Google GCP Solutions Architect Certification Exam Dump and Braindump

Company background. Pixel Horizon Studios builds session based multiplayer mobile games and has historically leased physical servers from several cloud providers. Sudden popularity spikes have made it hard to scale their audience, application tier, MySQL databases, and analytics, and they currently write gameplay metrics to files then push them through an ETL process into a central MySQL reporting database. Solution concept. They will launch a new title on Google Compute Engine so they can ingest streaming telemetry, run heavy analytics, take advantage of autoscaling, and integrate with a managed NoSQL database. Business goals include expanding globally, improving availability because downtime loses players, increasing cloud efficiency, and reducing latency for users worldwide. Technical requirements for the game backend include dynamic scaling based on player activity, connectivity to a managed NoSQL service, and the ability to run a customized Linux distribution. Technical requirements for analytics include elastic scaling, real time processing from game servers, handling late arriving mobile events, supporting SQL queries over at least 12 TB of historical data, ingesting files uploaded by devices on a regular basis, and using only fully managed services. The CEO reports that the last hit game failed to scale and damaged their reputation. The CTO wants to replace MySQL while adopting autoscaling and low latency load balancing and to avoid server maintenance. The CFO needs richer demographic and usage KPIs to improve targeted campaigns and in app sales. They ask you to define a new testing approach for this platform. How should test coverage change compared to their older backends on prior providers?

• ❏ A. Drop unit testing and rely solely on full end to end tests

• ❏ B. Depend only on Cloud Monitoring uptime checks for validation

• ❏ C. Test coverage must scale far beyond earlier backends to validate behavior during global traffic spikes

• ❏ D. Introduce testing only after features are released to production

How can you centrally enforce that most Compute Engine VMs have no external IPs across all current and future projects while allowing only an approved set of instances to keep external connectivity?

• ❏ A. VPC Service Controls perimeters

• ❏ B. Organization Policy compute.vmExternalIpAccess allowlist

• ❏ C. Hierarchical firewall policy with egress deny and tag exceptions

PixelForge Entertainment migrated to Google Cloud and is launching a cross-platform retro arena shooter whose backend will run on Google Kubernetes Engine. They will deploy identical Kubernetes clusters in three Google Cloud regions and require one global entry point that sends each player to the closest healthy region while allowing rapid scaling and low latency. You must design the external ingress to meet these business and technical goals and keep the platform ready for migrating older titles later. What should you implement?

• ❏ A. Create a global external HTTP(S) Load Balancer in front of a single multi-zonal GKE cluster

• ❏ B. Use Traffic Director with proxyless gRPC to steer requests between regional services

• ❏ C. Configure GKE Multi-Cluster Ingress with the global external HTTP(S) Load Balancer across the regional clusters

• ❏ D. Create a global external HTTP(S) Load Balancer backed by a managed instance group on Compute Engine

How should you expose v1 and a beta v2 of a REST API under the same DNS name and TLS certificate for 30 days while keeping separate backends on Google Cloud?

• ❏ A. Provision two external HTTPS load balancers and migrate with DNS later

• ❏ B. External HTTPS Load Balancer with one certificate and a path-based URL map

• ❏ C. Traffic Director

• ❏ D. Cloud DNS weighted round robin

Riverside Outfitters uses Google Cloud with an Organization that contains two folders named Ledger and Storefront. Members of the [email protected] Google Group currently hold the Project Owner role at the Organization level. You need to stop this group from creating resources in projects that belong to the Ledger folder while still allowing them to fully manage resources in projects under the Storefront folder. What change should you make to enforce this requirement?

• ❏ A. Assign the group the Project Viewer role on the Ledger folder and the Project Owner role on the Storefront folder

• ❏ B. Assign the group only the Project Viewer role on the Ledger folder

• ❏ C. Grant the group the Project Owner role on the Storefront folder and remove its Project Owner role at the Organization level

• ❏ D. Move the Ledger folder into a separate Organization and keep the current group role assignments unchanged

Which managed Google Cloud service should you use as the primary store to ingest high volume time series data with very low latency and serve recent records by device key and time range?

• ❏ A. Cloud Spanner

• ❏ B. Google Cloud Bigtable

• ❏ C. BigQuery

• ❏ D. Firestore

Riverview Analytics is preparing a major release and uses a managed instance group as the backend for an external HTTP(S) load balancer. None of the virtual machines have public IP addresses and the group keeps recreating instances roughly every 90 seconds. You need to ensure the backend configuration is correct so the instances remain stable and the service can receive traffic. What should you configure?

• ❏ A. Assign a public IP to each VM and open a firewall rule so the load balancer can reach the instance public addresses

• ❏ B. Add a firewall rule that allows client HTTP and HTTPS traffic to the load balancer frontend

• ❏ C. Create a firewall rule that permits load balancer health check probes to access the instance group on the health check port

• ❏ D. Configure Cloud NAT for the subnet so instances without public IPs can reach the internet

Which Google Cloud connectivity should you use to provide private connectivity that avoids the public internet and meets strict availability and compliance needs for critical workloads when upgrading from Partner Interconnect and Cloud VPN?

• ❏ A. Direct Peering

• ❏ B. Use Dedicated Interconnect

• ❏ C. Increase Partner Interconnect capacity

• ❏ D. HA VPN

Rivertown Analytics keeps regulated customer records in a Cloud Storage bucket and runs batch transformations on the files with Dataproc. The security team requires that the encryption key be rotated every 90 days and they want a solution that aligns with Google guidance and keeps operations simple for the data pipeline. What should you implement to rotate the key for the bucket that stores the sensitive files while preserving secure access for the Dataproc jobs?

• ❏ A. Use Secret Manager to store and rotate an AES-256 key then encrypt each object before uploading to Cloud Storage

• ❏ B. Generate and use a customer supplied encryption key for the bucket and pass the key with every object upload and download

• ❏ C. Create a key in Cloud Key Management Service and set it as the default encryption key on the Cloud Storage bucket then grant the Dataproc service account permission to use that key

• ❏ D. Call the Cloud KMS encrypt API for each file before upload and manage ciphertext and re encryption during rotations yourself

Which Cloud Storage lifecycle configuration minimizes cost by tiering objects older than 60 days to a colder class and deleting objects after 18 months while preserving audit access?

• ❏ A. Enable Autoclass with an 18 month bucket retention policy

• ❏ B. Lifecycle rules to move at 60 days to Coldline and delete after 18 months

• ❏ C. Lifecycle rules to move at 90 days to Nearline then at 180 days to Coldline

All GCP questions come from my Google Cloud Architect Udemy course and certificationexams.pro

BrightBay Media keeps 32 GB of security audit logs on an on premises NAS and plans to move them into a new Cloud Storage bucket. The compliance team requires that uploads use customer supplied encryption keys so that the data is encrypted at rest with your own keys. What should you do?

• ❏ A. Create a bucket with a default Cloud KMS key and copy the files using Storage Transfer Service

• ❏ B. Add the base64 encoded customer supplied key to the gsutil .boto configuration and upload with gsutil

• ❏ C. Run gsutil cp and pass the key using the –encryption-key flag

• ❏ D. Set an encryption key in gcloud config and then copy the files with gsutil

Firewall Insights shows no rows for VPC firewall rules in a shared VPC. What should you enable to produce log entries for analysis?

• ❏ A. Enable Packet Mirroring in the VPC

• ❏ B. Turn on Firewall Rules Logging for the relevant rules

• ❏ C. Enable Data Access audit logs for Compute Engine

• ❏ D. Enable VPC Flow Logs on the subnets

Harborline Freight operates a production web portal on Google Cloud and stores sensitive customer data in Cloud SQL. The compliance team requires that the database be encrypted while stored on disk and they want the simplest approach that does not require changing the application or managing encryption keys. What should you do?

• ❏ A. Enable TLS for connections between the application and Cloud SQL

• ❏ B. Configure Cloud KMS customer managed keys for the Cloud SQL instance

• ❏ C. Rely on Cloud SQL default encryption at rest

• ❏ D. Implement client side encryption in the application before writing to Cloud SQL

A new App Engine standard release increased latency. How should you quickly restore user experience and investigate the regression safely?

• ❏ A. Use App Engine traffic splitting to shift 90% of traffic to the previous version and investigate with Cloud Logging

• ❏ B. Increase App Engine instance class and raise autoscaling limits

• ❏ C. Roll back to the stable version, then use a staging project with Cloud Logging and Cloud Trace to diagnose latency

• ❏ D. Roll back to the previous version, then redeploy the updated build during a low traffic window at 3 AM and troubleshoot in production with Cloud Logging and Cloud Trace

Riverton Insights plans to move about eight petabytes of historical analytics data into Google Cloud and the data must be available around the clock. The analysts insist on using a familiar SQL interface for querying. How should the data be stored to make analysis as simple as possible?

• ❏ A. Migrate the data into Cloud SQL for PostgreSQL

• ❏ B. Load the dataset into BigQuery tables

• ❏ C. Keep the files in Cloud Storage and query them using BigQuery external tables

• ❏ D. Write the data to Cloud Bigtable

How should you quickly and reliably upload large batches of files from a Compute Engine staging directory to Cloud Storage within 10 minutes without changing the ETL tool?

• ❏ A. Use gcsfuse and write files directly to the bucket

• ❏ B. Use gsutil to move files sequentially

• ❏ C. Use gsutil with parallel copy

• ❏ D. Storage Transfer Service scheduled job

Orchid Publishing operates about 420 virtual machines in its on premises data center and wants to follow Google best practices to move these workloads to Google Cloud using a lift and shift approach with only minor automatic adjustments while keeping effort low. What should the team do?

• ❏ A. Create boot disk images for each VM, archive them to Cloud Storage, and manually import them to build Compute Engine instances

• ❏ B. Use Migrate for Compute Engine with one runbook and one job that moves all VMs in a single event across the environment

• ❏ C. Assess dependencies and use Migrate for Compute Engine to create waves, then prepare a runbook and a job for each wave and migrate the VMs in that wave together

• ❏ D. Install VMware or Hyper-V replication agents on every source VM to copy disks to Google Cloud and then clone them into Compute Engine instances

You must transfer 25 TB from on premises to Google Cloud within 3 hours during failover and you need encrypted connectivity with redundancy and high throughput. Which network design should you use?

• ❏ A. HA VPN with Cloud Router

• ❏ B. Partner Interconnect with dual links only

• ❏ C. Dedicated Interconnect with HA VPN backup

• ❏ D. Dedicated Interconnect with Direct Peering backup

Northlake Systems plans to deploy a customer portal on Compute Engine and must keep the service available if a whole region becomes unavailable. You need a disaster recovery design that automatically redirects traffic to another region when health checks fail in the primary and that does not require any DNS changes during failover. What should you implement to meet these requirements on Google Cloud?

• ❏ A. Run two single Compute Engine instances in different regions within the same project and configure an external HTTP(S) load balancer to fail over between them

• ❏ B. Serve production from a Compute Engine instance in the primary region and configure the external HTTP(S) load balancer to fail over to an on premises VM through Cloud VPN during a disaster

• ❏ C. Deploy two regional managed instance groups in the same project and place them behind a global external HTTP(S) load balancer with health checks and automatic failover

• ❏ D. Use Cloud DNS with health checks to switch a public hostname between two regional external IP addresses when the primary region fails

To address skills gaps and improve cost efficiency for a new Google Cloud initiative, what should you do next?

• ❏ A. Enforce labels and budgets with Cloud Billing and quotas across projects

• ❏ B. Budget for targeted team training and define a role based Google Cloud certification roadmap

• ❏ C. Set project budget alerts and purchase one year committed use discounts

• ❏ D. Hire external consultants for delivery and defer internal training

Peregrine Outfitters is moving fast on GCP and leadership values rapid releases and flexibility above all else. You must strengthen the delivery workflow so that accidental security flaws are less likely to slip into production while preserving speed. Which actions should you implement? (Choose 2)

• ❏ A. Mandate that a security specialist approves every code check in before it merges

• ❏ B. Run automated vulnerability scans in the CI/CD pipeline for both code and dependencies

• ❏ C. Build stubs and unit tests for every component boundary

• ❏ D. Set up code signing and publish artifacts only from a private trusted repository that is enforced by the pipeline

• ❏ E. Configure Cloud Armor policies on your external HTTP load balancer

Which Google Cloud services should you combine to guarantee per account ordered delivery and exactly once processing for a streaming pipeline in us-central1 that handles about 9,000 events per second with latency under 800 ms?

• ❏ A. Cloud Pub/Sub with Cloud Run

• ❏ B. Cloud Pub/Sub ordering keys and Dataflow streaming with exactly once

• ❏ C. Cloud Pub/Sub with ordering enabled only

• ❏ D. Cloud Pub/Sub with Cloud Functions

Arcadia Payments processes cardholder transactions through an internal service that runs in its colocated data center and the servers will reach end of support in three months. Leadership has chosen to move the workloads to Google Cloud and the risk team requires adherence to PCI DSS. You plan to deploy the service on Google Kubernetes Engine and you need to confirm whether this approach is appropriate and what else is required. What should you do?

• ❏ A. Move the workload to App Engine Standard because it is the only compute option on Google Cloud certified for PCI DSS

• ❏ B. Choose Anthos on premises so that PCI scope remains entirely outside of Google Cloud

• ❏ C. Use Google Kubernetes Engine and implement the required PCI DSS controls in your application and operations because GKE is within Google Cloud’s PCI DSS scope

• ❏ D. Assume compliance is automatic because Google Cloud holds a PCI DSS attestation for the platform

How is a Google Cloud project’s effective IAM policy determined when policies exist at the organization, folder, and project levels?

• ❏ A. Only the project policy applies

• ❏ B. Union of local and inherited bindings

• ❏ C. Intersection of local and inherited policies

• ❏ D. Nearest ancestor policy overrides others

Rivermark Outfitters has finished moving its systems to Google Cloud and now plans to analyze operational telemetry to improve fulfillment and customer experience. There is no existing analytics codebase so they are open to any approach. They require a single technology that supports both batch and streaming because some aggregations run every 30 minutes and other events must be handled in real time. Which Google Cloud technology should they use?

• ❏ A. Google Kubernetes Engine with Bigtable

• ❏ B. Cloud Run with Pub/Sub and BigQuery

• ❏ C. Google Cloud Dataflow

• ❏ D. Google Cloud Dataproc

The correct option is Test coverage must scale far beyond earlier backends to validate behavior during global traffic spikes.

The new platform relies on global autoscaling, low latency load balancing, and fully managed streaming analytics. Spiky traffic and worldwide expansion demand verification at massive scale. Tests must exercise autoscaling behavior during sudden surges, validate graceful degradation and recovery, and confirm that sessions remain stable while instances are added or removed. Real time analytics must be tested for throughput, ordering sensitivity, and data correctness when events arrive late. Historical queries over large volumes must be validated for performance and reliability. This broader scope ensures the game remains available and responsive as popularity grows.

This approach also supports the business goals. It reduces risk to reputation by proving resilience before launch and it improves efficiency by finding scaling limits early. It verifies the end to end pipeline from telemetry ingestion through processing and storage so the team can trust dashboards and KPIs for campaigns and in app sales.

Drop unit testing and rely solely on full end to end tests is wrong because unit and integration tests provide fast feedback, isolate defects, and make failures easier to diagnose. End to end tests alone are slow and brittle and they do not give sufficient coverage of edge cases in isolation.

Depend only on Cloud Monitoring uptime checks for validation is wrong because uptime checks confirm external availability and basic reachability but they do not validate functional correctness, latency under load, autoscaling behavior, or data accuracy in streaming pipelines.

Introduce testing only after features are released to production is wrong because testing must shift left to pre production environments. Early load and resilience testing prevents incidents and protects user experience during launch spikes.

The correct option is Organization Policy compute.vmExternalIpAccess allowlist.

The Organization Policy compute.vmExternalIpAccess allowlist lets you set a deny by default stance on external IPs at the organization or folder level so it automatically applies to all current and future projects through inheritance. You then add only the approved instances to the allowlist so those specific VMs can retain external connectivity while all others cannot be created with or retain an external IP. This provides centralized governance and precise exceptions without per project configuration.

VPC Service Controls perimeters focus on protecting access to Google managed APIs and reducing data exfiltration risk and they do not control whether a VM can be assigned an external IP or support instance level allowlists for that capability.

Hierarchical firewall policy with egress deny and tag exceptions can block traffic but it does not prevent the assignment of external IPs to instances and it cannot centrally enforce an organization wide allowlist of specific instances that are permitted to keep external connectivity.

The correct option is Configure GKE Multi-Cluster Ingress with the global external HTTP(S) Load Balancer across the regional clusters. This gives a single global anycast entry point that routes each player to the nearest healthy cluster, provides automatic cross-region failover, and scales quickly to meet traffic spikes while keeping the design ready to onboard additional games later.

Multi-Cluster Ingress uses the global external HTTP(S) Load Balancer to perform proximity based routing and health checking so users are sent to the closest healthy region and requests fail over when a region goes down. It integrates natively with GKE Services through Network Endpoint Groups which keeps latency low and operations simple. Because the load balancer is fully managed it can absorb sudden increases in traffic and maintain a stable global IP for clients.

This approach also preserves flexibility for migrating older titles since you can add or remove clusters and services under the same global front door without forcing client changes. You can iterate region by region while keeping a consistent ingress model across games.

Create a global external HTTP(S) Load Balancer in front of a single multi-zonal GKE cluster is not suitable because a single cluster cannot span multiple regions and therefore cannot direct players to the closest region or provide regional isolation and failover that the scenario requires.

Use Traffic Director with proxyless gRPC to steer requests between regional services does not meet the need for a global public entry point. Traffic Director is a service mesh control plane for L7 service to service traffic within your VPC and would still require a separate external load balancer for internet clients. It adds complexity without delivering the requested global HTTP(S) ingress behavior.

Create a global external HTTP(S) Load Balancer backed by a managed instance group on Compute Engine adds unnecessary layers and operational overhead because the backend is on GKE. While you could proxy from VMs to clusters, it does not leverage native GKE integrations and is less direct for multi-region Kubernetes services compared to Multi-Cluster Ingress.

The correct option is External HTTPS Load Balancer with one certificate and a path-based URL map because it allows both API versions to share the same DNS name and TLS certificate while routing requests to separate backend services for the 30 day overlap.

This approach uses a single global frontend with one anycast IP and one certificate, and a URL map that matches paths such as /v1 and /v2 to different backend services. You avoid DNS changes during the coexistence period and can retire or switch a path rule when the beta ends without impacting the hostname or certificate.

Provision two external HTTPS load balancers and migrate with DNS later is incorrect because it depends on DNS changes and propagation and it cannot route by URL path, so it does not reliably present both versions at the same stable endpoint during the overlap.

Traffic Director is incorrect because it is a control plane for service mesh traffic within your network and does not provide a public edge endpoint or internet facing TLS termination.

Cloud DNS weighted round robin is incorrect because Cloud DNS does not support weighted routing and DNS cannot make decisions based on HTTP paths or ensure a single TLS certificate across multiple edge services.

The correct option is Grant the group the Project Owner role on the Storefront folder and remove its Project Owner role at the Organization level. This change removes the broad Organization level ownership that currently grants permissions everywhere and then scopes full control to only the Storefront folder, which lets the team fully manage Storefront projects while preventing them from creating resources in Ledger projects.

IAM policies are inherited from the Organization down to folders and projects, and permissions are additive. Removing the Organization level Owner binding stops those permissions from flowing into the Ledger folder. Granting Project Owner on the Storefront folder then restores full management rights for projects under that folder only. This satisfies least privilege and matches the requirement exactly.

Assign the group the Project Viewer role on the Ledger folder and the Project Owner role on the Storefront folder is incorrect because the existing Organization level Project Owner role would still be inherited by Ledger projects. IAM is additive and adding Viewer on Ledger does not remove or override Owner inherited from the Organization.

Assign the group only the Project Viewer role on the Ledger folder is incorrect for the same reason. The Organization level Project Owner role would continue to grant full rights on Ledger projects, and a Viewer grant cannot restrict those inherited permissions.

Move the Ledger folder into a separate Organization and keep the current group role assignments unchanged is unnecessary and introduces operational complexity. You can meet the requirement simply by removing the Organization level Owner grant and assigning the needed role at the Storefront folder, which avoids a cross organization migration and its constraints.

The correct option is Google Cloud Bigtable.

Google Cloud Bigtable is a wide column NoSQL database designed for very high write throughput and single digit millisecond reads. Time series is a primary use case and you can model rows by device identifier with a timestamp component in the row key so the data is naturally ordered for efficient range scans. This lets you ingest large volumes with very low latency and then serve recent records quickly by device key and time range. Bigtable scales horizontally without manual sharding and supports predictable performance as load grows.

Cloud Spanner is a relational service that excels at strongly consistent transactions and global schemas. It is not optimized as a primary store for high volume time series ingestion or ultra low latency key and time range lookups at large scale, so it is not the best fit here.

BigQuery is an analytical data warehouse that is optimized for large scale SQL analytics rather than operational serving. Even with streaming inserts, it is better suited for batch analysis and aggregations and not for very low latency per device range queries on recent data.

Firestore is a document database aimed at application backends for mobile and web. It has indexing and query constraints that make wide time series range scans inefficient at large scale and it is not intended as a primary store for high volume time series ingestion with very low latency serving.

The correct option is Create a firewall rule that permits load balancer health check probes to access the instance group on the health check port.

External HTTP(S) load balancers determine backend health by sending probes from Google front ends to the instances on the configured health check port. Because the virtual machines do not have public IP addresses, the probes still reach them over the VPC network and require an allow rule that targets the instance group and permits the health checker source ranges on the health check port. Without this rule the probes fail, the backend service marks instances unhealthy, and the managed instance group autoheals by recreating them in a loop. Allowing the probes stabilizes the group and lets healthy backends receive traffic.

Assign a public IP to each VM and open a firewall rule so the load balancer can reach the instance public addresses is incorrect because external HTTP(S) load balancing does not require public IPs on backend instances and the load balancer does not connect to backend public addresses. Adding public IPs would not fix the failed probe condition and would increase exposure.

Add a firewall rule that allows client HTTP and HTTPS traffic to the load balancer frontend is incorrect because clients connect to the load balancer external IP that is managed by Google and not to your instances. Your VPC firewall does not govern traffic from clients to the load balancer frontend, and this does not address the failed health checks that trigger instance recreation.

Configure Cloud NAT for the subnet so instances without public IPs can reach the internet is incorrect because health checks for external HTTP(S) load balancers originate from Google front ends and do not require internet egress from the instances. Cloud NAT can enable outbound internet access for package updates or external calls but it does not resolve the health check reachability needed here.

The correct choice is Use Dedicated Interconnect because it delivers private connectivity that bypasses the public internet and provides the strongest availability and compliance posture for critical workloads.

With Dedicated Interconnect your enterprise uses physical cross connects into Google to reach your VPC over private VLAN attachments and Cloud Router. Traffic stays off the public internet and you can design for 99.9 or 99.99 percent availability by deploying redundant links in diverse locations which aligns with strict compliance and uptime requirements and makes it a natural upgrade from partner based or VPN connectivity.

Direct Peering connects your network to Google public services using public IP addresses and it does not connect to your VPC networks. It therefore does not provide private connectivity to your workloads nor does it avoid the public internet path to your resources.

Increase Partner Interconnect capacity only adds bandwidth while keeping you on a partner delivered service and it does not change the reliance on a third party or deliver the highest end to end availability and control that strict compliance often requires.

HA VPN increases availability for tunnels but it still traverses the public internet so it cannot satisfy a requirement to keep critical workload traffic off the internet.

The correct option is Create a key in Cloud Key Management Service and set it as the default encryption key on the Cloud Storage bucket then grant the Dataproc service account permission to use that key.

This configuration uses a customer managed key that can be set to rotate automatically every ninety days. Setting it as the bucket default means all new objects are encrypted without any application changes and decryption remains transparent to readers that have permission to use the key. Granting the Dataproc service account the encrypter and decrypter role on the key preserves secure access for the jobs while keeping operations simple.

Rotation occurs by creating new key versions on schedule and Cloud Storage automatically uses the latest version for new writes while continuing to decrypt older objects with the version that encrypted them. There is no need to re encrypt existing data and access is governed by IAM on both the bucket and the key which aligns with Google guidance for regulated data.

Use Secret Manager to store and rotate an AES-256 key then encrypt each object before uploading to Cloud Storage is wrong because it forces client side encryption and requires you to design key distribution and re encryption during rotations which adds operational complexity and risk and does not integrate with bucket default encryption.

Generate and use a customer supplied encryption key for the bucket and pass the key with every object upload and download is wrong because it requires sending the key with each operation and does not provide automatic rotation which complicates Dataproc access and is not the recommended approach for managed simplicity.

Call the Cloud KMS encrypt API for each file before upload and manage ciphertext and re encryption during rotations yourself is wrong because it again implements client side encryption and makes you responsible for storing ciphertext, tracking key versions, and re encrypting data which is unnecessary when a bucket default key provides a managed path.

The correct option is Lifecycle rules to move at 60 days to Coldline and delete after 18 months.

This lifecycle policy directly implements an age based transition to a colder storage class at 60 days which minimizes cost for infrequently accessed data. It then deletes objects after 18 months which removes ongoing storage charges. Audit access is preserved because Cloud Storage audit logs are recorded in Cloud Logging and remain available based on your log retention settings even after objects are transitioned or deleted.

Enable Autoclass with an 18 month bucket retention policy is incorrect because Autoclass responds to access patterns and does not guarantee a move exactly at 60 days or specifically to Coldline. A retention policy only blocks deletion before the set duration and does not perform the required deletion after that time which means it does not satisfy the explicit delete requirement.

Lifecycle rules to move at 90 days to Nearline then at 180 days to Coldline is incorrect because it does not transition at 60 days and introduces an unnecessary Nearline step that can increase cost. It also fails to specify deletion after 18 months which is part of the requirement.

The correct option is Add the base64 encoded customer supplied key to the gsutil .boto configuration and upload with gsutil.

This satisfies the requirement for customer supplied encryption keys because Cloud Storage supports CSEK when you configure gsutil with a base64 encoded AES 256 key in the .boto file. After setting the encryption key in the configuration, uploads made with gsutil will encrypt the data at rest using your supplied key.

Create a bucket with a default Cloud KMS key and copy the files using Storage Transfer Service is incorrect because a default Cloud KMS key uses customer managed encryption keys rather than customer supplied keys. Storage Transfer Service can work with CMEK on the destination but it does not let you upload using CSEK.

Run gsutil cp and pass the key using the –encryption-key flag is incorrect because gsutil does not provide that flag. CSEK with gsutil is configured through the .boto file or by using a runtime configuration option rather than a dedicated –encryption-key switch.

Set an encryption key in gcloud config and then copy the files with gsutil is incorrect because gcloud configuration does not control gsutil encryption behavior. gsutil relies on its own configuration file for CSEK and will not read an encryption key from gcloud config.

The correct option is Turn on Firewall Rules Logging for the relevant rules.

Firewall Insights relies on firewall decision entries in Cloud Logging. When you enable this feature on specific rules it records allow and deny hits with the matching rule and target information. Those entries provide the data that Firewall Insights uses to populate its analysis even in a shared VPC.

Enable Packet Mirroring in the VPC is for copying traffic to a collector for deep packet inspection and it does not produce firewall decision logs that Firewall Insights can analyze.

Enable Data Access audit logs for Compute Engine records API accesses to resources and it does not log network traffic decisions or firewall rule matches that Firewall Insights needs.

Enable VPC Flow Logs on the subnets captures flow metadata from VM interfaces for monitoring and troubleshooting and it does not include which firewall rule allowed or denied the traffic so it cannot populate Firewall Insights.

The correct option is Rely on Cloud SQL default encryption at rest.

Cloud SQL automatically encrypts data at rest using Google managed encryption keys. This meets the requirement to protect data stored on disk while keeping the approach simple. It does not require any application changes and there is no need for you to create, rotate, or manage keys.

Enable TLS for connections between the application and Cloud SQL is not correct because TLS protects data in transit and does not address encryption of data stored on disk.

Configure Cloud KMS customer managed keys for the Cloud SQL instance is not correct because although CMEK also encrypts data at rest, it requires you to provision and manage keys and policies which is not the simplest approach and does not meet the requirement to avoid managing encryption keys.

Implement client side encryption in the application before writing to Cloud SQL is not correct because it requires changes to the application and additional key management. It adds significant complexity that is unnecessary when Cloud SQL already encrypts data at rest by default.

The correct answer is Roll back to the stable version, then use a staging project with Cloud Logging and Cloud Trace to diagnose latency. This restores the user experience immediately and gives you a safe place to analyze the regression without putting production at risk.

Rolling back returns all traffic to a known good version so users are protected while you work. A separate staging project lets you reproduce and profile the issue with Cloud Logging and Cloud Trace so you can inspect logs, request traces, and latency breakdowns without noisy production traffic or the risk of introducing new errors. This approach isolates blast radius, shortens mean time to recovery, and supports disciplined root cause analysis before any further rollout.

Use App Engine traffic splitting to shift 90% of traffic to the previous version and investigate with Cloud Logging still leaves a portion of users on the slow release, which means user impact continues. It also mixes signals across versions and slows recovery. In an incident you should stop the bleeding by sending all traffic to the stable version and only use splitting later for controlled canaries.

Increase App Engine instance class and raise autoscaling limits treats symptoms rather than the cause. A code or configuration regression will not reliably be fixed by adding resources and this can increase cost and even mask the real problem. Performance troubleshooting should start with returning to a known good build and then using observability tools to find the bottleneck.

Roll back to the previous version, then redeploy the updated build during a low traffic window at 3 AM and troubleshoot in production with Cloud Logging and Cloud Trace reintroduces risk by debugging in production and scheduling a speculative redeploy before you understand the issue. Investigation should occur in an isolated staging environment first, and only then should you promote a validated fix back to production.

The correct option is Load the dataset into BigQuery tables.

Native BigQuery tables are designed for petabyte scale analytics and provide a managed, highly available storage and compute platform. Analysts can use Standard SQL in BigQuery which keeps the experience familiar while delivering strong performance, automatic scaling, and simple operations. Storing the data in BigQuery tables also enables advanced features such as partitioning, clustering, time travel, and fine‑grained access control that make analysis straightforward and efficient.

Migrate the data into Cloud SQL for PostgreSQL is not suitable for eight petabytes because it targets transactional workloads and has instance storage and performance characteristics that do not fit warehouse scale analytics. Achieving this scale would require complex sharding and management which is the opposite of keeping analysis simple.

Keep the files in Cloud Storage and query them using BigQuery external tables sacrifices performance and features compared with loading the data into native tables. External tables can be convenient for occasional access, yet they add latency, limit some SQL capabilities, and make large scale analytics less predictable which makes analysis less simple for heavy, always‑on querying.

Write the data to Cloud Bigtable does not meet the requirement for a familiar SQL interface because it is a NoSQL wide column database optimized for low latency operational workloads. It is not a data warehouse and would require additional tooling to support SQL analytics which adds complexity.

The correct choice is Use gsutil with parallel copy.

This approach meets the ten minute requirement because gsutil can run multiple transfers in parallel and it supports resumable uploads and checksums for reliability. You can tune concurrency and enable parallel composite uploads for large objects which maximizes throughput from a Compute Engine staging directory while keeping the ETL tool unchanged.

Use gcsfuse and write files directly to the bucket is not suitable for high throughput bulk ingestion. gcsfuse provides convenience by mounting a bucket as a file system, yet it is not optimized for heavy parallel writes or large batch transfers and can be slower and less reliable under load.

Use gsutil to move files sequentially would process items one by one and is unlikely to finish a large batch within ten minutes. Move also deletes the source files which complicates retries if a transfer is interrupted.

Storage Transfer Service scheduled job focuses on scheduled or large scale migrations and requires setup and sometimes agents for POSIX sources. It is not an immediate on demand push from a VM directory and the scheduling and configuration overhead make it a poor fit for a quick upload window.

The correct option is Assess dependencies and use Migrate for Compute Engine to create waves, then prepare a runbook and a job for each wave and migrate the VMs in that wave together.

This approach follows Google guidance for large scale lift and shift migrations. You first assess application and infrastructure dependencies so that tightly coupled systems move together. You then use Migrate for Compute Engine to organize migration waves and orchestrate each wave with a runbook and a job. This enables repeatable replication, testing, and controlled cutover with low effort and only minor automatic adjustments, which aligns with the requirement to keep effort low while lifting and shifting.

Note that Migrate for Compute Engine has been succeeded by Migrate to Virtual Machines in Migration Center. The same principles of dependency assessment, waves, runbooks, and jobs continue to apply, so on newer exams you may see the newer service name even though the recommended process remains the same.

Create boot disk images for each VM, archive them to Cloud Storage, and manually import them to build Compute Engine instances is too manual for hundreds of VMs and does not provide automated dependency grouping, iterative testing, or streamlined cutover. It increases risk and effort rather than reducing them.

Use Migrate for Compute Engine with one runbook and one job that moves all VMs in a single event across the environment is a big bang migration that ignores dependency driven planning and wave based execution. This is risky and not a Google best practice for large estates where staged waves allow validation and rollback per wave.

Install VMware or Hyper-V replication agents on every source VM to copy disks to Google Cloud and then clone them into Compute Engine instances does not use the managed migration service and adds unnecessary agent overhead. It lacks integrated runbooks, jobs, and wave orchestration and therefore does not meet the low effort and best practice criteria.

The correct choice is Dedicated Interconnect with HA VPN backup.

This design meets the extreme throughput requirement. Moving 25 TB in three hours requires roughly 20 Gbps sustained. Dedicated Interconnect provides 10 Gbps and 100 Gbps ports and supports link aggregation across diverse edge locations, which gives the needed throughput with headroom and high availability. Because Interconnect traffic is not encrypted by default, the HA VPN backup supplies an encrypted path and adds resilience when the private circuit is unavailable or when encryption must be enforced.

Using redundant interconnects in separate edge availability domains with separate Cloud Routers is the recommended pattern for resiliency. Pairing that primary path with the VPN failover keeps connectivity during maintenance or outages. This combination satisfies the need for redundancy and encrypted connectivity while still achieving the required transfer window.

HA VPN with Cloud Router does provide encryption and dynamic routing, yet its practical tunnel throughput and aggregate limits make sustaining about 20 Gbps unrealistic, especially during failover when overhead and latency can increase. It is unlikely to move 25 TB in three hours.

Partner Interconnect with dual links only can offer high bandwidth, but it does not encrypt traffic by default and the option as written does not include any encrypted path. Since encryption is required, this design does not meet the needs.

Dedicated Interconnect with Direct Peering backup is not suitable because Direct Peering reaches Google public services rather than private VPC resources and it also does not provide encryption. It fails both the scope and security requirements.

The correct option is Deploy two regional managed instance groups in the same project and place them behind a global external HTTP(S) load balancer with health checks and automatic failover.

This design uses a global external HTTP(S) load balancer that presents a single anycast virtual IP worldwide. The load balancer continuously probes backend health and automatically shifts traffic to the secondary region when the primary fails. Because clients connect to the same global IP, no DNS changes are required during failover. Regional managed instance groups provide multi zone resilience within each region and support autohealing and autoscaling which strengthens both availability and recovery.

Run two single Compute Engine instances in different regions within the same project and configure an external HTTP(S) load balancer to fail over between them is not sufficient because each region would rely on a single VM which creates a single point of failure within that region. A robust design for disaster recovery uses managed instance groups for health, scaling, and autohealing rather than lone instances.

Serve production from a Compute Engine instance in the primary region and configure the external HTTP(S) load balancer to fail over to an on premises VM through Cloud VPN during a disaster does not meet the requirement to fail over to another region in Google Cloud. It also introduces dependency on a VPN path and on premises capacity which is not aligned with the stated goal of regional redundancy inside Google Cloud.

Use Cloud DNS with health checks to switch a public hostname between two regional external IP addresses when the primary region fails relies on DNS answer changes and client cache expiration which can delay failover due to TTLs. The question explicitly requires no DNS changes during failover, which DNS based approaches cannot guarantee.

The correct option is Budget for targeted team training and define a role based Google Cloud certification roadmap. This choice directly addresses the stated skills gap and creates a sustainable foundation for cost efficient adoption. Building internal capability leads to better design decisions, stronger governance and automation, and more efficient use of managed services which collectively reduce waste and operating costs.

Targeted training aligned to role based learning paths ensures engineers, operators, security professionals, and architects gain the skills they need for their responsibilities. A certification roadmap provides milestones and accountability which improves adoption quality and consistency. Teams that understand Google Cloud pricing models, resource provisioning, and service tradeoffs are more likely to right size workloads, implement budgets and alerts correctly, and avoid costly rework.

Enforce labels and budgets with Cloud Billing and quotas across projects is valuable governance, yet it does not solve the underlying skills gap and will not by itself drive better architectures or efficient operations. These controls are complementary and are most effective when teams are trained to use them well.

Set project budget alerts and purchase one year committed use discounts mixes a good basic control with a purchasing decision that should follow workload baselining and right sizing. Committed use discounts require predictable usage and informed capacity planning which are risky without the necessary skills in place.

Hire external consultants for delivery and defer internal training can help with short term execution, but it increases costs and creates dependency while leaving the skills gap unresolved. The question prioritizes closing the skills gap and improving long term cost efficiency which makes deferring training a poor next step.

The correct options are Run automated vulnerability scans in the CI/CD pipeline for both code and dependencies and Set up code signing and publish artifacts only from a private trusted repository that is enforced by the pipeline.

Automated vulnerability scanning in the pipeline catches known flaws in your code and in third party libraries before merges and releases. Integrating scanners with your CI system and scanning images stored in a registry allows teams to shift left while keeping throughput high because checks run consistently and quickly on every change.

Code signing with a private trusted repository that the pipeline enforces hardens the software supply chain. Only signed and verified artifacts from a controlled repository can be promoted, and deployment controls can block anything untrusted. This keeps releases fast because verification is automated while greatly reducing the chance of tampered or unverified builds reaching production.

Mandate that a security specialist approves every code check in before it merges is not appropriate when speed is a top priority because human approval becomes a bottleneck and does not scale. Automated policy gates provide consistent coverage without slowing the team.

Build stubs and unit tests for every component boundary improves correctness but does not directly address common security risks like vulnerable dependencies or supply chain tampering. It can also slow delivery without providing targeted security controls in the workflow.

Configure Cloud Armor policies on your external HTTP load balancer protects production traffic, yet it operates at runtime and does not strengthen the delivery workflow or prevent flawed code from entering production.

The correct option is Cloud Pub/Sub ordering keys and Dataflow streaming with exactly once.

Using Pub/Sub ordering keys lets you key messages by account so all events for the same account are delivered in order to the subscriber. This meets the per account ordering requirement while keeping producers and consumers decoupled and scalable in us-central1.

Dataflow streaming provides exactly once processing when the pipeline and sink support it. With state and checkpointing, and by writing to an exactly once capable sink such as BigQuery through the Storage Write API, the pipeline can deduplicate and commit atomically so each event is processed only once. Deployed in the same region, Pub/Sub and Dataflow can comfortably handle about 9,000 events per second and keep end to end latency under 800 ms with proper tuning and autoscaling.

Cloud Pub/Sub with Cloud Run does not guarantee exactly once processing and ordering can be broken when the service scales concurrently. Pub/Sub push delivery is at least once which means duplicates are possible and Cloud Run alone does not provide transactional sinks to enforce exactly once.

Cloud Pub/Sub with ordering enabled only ensures ordering per key but Pub/Sub delivery is at least once which means duplicates can occur so you still need a processing layer and a compatible sink to achieve exactly once.

Cloud Pub/Sub with Cloud Functions also relies on at least once delivery and can invoke functions multiple times which means duplicates must be handled. It does not provide the end to end guarantees needed for exactly once or per key ordering under horizontal scaling.

The correct option is Use Google Kubernetes Engine and implement the required PCI DSS controls in your application and operations because GKE is within Google Cloud’s PCI DSS scope.

Google Kubernetes Engine is included in Google Cloud’s PCI DSS attestation which means the platform can support cardholder data workloads. Compliance is a shared responsibility so you still need to design and operate the workload to meet control requirements. You should use hardened cluster settings such as GKE private clusters, Workload Identity, network policies, Shielded GKE nodes, and Binary Authorization. You should also enforce encryption in transit with TLS and encryption at rest with Cloud KMS or Cloud HSM for key management. Implement strong IAM with least privilege and Secrets management with Secret Manager. You should enable comprehensive logging and monitoring with Cloud Logging and Cloud Monitoring and set up audit trails, vulnerability scanning for container images, patching, backup and recovery, and network segmentation to keep the cardholder data environment isolated. You should coordinate with your assessor and use Google Cloud compliance documentation to map responsibilities.

Move the workload to App Engine Standard because it is the only compute option on Google Cloud certified for PCI DSS is incorrect because App Engine is not the only in-scope compute service. GKE and other services are also within Google Cloud’s PCI DSS scope, so limiting the choice to App Engine is not required.

Choose Anthos on premises so that PCI scope remains entirely outside of Google Cloud is incorrect because PCI scope is defined by where cardholder data is stored, processed, or transmitted rather than by avoiding Google Cloud. It also does not meet the stated direction to move the workloads to Google Cloud, and you would still be responsible for controls and assessment.

Assume compliance is automatic because Google Cloud holds a PCI DSS attestation for the platform is incorrect because the provider attestation does not make your application compliant. You must implement and validate required controls for your specific workload and undergo your own assessment as part of the shared responsibility model.

The correct option is Union of local and inherited bindings.

In Google Cloud IAM the effective permissions at a project are additive across the resource hierarchy. Bindings that grant roles at the organization or any parent folder are inherited by the project and combine with bindings that are set directly on the project. If any binding in that chain grants a role to a principal then that principal has the resulting permissions at the project. Project level bindings can add access but they do not remove access granted by ancestors unless a separate deny mechanism is used which is outside the scope of this question.

Only the project policy applies is incorrect because IAM roles and memberships set on the organization and folders are inherited by their descendant projects. The project does not ignore its ancestors.

Intersection of local and inherited policies is incorrect because IAM is not based on the overlap of bindings. A principal does not need the same role in multiple places. A single grant at any level in the hierarchy is sufficient.

Nearest ancestor policy overrides others is incorrect because there is no override behavior for allow policies in standard IAM evaluation. Child resources can add more permissions and ancestor grants remain effective.

The correct option is Google Cloud Dataflow. It is the only single managed service here that natively supports both batch and streaming in one technology, so it can handle periodic 30 minute aggregations and real-time events without changing platforms.

It provides a unified programming model through Apache Beam so you can implement one pipeline that runs in streaming or batch mode as needed. The service is serverless and autoscaling which reduces operational overhead and it offers windowing, triggers, stateful processing, and exactly once capabilities that are well suited to both real-time event handling and scheduled aggregations. Because there is no existing analytics codebase, starting fresh with Beam SDKs, ready-made templates, or SQL options is straightforward.

Google Kubernetes Engine with Bigtable is not a single analytics technology. It combines a container orchestration platform with a NoSQL database, so you would still need to build and operate your own ingestion and processing services to achieve unified batch and streaming analytics.

Cloud Run with Pub/Sub and BigQuery uses multiple services rather than one technology. While this architecture can process events, it is a composition of services and does not provide a single managed engine that unifies batch and streaming pipelines in one place.

Google Cloud Dataproc can run Spark for batch and streaming, yet it is a cluster-based service that you manage and it shines when you are migrating existing Hadoop or Spark code. Given there is no existing codebase and the requirement for one technology that unifies modes with minimal operations, it is not the best fit.