CCNA Ensuring successful operation of a cloud solution Questions

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Option A is wrong because 'gcloud container clusters describe cluster-name' returns static cluster configuration metadata (e.g., zone, node count, network settings) but does not provide live resource usage metrics. Option C is wrong because the Cloud Console Monitoring page offers historical and aggregated metrics with dashboards, but it is not a direct command-line tool for quick verification; it requires navigating the UI and may have a delay in data ingestion. Option D is wrong because 'kubectl describe node node-name' shows node conditions, capacity, and allocated resources, but it does not show real-time usage; it reports requests and limits, not actual consumption, so it cannot confirm current resource contention.

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Option A is wrong because deleting old records does not release disk space back to the operating system in Cloud SQL PostgreSQL; the space is retained by the database for future writes and does not reduce the provisioned storage size. Option C is wrong because creating a new larger instance and migrating data with Cloud Database Migration Service introduces significant downtime and operational complexity, which is slower and riskier than a simple online storage resize. Option D is wrong because enabling automatic storage increase does not retroactively expand the disk; it only allows future automatic expansions, and the instance is already at 90% usage with no immediate relief.

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Option A is wrong because Cloud Tasks is a task queue/distributed execution service, not a scheduler; you would still need Cloud Scheduler or a separate trigger to enqueue the task daily, adding unnecessary complexity. Option C is wrong because Cloud Composer (Apache Airflow) is a full workflow orchestration platform designed for complex, multi-step pipelines with dependencies, not for a simple timed HTTP call — it introduces heavy overhead and cost. Option D is wrong because Cloud Monitoring alerts are for reacting to metric thresholds or system states, not for scheduling recurring actions; using an alert to trigger a job at a fixed time is an incorrect architectural pattern and would require a custom metric or log-based alert, which is convoluted and unreliable for simple cron-like scheduling.

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Option A is wrong because using `gsutil -m cp` from a Compute Engine VM introduces a single point of failure, requires managing the VM's lifecycle, and the VM's network egress from AWS is still limited by the same 1 Gbps pipe; moreover, the VM adds latency and cost without any throughput advantage over a managed service. Option C is wrong because downloading to a local machine and re-uploading is impractical for 50 TB (local bandwidth is often far lower than 1 Gbps, and the process is manual, error-prone, and violates the 48-hour SLA). Option D is wrong because BigQuery Data Transfer Service is designed for loading data into BigQuery tables, not for moving raw objects into Cloud Storage; it cannot write to a GCS bucket as a destination.

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Option B is wrong because deleting existing Pods disrupts running workloads and does not guarantee that freed resources will be sufficient for pending Pods; it also violates the constraint of not deleting existing Pods. Option C is wrong because changing resource requests to zero bypasses Kubernetes resource guarantees, leading to potential resource starvation and unpredictable scheduling behavior, and it requires modifying Pod specs which is not a fast or safe resolution. Option D is wrong because upgrading the control plane version does not add compute resources; it updates the Kubernetes API server and controller manager but does not affect node capacity or scheduling of pending Pods.

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Option A is wrong because Cloud Monitoring alerting policies cannot directly use billing/cost metrics; billing data is not exposed as a Cloud Monitoring metric, and the 'billing/cost metric' does not exist in the Monitoring API. Option B is wrong because while a Cloud Scheduler job could theoretically query the Billing API and send an email, this is not a built-in GCP feature for budget alerts—it requires custom development, cron management, and is not the recommended or simplest solution. Option D is wrong because Cloud Logging alerts on billing cost log entries are not supported; billing data is not written to Cloud Logging as structured log entries that can trigger alerts, and the Billing budget feature already handles threshold-based notifications natively.

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Option A is wrong because Cloud Scheduler jobs running gsutil rewrite and gsutil rm commands introduce unnecessary complexity, potential for human error, and additional cost for compute resources; lifecycle management handles this natively without custom scripts. Option C is wrong because Cloud Pub/Sub notifications triggering a Cloud Function on each object creation would only fire on new objects, not on existing objects, and would require custom code to implement age-based transitions and deletions, which is less efficient and more error-prone than built-in lifecycle rules. Option D is wrong because retention policies are used to prevent object deletion or modification for a specified period, not to automate transitions or deletions; locking objects in Coldline after 90 days would actually prevent the deletion at 365 days that the requirement specifies.

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Option A is wrong because granting individual permissions like run.services.create and run.services.update is not a predefined IAM role and would require custom role creation, which is not the most straightforward or recommended approach for a new developer. Option B is wrong because 'roles/editor' grants broad project-level permissions (e.g., to modify all resources), which violates the principle of least privilege and is excessive for deploying only Cloud Run services. Option D is wrong because 'roles/cloudrun.admin' grants full administrative control over Cloud Run resources, including deletion and IAM policy changes, which is more than the minimum necessary permissions for deploying services.

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Option A is wrong because there is no 'Custom Metrics feature flag' in Cloud Monitoring settings; custom metrics are enabled by default once you write data via the API, and no toggle is required. Option C is wrong because a log-based metric extracts values from existing log entries, but the question states the metric is emitted via code, not logs — creating a log-based metric would require the application to first write logs, which is an unnecessary extra step and not the direct method for a custom metric. Option D is wrong because custom metrics do not require BigQuery; Cloud Monitoring stores custom metric data natively in its time-series database, and BigQuery integration is optional for long-term analysis, not a prerequisite.

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Option B is wrong because deploying to the wrong region would cause a 'function not found' or routing error, not a permission denied error; the IAM check occurs before regional routing. Option C is wrong because CORS misconfiguration affects browser-based cross-origin requests by blocking HTTP responses, not the underlying IAM authorization; the error message explicitly references a permission denial, not a CORS header issue. Option D is wrong because VPC connector misconfiguration would cause network connectivity failures (e.g., timeouts or unreachable endpoints) but does not affect IAM permission checks; the error is about authorization, not network access.

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Option A is wrong because `kubectl logs POD_NAME` only shows logs from the currently running container instance, not from the previous crashed instance, so it would not show the crash cause. Option C is wrong because `kubectl describe pod POD_NAME` shows pod metadata, status, and events, but does not retrieve container logs; it cannot show the log output from the previous container instance. Option D is wrong because `kubectl get events --field-selector reason=OOMKilled` only filters for Out-Of-Memory kill events, which is too narrow and may miss other crash reasons; it also does not retrieve the actual container logs needed for diagnosis.

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Option A is wrong because taking a snapshot and creating a new instance introduces significant downtime while the snapshot is taken, the new instance is provisioned, and the connection string is updated — violating the 'no downtime' requirement. Option C is wrong because deleting tables is a destructive, time-consuming operation that may not free enough space quickly, and it risks data loss; it also does not address the root cause of insufficient disk capacity. Option D is wrong because switching to SSD storage requires recreating the instance or migrating data, which causes downtime, and SSD does not increase disk capacity — it only improves I/O performance, so it would not resolve the disk-full write failures.

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Option A is wrong because `textPayload` is used for unstructured text logs, not structured JSON; the syntax `textPayload:"user_id:12345"` would search for that literal string in the text payload, not the JSON field. Option C is wrong because `resource.labels` refers to labels on the monitored resource (e.g., Cloud Run service name, revision), not the application's JSON payload fields. Option D is wrong because `labels` in Cloud Logging refer to user-defined metadata labels on the log entry itself, not the structured JSON fields from the application output.

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Option A is wrong because an outdated provider version would typically cause deprecation warnings or missing features, not a specific error about a required argument name. Option B is wrong because an incorrect project ID would result in an error like 'project not found' or 'permission denied', not a missing 'member' argument. Option C is wrong because the 'member' argument can accept users, service accounts, groups, or domains (e.g., 'user:email', 'serviceAccount:sa@project.iam.gserviceaccount.com'); the error is about the argument name, not the value type.

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Option A is wrong because raising memory limits without adjusting requests does not improve scheduling accuracy; limits only cap usage, but the Pod still lacks a guaranteed reservation, so it remains in a lower QoS class and is still evicted first under pressure. Option C is wrong because disabling kubelet eviction (via --eviction-hard or --eviction-soft flags) would allow the node to run out of memory entirely, leading to system OOM kills or node instability, which is not a valid long-term fix. Option D is wrong because adding nodes only distributes the load temporarily; without requests, new Pods will still be placed without guarantees, and the same eviction pattern will recur on any node under memory pressure.

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Option A is wrong because `kubectl describe pods` shows configuration details, events, and status, but not real-time CPU or memory utilization metrics. Option C is wrong because `kubectl get pods -o wide` displays pod IPs and node assignments, not resource utilization data. Option D is wrong because `kubectl logs` retrieves container logs for debugging, not CPU or memory metrics.

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Option A is wrong because `severity >= ERROR` uses an unquoted severity value, which is invalid syntax; severity values must be quoted strings (e.g., `"ERROR"`). Option B is wrong because it restricts results to only `gce_instance` resource type, while the requirement is to retrieve entries across all resource types. Option D is wrong because it filters by a specific log name (`stderr`) and uses `severity > "WARNING"`, which excludes ERROR-level entries (since ERROR is not greater than WARNING in the severity hierarchy; ERROR is higher than WARNING, but the operator `>` is not standard for severity comparison in Cloud Logging, and the query also incorrectly limits to a single log stream).

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Option A is wrong because Cloud DNS inbound server policy is used to forward DNS queries from on-premises networks to Cloud DNS, not to forward queries between VPCs in different projects. Option B is wrong because exporting a private zone as a public zone would expose internal DNS records to the internet, violating security requirements and not providing a secure resolution path for internal instances. Option D is wrong because creating a new Private DNS zone with forwarding to on-premises DNS does not enable resolution of private DNS names hosted in another project; it would only forward queries to an on-premises resolver, not to the target private zone.

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Option A is wrong because writing a Cloud Function that runs daily to list, delete, or move objects introduces unnecessary complexity, potential execution failures, and additional costs; lifecycle rules achieve the same result natively without custom code. Option C is wrong because a bucket-level retention policy prevents object deletion or modification before the retention period ends, which conflicts with the requirement to delete objects older than 30 days, and manually changing storage classes does not automate the process. Option D is wrong because using Cloud Scheduler to trigger gsutil commands is a manual, brittle approach that requires maintaining scripts and handling errors, whereas lifecycle rules are a declarative, serverless feature built into Cloud Storage.

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Option A is wrong because increasing max_connections to 10,000 does not reduce the number of connections; it merely raises the limit, which can lead to memory exhaustion and degraded performance on the Cloud SQL instance, as each connection consumes memory and CPU overhead. Option C is wrong because Cloud SQL HA (high availability) uses a standby instance that does not accept connections for read/write traffic; it only takes over during failover and does not help with connection overflow during normal operations. Option D is wrong because adding a read replica does not reduce the connection count on the primary instance; microservices would still need to connect to the primary for writes, and read replicas have their own connection limits, so the underlying issue of too many connections is not addressed.

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Option A is wrong because GKE does not terminate all nodes simultaneously; the surge configuration explicitly prevents that by keeping one extra node available during the upgrade. Option C is wrong because GKE does not perform in-place kernel patching on running nodes; it replaces nodes with new images via node pool upgrades. Option D is wrong because the surge upgrade does not take two nodes offline at once; only one old node is drained at a time while the surge node handles the workload, and `max-unavailable: 0` means no old node goes offline before the new one is ready.

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Option A is wrong because the Recreate strategy terminates all existing Pods before creating new ones, causing downtime during the transition. Option B is wrong because a blue-green deployment with a Service selector swap is a valid zero-downtime approach, but it requires a separate Deployment and manual or automated traffic switch, not a single Deployment update strategy as specified in the question. Option D is wrong because a Canary deployment with a traffic-splitting ingress is a more advanced pattern that typically uses an Ingress controller (e.g., with weighted routing) to gradually shift traffic, but it is not a native Deployment update strategy in GKE; the question asks for a strategy configured on the Deployment itself.

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Option A is wrong because SSH into the VM and running ping tests live connectivity, but if routing is misconfigured, the ping may fail due to asymmetric routing or firewall rules, and it doesn't isolate whether the issue is routing, VPN tunnel, or firewall — plus, you may not have SSH access or the VM may not have ICMP enabled. Option C is wrong because Cloud Monitoring tunnel metrics (e.g., packet loss, throughput) show tunnel health but cannot analyze the specific path from the VM to the on-premises IP or identify routing misconfigurations. Option D is wrong because `gcloud compute routes list` only shows routes in the VPC, not whether the route is actually being used by the VM or if the on-premises network has a return route; it doesn't test end-to-end connectivity or validate firewall rules.

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