google Cloud, define a MetricDescriptor for your metric type under custom.googleapis.com/, and then call projects.timeSeries.create to push observations. After valid time series data is written, Cloud Monitoring automatically makes the metric available for charting in Metrics Explorer and for use in alerting policies. This is the standard, direct path for application-level custom metrics like orders per minute.

This role is scoped specifically to compute instances. It allows creating, deleting, starting, stopping, and modifying instances, but not managing networks, subnets, firewalls, or other global Compute Engine resources. This is the correct least-privilege choice because it covers all instance lifecycle operations without granting access to the broader infrastructure.

This is the correct command. 'gcloud sql import sql' explicitly tells the Cloud SQL API that the source file is a SQL dump (typically generated by mysqldump or pg_dump). The arguments specify the instance name, the Cloud Storage URI of the dump file, and the target database using the --database flag. This syntax works for both MySQL and PostgreSQL instances and is the standard way to import SQL dump files.

Google Cloud's HTTP health check issues a GET request to the exact requestPath configured, and the instance is considered healthy only if it responds with a 2xx or 3xx status code. Since this application does not expose a /healthz endpoint, the server returns a 404 Not Found, which the health checker treats as a failure. To fix this, the application should serve a 200 OK at /healthz, or the requestPath should be changed to match an existing endpoint, such as / or /index.html.

Using the `--service-account` flag with `gcloud run deploy` explicitly assigns the specified IAM service account as the runtime identity for the Cloud Run service. This is the correct syntax because the flag is designed to set the service account that the container will run as, and `--platform managed` ensures the command targets Cloud Run (fully managed) rather than other platforms. The deployed service will inherit the IAM permissions of `my-sa@my-project.iam.gserviceaccount.com`, which is exactly what the DevOps engineer needs.

Running `gcloud config set project new-project` updates the `core/project` property in the active configuration, making new-project the default project for subsequent gcloud commands. Following that with `gcloud config list` displays the configuration properties, including the newly set project value, providing immediate verification. This is the clean, targeted approach because it directly changes the persistent configuration and uses the built-in command for project selection.

Cloud Identity-Aware Proxy (IAP) placed in front of the load balancer enforces zero-trust access by intercepting every request, forcing employees to authenticate with a Google account, and then checking their IAM roles or groups before authorizing forwarding to the GKE backend. This occurs entirely at the infrastructure layer, so unauthenticated and unauthorized requests are rejected before reaching the application, with no code changes required. IAP provides the exact browser-based identity authentication needed for this web app.

Updating the service with a new image tag, for example via `gcloud run deploy`, is what creates a new revision. A revision cannot be manually created in isolation—it is always the result of deploying a new container image or configuration change. This step is a prerequisite because the later `update-traffic` command must reference the new revision's name to assign it a percentage of incoming requests.

Stopping the instance first moves it to the TERMINATED state, where the underlying vCPU/memory allocation can be changed. The `gcloud compute instances set-machine-type` command only works on a stopped instance, so stopping, changing, then starting is the documented, supported path. This preserves the boot disk, persistent disks, static IP, and all instance metadata.

Cloud Run's default request timeout is 60 seconds, and requests that exceed it are terminated, causing the client to see a timeout error. For requests that legitimately need up to 3 minutes, you must explicitly increase the timeout to at least 180 seconds (the maximum allowed is 3600 seconds). You can set this in the Cloud Run console or via the `--timeout` flag when deploying, which directly extends the per-request handling time to cover the full duration needed.

A managed instance group health check has a configurable timeout that defines how long the probe waits for a response before marking the instance unhealthy. After the change, the application's /health endpoint consistently returns 200 but only after ~1.5 seconds, which exceeds the 1-second timeout. This creates false negatives: the instance is healthy, but the probe gives up too early and the instance is removed from load balancing. Increasing the timeout (or reducing endpoint latency) resolves the issue.

Cloud Functions enforces IAM authorization at the time of invocation: the caller's identity (a user or service account) must hold the roles/cloudfunctions.invoker role on the function. If that binding is missing, the Cloud Functions API responds with HTTP 403 Permission Denied even though the function exists and is healthy. This is the standard failure when a service account is used for authentication without explicitly granting the invoker role.

VPC Service Controls access levels allow you to define context-based conditions, such as allowed IP ranges, under which requests are permitted. If the requester's external IP address falls within a range listed in the access level, the perimeter will not block the request even though it originates outside the VPC network. Thus, the configuration of an IP-based access level explains why the sensitive data was accessible.

gcloud services enable compute.googleapis.com is the correct command to enable the Compute Engine API in the current project. It interacts with the Service Usage API to set the service's enabled state for the active project. After running this command, you can start using Compute Engine features, assuming a billing account is linked and the IAM permission serviceusage.services.enable is granted. The full service name compute.googleapis.com uniquely identifies the API.

Adding `nodeSelector: cloud.google.com/gke-spot: 'true'` is the precise way to ensure the Simulation pod lands on a Spot node. When a node pool is created with Spot VM settings, Google Kubernetes Engine automatically labels every node with `cloud.google.com/gke-spot=true`. A nodeSelector constrains the pod's scheduling to nodes that carry that exact label, which is a hard constraint during the scheduling decision. This directly targets the Spot pool without relying on ambiguous affinity logic or naming conventions.

When you bind a Cloud Run service to a Cloud SQL instance by its connection name, the platform automatically injects and runs the Cloud SQL Auth Proxy as a sidecar container. The proxy connects to the database over an encrypted channel using either a private IP or a Unix socket, and it leverages IAM permissions to authorize the connection. This pattern avoids static IP management, network whitelisting, and manual secret handling, making it the officially recommended integration.

The `--allow-unauthenticated` flag is the correct way to enable public HTTPS access to a Cloud Run service. When set at deploy time, Cloud Run binds the IAM role `roles/run.invoker` to the special `allUsers` principal, allowing any client to invoke the service without providing credentials. This directly satisfies the requirement of making the containerized app reachable from the internet.

Global HTTP(S) Load Balancing is a correct choice because it uses a single anycast IP to route user traffic to the closest healthy backend across multiple Google Cloud regions. It automatically detects and drains unhealthy backends, supports autoscaling, and provides layer 7 content-based routing. This service delivers the required high availability for the web application's compute and network layer.

StatefulSet is purpose-built for stateful applications: each pod gets a stable, ordinal identity (e.g., web-0, web-1) and a matching PersistentVolumeClaim provisioned from the volumeClaimTemplates block. When a pod is rescheduled, the StatefulSet controller reattaches the same PVC to the same pod name, preserving the data and ensuring consistent storage across restarts. This combination of stable identity, ordered deployment, and per-pod persistent storage is exactly what a stateful service on Google Kubernetes Engine needs.

In Google Cloud VPC firewall rules, priority values determine evaluation order; lower numbers are evaluated first. The deny rule has a priority of 500, whereas the allow rule has 1000, so the deny rule is matched first. Because GCP applies the first matching rule and then stops, the traffic is denied before the allow rule is ever considered.

The `gcs` backend is the official Google Cloud Terraform backend: it stores the state file in a Cloud Storage bucket and supports state locking through the coordinated creation of a lock object using GCS object generation and precondition checks. This lock is released after the operation, preventing concurrent runs from corrupting state. It also supports bucket versioning to retain history and can use customer-managed encryption keys (CMEK) for at-rest protection, making it the correct choice for team collaboration.

Using a gcloud script to iterate over all projects is a recommended, scalable, and auditable approach. A script can call `gcloud projects list` to enumerate the project IDs and then execute `gcloud services enable <api> --project=<id>` for each one, making the operation idempotent and repeatable. This can be filtered to target only the relevant 150 projects and easily integrated into CI/CD pipelines or run as a one-time maintenance task. It is preferable because it eliminates the risk of human error, provides a clear command log, and requires minimal effort even for hundreds of projects.

The `--previous` flag instructs kubectl to fetch the log output from the last terminated container instance in the pod. This is exactly what's needed because when a container enters a crash loop, the current instance may be freshly restarted and have no logs, while the previous instance contains the error messages, stack traces, or panic output that caused the termination. It works for both single- and multi-container pods, though for multi-container pods you must also use `-c <container-name>` to identify the correct container.

This is the correct approach: Data Access audit logs for Cloud Storage are enabled by adding a Cloud Storage audit config at the project, folder, or organization level using the IAM 'Audit Logs' tab or by setting an auditConfig in the IAM policy. You can specify the service storage.googleapis.com and include the desired permission types—ADMIN_READ, DATA_READ, DATA_WRITE—or use ALL. After the audit config is applied, Cloud Logging begins recording data access events on the matching buckets automatically, with no further per-bucket steps needed.

Cloud Shell provisions an ephemeral VM but attaches a persistent 5 GB home directory for each user. gcloud configurations are stored as files under ~/.config/gcloud, so whenever the developer runs `gcloud config set project`, the value is written to disk and remains available in future sessions. When the session ends, any new VM in a future session mounts the same home directory, preserving the settings automatically.

This is the correct command because --trigger-topic my-topic binds the function to Pub/Sub, causing it to be invoked every time a message is published to that topic. The --entry-point main identifies the Python callable in the code, while --runtime python39 specifies the runtime. It correctly deploys a background Cloud Function without exposing an HTTP endpoint.

Enabling the --enable-private-ip-google-access flag on the subnet is the core requirement because it tells Google Cloud to allow instances in that subnet to reach Google APIs using only their internal IP addresses. After this flag is set, the VPC's routing table automatically includes a route for Google's API ranges that sends traffic over the internal Google network instead of the public internet. This flag must be present on every subnet where you want Private Google Access to work; enabling it on the VPC or a single instance is not possible.

Cloud SQL supports increasing the configured disk size without restarting the instance, either through the Cloud Console or the `gcloud sql instances patch INSTANCE --storage-size=NEW_SIZE` command. This online operation allocates additional persistent storage while the instance continues serving traffic, directly resolving the disk-full condition and satisfying the no-downtime requirement. Remember that storage size can only be increased, not decreased, so choose the new size based on projected growth.

The Cloud Shell is in a different project. Cloud Shell operates using the project configured in its gcloud settings, which is often the 'Cloud Shell' project or the last project set via `gcloud config set project`, and this may differ from the project you are viewing in the Google Cloud Console. When you run `gcloud compute instances list` in Cloud Shell, it queries only the currently configured project, not the Console-selected project. Thus, if the VM exists in the Console project but not in the Cloud Shell's configured project, the list will be empty.

This command correctly retrieves the IAM policy for the specified GCP project, including all role bindings for members at the project level. The --format json flag ensures the output is machine-readable JSON, which is useful for automation or programmatic inspection. It is the standard gcloud command for viewing project-level IAM policies.

This is the correct operational behavior. After rotating the key version in Cloud KMS, you must restart the Cloud SQL instance using the console, gcloud command, or API so that it recognizes the new key version as the encryption key for upcoming writes. The restart is required because the instance caches the old key version in memory, and the new version is only picked up during instance startup. This allows existing data to still be decrypted with the prior version while new encrypted data uses the updated version.

This is the correct filter because Cloud Run automatically converts JSON-formatted lines written to stdout/stderr into structured LogEntry objects whose contents populate jsonPayload. Using jsonPayload.user_id="12345" directly compares the value of the user_id field inside that JSON object, giving a precise, indexed lookup. It follows Cloud Logging's filter syntax and avoids text scanning or guessing at metadata.