This solution correctly addresses the security requirement of having no public internet access by assigning only an internal IP address to the VM. It then leverages Private Google Access, which is the designated mechanism for VMs with only internal IPs to reach the public IP addresses of Google APIs and services, such as Cloud Storage. Cloud Storage is the appropriate service for storing arbitrary files like software installers. The gsutil command-line tool is the standard method for interacting with Cloud Storage from a VM. This approach is secure, efficient, and follows Google-recommended best practices for this scenario.

B: Using firewall rules to allow traffic to Cloud Storage IP ranges still requires the VM to have an external IP address to route traffic, which violates the core security constraint of no public internet access.

C: Cloud Source Repositories is a managed Git repository service designed for source code, not for storing software installation binaries or packages. Cloud Storage is the appropriate service for this use case.

D: This option is incorrect for two reasons: it uses the inappropriate service (Cloud Source Repositories) for the file type, and it proposes using the wrong tool (gsutil) to access it.

1. Private Google Access: "Private Google Access provides access to the external IP addresses of Google APIs and services from VMs that don't have external IP addresses." This directly supports the method described in option A.

Source: Google Cloud Documentation

"Private Google Access overview"

Section: "Overview".

2. Cloud Storage Use Cases: "Cloud Storage is a service for storing your objects in Google Cloud... Cloud Storage is a good choice for storing data for archival and disaster recovery

or for distributing large data objects to users via direct download." This confirms Cloud Storage is the correct service for installation files.

Source: Google Cloud Documentation

"Cloud Storage - Product overview".

3. gsutil Tool: "gsutil is a Python application that lets you access Cloud Storage from the command line." This confirms gsutil is the correct tool for downloading files from Cloud Storage.

Source: Google Cloud Documentation

"gsutil tool".

4. Cloud Source Repositories Use Cases: "Cloud Source Repositories provides Git version control to support developing and deploying your app." This highlights its purpose for source code

making it an inappropriate choice for software installers.

Source: Google Cloud Documentation

"Cloud Source Repositories - Product overview".

The App Engine flexible environment allows applications to be deployed within a Virtual Private Cloud (VPC) network. This is a critical capability for this scenario. By placing the application in a VPC, it can leverage Google Cloud's networking services, such as Cloud VPN. Cloud VPN establishes a secure, private IPsec tunnel between the Google Cloud VPC and the on-premises network. This configuration enables the App Engine application to communicate with the on-premises database using private IP addresses, fulfilling the core security requirement that the database is not exposed to the public internet.

A. App Engine firewall rules control inbound traffic to the application, not outbound connections to a database. The standard environment also lacks the required native VPC integration.

B. The App Engine standard environment runs in a sandboxed environment and cannot be placed directly within a VPC to initiate connections over a Cloud VPN tunnel.

C. App Engine firewall rules are the incorrect tool; they manage incoming requests to the application, not secure outgoing connections to an external database.

1. App Engine Flexible Environment Networking: The official documentation states that App Engine flexible environment instances run on Compute Engine virtual machines within your project's VPC network. This enables direct access to other VPC resources.

Source: Google Cloud Documentation

"App Engine flexible environment

Connecting to a VPC network". Section: "Accessing Google Cloud services".

2. Comparison of App Engine Environments: The choice between standard and flexible environments often depends on networking requirements. The flexible environment is explicitly designed for applications that need to operate within a VPC and access resources via VPN or Interconnect.

Source: Google Cloud Documentation

"Choosing an App Engine environment". The table comparing features highlights that the flexible environment allows "Access to resources in a VPC network".

3. Cloud VPN for Hybrid Connectivity: Cloud VPN is the designated service for securely connecting a VPC network to an on-premises network over an IPsec VPN connection.

Source: Google Cloud Documentation

"Cloud VPN overview". Section: "How Cloud VPN works".

4. App Engine Firewall Functionality: The documentation clarifies that App Engine firewalls are used to control incoming requests to the application from specified IP ranges

not for securing outbound traffic.

Source: Google Cloud Documentation

"Controlling Ingress Traffic". Section: "Creating firewalls".

The question asks for a method to perform resilience testing on an architecture that uses a regional Managed Instance Group (MIG). Resilience testing involves simulating failures to verify that a system can withstand them and continue operating. A regional MIG is specifically designed to provide high availability by distributing application instances across multiple zones within a region. Therefore, simulating a zonal failure by shutting down all virtual machines in one zone is the most direct and appropriate way to test the resilience of this architecture. This test will verify that the load balancer correctly redirects traffic to the instances in the remaining healthy zones, ensuring the application remains available as designed.

A. This describes a security service for detecting compromised credentials. It does not test the infrastructure's ability to withstand and recover from a failure.

B. This is a security monitoring practice to detect unauthorized access. It is not a form of resilience testing for infrastructure failures.

D. This tests the resilience of the Cloud SQL database layer, not the primary compute layer (the regional MIG) described as the core of the authentication service. While a valid test for the database, it doesn't test the resilience of the MIG itself.

1. Google Cloud Documentation - About managed instance groups (MIGs): "To protect your app from extreme cases where an entire zone fails

you can create a regional MIG... A regional MIG maintains instances of your app across multiple zones in a region... If any of these zones fail

your app can continue serving traffic from instances running in the remaining available zones in the same region." This source confirms that testing a zonal failure (as in option C) is the correct way to validate the resilience of a regional MIG.

2. Google Cloud - Disaster recovery planning guide: Under the section "Test disaster recovery

" the guide states

"After you've defined your disaster recovery plan

you should regularly test it... For example

to simulate a VM failure

you can stop the VM instance. To simulate a zonal failure

you can shut down all the VMs in a zone." This directly supports the methodology described in option C as a standard practice for resilience testing.

3. Google Cloud Blog - Chaos Engineering: the history

principles

and practice: "Chaos Engineering is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production... Common experiments include shutting down a VM or an entire zone to test for resiliency." This article frames the action in option C as a fundamental practice in Chaos Engineering

which is a form of resilience testing.

The core requirement is to capture and monitor real-time Key Performance Indicators (KPIs) with low latency. Google Cloud Monitoring is the purpose-built service designed specifically for this use case. It allows applications to send high volumes of custom metrics (the KPIs) via its API for real-time ingestion. The Cloud Monitoring console provides highly configurable, low-latency dashboards for immediate visualization and alerting. This solution directly and efficiently meets all stated requirements: high-volume, real-time capture, and low-latency monitoring, making it the most appropriate choice.

A. While Bigtable is excellent for storing large volumes of time-series data, Google Data Studio is a business intelligence tool with data freshness delays, making it less suitable for the "low-latency monitoring" requirement compared to native Cloud Monitoring dashboards.

C. This option describes a batch processing pipeline. Loading files from Cloud Storage every ten minutes introduces significant delay, which violates the "real-time" and "low latency" requirements of the scenario.

D. Cloud Datastore is a transactional NoSQL database not optimized for the high-throughput, analytical query patterns of time-series monitoring. Cloud Datalab is an interactive analysis environment, not a real-time monitoring dashboard.

1. Cloud Monitoring for Custom Metrics: Official Google Cloud documentation states

"You can instrument your application to send custom metrics to Cloud Monitoring... You can then use the data from custom metrics in charts and alerting policies." This directly supports using Cloud Monitoring to capture and view KPIs.

Source: Google Cloud Documentation

"Custom metrics overview"

Section: "What are custom metrics?".

2. Cloud Monitoring Dashboards: "You can display the metric data that you've collected as charts on a custom dashboard. The console provides both predefined dashboards and a Dashboards page where you can create and modify custom dashboards." This supports the low-latency monitoring requirement.

Source: Google Cloud Documentation

"Dashboards and charts"

Section: "Custom dashboards".

3. Bigtable for Time-Series Data: Google Cloud documentation positions Bigtable for time-series workloads

particularly for "large-scale time-series data

financial analysis

[and] IoT data

" often as a backend for applications or heavy analytics

not typically for direct

low-latency dashboarding via a BI tool.

Source: Google Cloud Documentation

"Schema design for time series data" in Cloud Bigtable.

4. BigQuery Batch Loading: Loading data from Cloud Storage into BigQuery is a batch operation. "Loading data from Cloud Storage is a free operation

but you are charged for storing the data in Cloud Storage." This method is not designed for real-time ingestion.

Source: Google Cloud Documentation

"Introduction to loading data from Cloud Storage" for BigQuery.

The most direct and idiomatic method for testing a new version of an App Engine application with production traffic is to use its built-in versioning and traffic-splitting capabilities. By deploying the update as a new version within the same application, you can configure App Engine to route a percentage of live traffic to this new version. This allows for canary testing and a gradual rollout, enabling you to monitor performance and user impact before migrating all traffic. This approach is a core feature of the App Engine platform, designed specifically for this use case, and does not require external infrastructure like load balancers or separate applications.

A. The Instance Group Updater is a feature for managing updates to Compute Engine Managed Instance Groups (MIGs), not for the App Engine platform-as-a-service (PaaS).

C. App Engine Standard environment services do not run within a user-managed VPC. While a load balancer can be used, it's an unnecessarily complex solution for a natively supported feature.

D. Deploying a completely new App Engine application is incorrect. The standard practice is to deploy a new version within the existing application to leverage built-in traffic management.

1. Google Cloud Documentation

App Engine

"Splitting Traffic": "App Engine allows you to split incoming traffic for your app between two or more of its versions. Splitting traffic allows you to do A/B testing between your versions and provides control over the pace when rolling out features." This directly supports deploying a new version and splitting traffic.

2. Google Cloud Documentation

App Engine

"Deploying a new version": "When you deploy a new version

it doesn't automatically receive traffic. You must choose to route traffic to it." This confirms the process described in the correct answer.

3. Google Cloud Documentation

Compute Engine

"Updating managed instance groups (MIGs)": This documentation details the use of the updater for Compute Engine VMs

confirming that it is not the correct tool for App Engine

thereby refuting option A.

Google Cloud VPC firewall rules are stateful and evaluated in order of priority, from the lowest number (highest priority) to the highest number (lowest priority). The first rule that matches the traffic is enforced, and subsequent rules are ignored. To implement a policy that allows specific egress traffic while denying all other egress traffic, you must create two rules:

1. An allow rule for the specific traffic (Active Directory) with a high priority (a low number, e.g., 100).

2. A deny rule for all other traffic with a lower priority (a higher number, e.g., 1000).

This configuration ensures that when a Compute Engine instance attempts to connect to the Active Directory server, the high-priority allow rule (priority 100) is matched first, and the connection is permitted. For any other destination, the allow rule is skipped, and the lower-priority deny rule (priority 1000) is matched, blocking the traffic.

B: This configuration is incorrect because the deny all rule has a higher priority (100) than the allow rule (1000). Consequently, all egress traffic, including the intended Active Directory traffic, would be blocked by the deny rule before the allow rule is ever evaluated.

C: This option is incorrect because it relies on an "implied deny egress rule," which does not exist. VPC networks have an implied allow egress rule. Furthermore, it incorrectly states the priority of this non-existent rule as 100.

D: Similar to option C, this is incorrect because it relies on a non-existent "implied deny egress rule." The implied egress rule in a VPC network allows all outbound traffic by default. It also incorrectly states the priority of this non-existent rule as 1000.

1. Google Cloud Documentation - VPC firewall rules: "Firewall rules are defined at the VPC network level... The rule's priority is an integer from 0 to 65535. Lower integers have higher priorities. If two rules have the same priority

the deny rule takes precedence." (See section: "VPC firewall rules").

2. Google Cloud Documentation - Using VPC firewall rules - Priority: "The priority of the rule

from 0 to 65535

inclusive. Lower integers indicate higher priorities. If you do not specify a priority

1000 is used. You create rules that have higher priorities (lower priority values) to allow or deny specific kinds of packets." (See section: "Firewall rule components > Priority").

3. Google Cloud Documentation - Using VPC firewall rules - Implied rules: "Every VPC network has two implied firewall rules that cannot be removed... An egress rule whose action is allow

destination is 0.0.0.0/0

and priority is the lowest possible (65535) lets any instance send traffic to any destination..." This confirms there is no implied deny egress rule

making options C and D fundamentally flawed. (See section: "Implied rules").

This approach directly addresses the core requirements of evaluating team readiness and creating a skills gap plan while incorporating cost optimization. Creating a role-based certification roadmap is a structured, measurable method to upskill the existing team. This builds long-term, in-house expertise, which is more cost-effective than continuously relying on expensive external consultants. Google Cloud certifications are designed around best practices, including the principles of the Google Cloud Architecture Framework's cost optimization pillar. By investing in the team's skills, the organization ensures that future projects are designed and managed with cost efficiency as a foundational principle, directly supporting the stated business goal.

A. Setting a deadline is a project management task, not a skills gap plan. It fails to provide a structured approach to ensure the team has the necessary competencies to meet that deadline successfully.

C. Hiring external consultants is a short-term solution that is generally more expensive than training an internal team. It does not build sustainable, in-house capability, thus conflicting with the long-term goal of cost optimization.

D. While a certification roadmap is good, pairing it with hiring consultants makes it a less cost-optimal solution. The primary focus should be on developing the existing team to foster self-sufficiency and reduce long-term costs.

1. Google Cloud Adoption Framework: The framework's "People" theme emphasizes the importance of training and developing internal teams. It states

"As your organization adopts the cloud

you need to help your people learn new skills... A training plan helps you to methodically upskill your teams." This supports investing in team training over external hires for long-term success. (Source: Google Cloud Adoption Framework whitepaper

"Phase 2: Plan and foundation

" Section: "The People theme").

2. Google Cloud Architecture Framework - Cost Optimization Pillar: This document outlines principles for building cost-effective solutions on Google Cloud. The skills validated by certifications

particularly the Professional Cloud Architect

are directly aligned with these principles

such as "Control resource costs" and "Optimize resource costs." A certified team is better equipped to apply these principles. (Source: Google Cloud Architecture Framework documentation

"Cost optimization pillar

" Section: "Overview of the pillar").

3. Google Cloud Certifications: Official documentation states that "Google Cloud certification validates your expertise and shows you can design

develop

manage

and administer application infrastructure and data solutions on Google Cloud technology." The role-based nature of these certifications ensures that team members acquire the specific skills needed for their function

which is the essence of a skills gap plan. (Source: Google Cloud

"Cloud Certifications" official page

"Grow your career" section).

The most direct and efficient method to audit BigQuery job history is by querying the INFORMATIONSCHEMA.JOBS views. These built-in, read-only views contain real-time metadata about BigQuery jobs run in the last 180 days. A simple SQL query against the INFORMATIONSCHEMA.JOBSBYPROJECT or JOBSBYUSER view allows you to filter by creationtime for the last month, filter jobtype for 'QUERY', and group by useremail to get the required count for each user. This approach is self-contained within BigQuery and designed specifically for this type of metadata analysis.

A. Data Studio is a data visualization tool. While it can display the results, it is not the primary method for retrieving the data. It would first need to connect to BigQuery and run a query, as described in option B.

C. This method is highly inefficient. It would require scripting to list jobs and then make a separate API call (bq show) for each job to get its details. Furthermore, the commands are described incorrectly; bq ls -j lists jobs, and bq show describes a specific resource.

D. While Cloud Audit Logs contain the necessary information, retrieving an aggregated count per user is more complex than a direct SQL query. It would require filtering in the Logs Explorer and then either manual counting, exporting logs for analysis, or creating a log-based metric.

1. Correct Answer (B): Google Cloud Documentation

"View job history

" describes using INFORMATIONSCHEMA views to retrieve job metadata. It provides example queries

such as counting jobs by user: SELECT useremail

COUNT(jobid) AS numjobs FROM region-us.INFORMATIONSCHEMA.JOBSBYPROJECT GROUP BY useremail ORDER BY numjobs DESC;. This directly supports option B.

Source: Google Cloud Documentation

"Get information about jobs

" Section: "Query the INFORMATIONSCHEMA.JOBS view".

2. Incorrect Option (D): Google Cloud Documentation

"BigQuery audit logs

" explains that audit logs are used to answer "who did what

where

and when." While valid for auditing

the process of getting an aggregate count is less direct than a SQL query.

Source: Google Cloud Documentation

"BigQuery audit logs

" Section: "Viewing logs".

3. Incorrect Option (C): Google Cloud Documentation

"Using the bq command-line tool

" details the use of bq ls to list resources and bq show to display properties of a specific resource. This confirms the option describes the commands incorrectly and highlights the inefficiency of a per-job lookup.

Source: Google Cloud Documentation

"bq command-line tool reference

" Sections: "ls" and "show".

4. Incorrect Option (A): Looker Studio (formerly Data Studio) Help

"Connect to BigQuery

" states that Looker Studio is a tool to "visualize data from your BigQuery tables." This confirms its role is in presentation

not primary data retrieval for an audit count.

Source: Looker Studio Help Center

"Connect to BigQuery".

The Organization Policy Service is the designated Google Cloud feature for enforcing broad constraints across your resource hierarchy. The constraints/compute.vmExternalIpAccess constraint is specifically designed to control which VM instances are permitted to have external IP addresses. By setting this policy at the organization level, you can define a list of specific, approved instances that are allowed to have external IPs. This policy is then inherited by all folders and projects, ensuring consistent enforcement across all VPCs in the organization, which directly and scalably meets the stated requirement.

A. Removing default routes prevents instances from reaching the internet but does not prevent a user with appropriate permissions from assigning an external IP address to an instance.

B. This approach only addresses a single, newly created VPC. It does not enforce the restriction across all existing and future VPCs within the organization as required.

C. Cloud NAT provides outbound internet connectivity for instances without external IPs. It is a mechanism to reduce the need for external IPs but does not restrict their assignment.

1. Google Cloud Documentation

Organization Policy Service

"Organization policy constraints": The compute.vmExternalIpAccess constraint is listed under Compute Engine constraints. The documentation states it "Defines the set of VM instances that are allowed to use external IP addresses." This directly supports using this constraint for the specified goal.

Source: Google Cloud Documentation

"Organization policy constraints"

Section: "Compute Engine constraints".

2. Google Cloud Documentation

Organization Policy Service

"Introduction to the Organization Policy Service": This document explains the hierarchical nature of organization policies. "The Organization Policy Service allows you to enforce policies on resource hierarchy nodes... Once a policy is set on a resource hierarchy node

that policy is inherited by all descendants of that node." This confirms that setting the policy at the organization level will enforce it everywhere.

Source: Google Cloud Documentation

"Introduction to the Organization Policy Service"

Section: "Benefits".

3. Google Cloud Documentation

Organization Policy Service

"Using organization policies": This guide provides examples of how to configure policies. For list constraints like vmExternalIpAccess

it shows how to use allowedValues to specify the exact resource names (e.g.

projects/my-project/zones/us-central1-a/instances/my-instance) that are exempt from the restriction.

Source: Google Cloud Documentation

"Using organization policies"

Section: "Set an organization policy with a list".

4. Google Cloud Documentation

VPC

"Routes overview": This document explains that routes direct traffic from an instance to a destination. While removing the default internet gateway route prevents egress traffic

it does not affect the metadata or configuration of the instance itself

including whether it has an external IP assigned.

Source: Google Cloud Documentation

"Routes overview"

Section: "Default route".

This architecture represents a standard active-passive disaster recovery (DR) pattern. A Global External HTTP(S) Load Balancer is used to direct traffic to a primary managed instance group (MIG) in one region. For DR, a second, standby MIG is deployed in a different region. The load balancer's health checks continuously monitor the primary MIG. If a regional outage makes the primary MIG unhealthy, the load balancer automatically fails over, redirecting all user traffic to the healthy standby MIG in the secondary region. Using instance groups, rather than single instances, provides high availability and scalability within each region.

A. Using single Compute Engine instances in each region creates single points of failure and lacks the scalability and auto-healing benefits provided by managed instance groups.

B. This describes a hybrid cloud DR strategy, not a multi-region cloud-native one. The primary deployment on a single instance is not a resilient or highly available design.

D. Deploying in separate projects adds unnecessary operational and networking complexity (e.g., requiring Shared VPC or VPC Peering) without providing any direct benefit for the stated DR goal.

1. Google Cloud Documentation

Disaster recovery planning guide: This guide outlines DR patterns. The "Warm standby (active-passive)" pattern for applications describes using Cloud Load Balancing to fail over between regions. It states

"You configure a health check that determines when to fail over to the VMs in [REGIONB]." This directly supports the architecture in option C.

Source: Google Cloud

"Disaster recovery scenarios for applications"

Section: "Warm standby (active-passive)".

2. Google Cloud Documentation

Cloud Load Balancing overview: The documentation for the Global External HTTP(S) Load Balancer explicitly states its multi-region capabilities. "Global external HTTP(S) Load Balancer is a global load balancer... You can use this load balancer to route traffic to backends in multiple regions." This confirms it is the correct tool for cross-region failover.

Source: Google Cloud

"Cloud Load Balancing overview"

Section: "Types of load balancers".

3. Google Cloud Documentation

Managed instance groups (MIGs): This document explains the benefits of MIGs over single instances

including high availability through autohealing and scalability through autoscaling

which are critical for a robust production architecture.

Source: Google Cloud

"Managed instance groups (MIGs)"

Section: "Benefits of MIGs".

4. Google Cloud Architecture Framework

Reliability pillar: This framework recommends deploying applications across multiple zones and regions to protect against failures. "To protect against regional failure

you need to deploy your application in multiple regions... you can use Cloud Load Balancing to provide a single IP address for users."

Source: Google Cloud Architecture Framework

"Reliability pillar"

Section: "Design for disaster recovery".