Professional Cloud Architect Free Practice Questions — Page 6
Question 37
Company Overview -
KnightMotives is a car manufacturer specializing in autonomous, self-driving vehicles, including Battery Electric Vehicles (BEVs), hybrids and traditional internal combustion engine (ICE) vehicles. While KnightMotives has made strides with the in-vehicle experience in their BEV fleet, the hybrid and ICE vehicles have yet to implement these new systems and are viewed poorly by critics and drivers. The lack of modern in-vehicle technology in hybrid and ICE vehicles has resulted in declining sales and customer satisfaction.
KnightMotives wants to modernize the consumer experience across all vehicles within five years Artificial Intelligence offers a unique opportunity to revolutionize the in-vehicle experience, as well as the shopping buying and service/maintenance experience. Investment in this new technology will require a shift in financial priorities on a global scale.
KnightMotives also wants to improve their online ordering system, which is unreliable. Systems for customers to build their vehicle online for acquisition through a dealer are not delivering the data or reliability that dealers need, causing. A strain in the relationship between KnightMotives and dealers. Service technicians and sales staff need better tooling to enhance dealer successes, including built-to-order vehicles.
Solution Concept -
KnightMotives wants to shift from manufacturing cars to creating a complete and compelling “automotive experience.” Then strategy prioritizes delivering a consistent experience across all models, developing AI-powered features, generating new revenue from data monetization, adopting a digital focus to differentiate their brand from competitors, and developing better tools for mechanics and salespeople.
Existing Technical Environment -
KnightMotives's IT is largely on-premises with some applications on major cloud platforms. Their supply chain runs on an outdated mainframe, and Enterprise Resource Planning (ERP) is also outdated, making new promotions and dealer discounts difficult to implement. Dealers have no budget for new equipment. There is fragmentation across vehicles with multiple code bases, and significant technical debt from supporting backwards compatibility. Network connectivity to manufacturing plants and vehicle connectivity in rural areas are challenges.
Business Requirements -
Key business requirements include fostering a personalized relationship with the driver and delivering a cohesive experience across all models. Creating a better build-to-order model will reduce time on the lot and provide transparency for both dealers and customers. Additionally, KnightMotives seeks to monetize corporate data to finance new technology investments, as their current AI infrastructure is obsolete and corporate data remains siloed. Security is a paramount concern due to past data breaches Adherence to European Union (EU) data protection regulations, especially for emerging autonomous platforms, is critical.
KnightMotives plans to make significant investments in fully autonomous driving capabilities, with initial implementation targeting regions with favorable regulatory environments. Prioritizing employee upskilling, attracting top-tier talent, and fostering better communication between business and technical teams are also critical objectives.
Technical Requirements -
• Modernizing the in-vehicle experience includes developing a consistent user experience (UX) that seamlessly integrates AI-powered features across all models, updating in-vehicle hardware and software in legacy models to support new UX features and AI capabilities, and ensuring reliable network connectivity, especially in rural areas, to support real-time AI features and data transmission.
• Network upgrades are necessary to support increased data traffic and improve connectivity between plants and headquarters.
• IT infrastructure modernization requires adopting a hybrid cloud strategy to leverage the benefits of both on-premises and cloud infrastructure, and gradually modernizing or replacing legacy systems to improve efficiency and agility.
• Autonomous vehicle development and testing requires investing in cutting-edge AI and machine learning technologies, building a robust simulation environment, and ensuring compliance with evolving regulations related to autonomous vehicles.
• Data monetization and insights requires implementing a robust data management platform, strict data security and privacy measures, and a scalable AI/ML infrastructure.
• Increased focus on security and risk management involves implementing a comprehensive security framework to protect against cyber threats and data breaches, developing an incident response plan, and providing security awareness training to employees.
• Providing a delightful experience for dealers and customers requires improving the online build-to-order system; developing modern dealer tools to streamline dealer operations, including sales, service, and inventory management; and implementing a comprehensive Customer Relationship Management (CRM) system to track customer interactions personalize experiences, and improve customer satisfaction.
Executive Statement -
KnightMotives is committed to enhancing safety and saving lives by leveraging an extensive body of data — encompassing driving, road conditions, behavioral studies, and crash safety statistics — to create compelling digital experiences for drivers. Our AI consistently outperforms national safety statistics, ensuring the unique and coveted KnightMotives experience is aligned across all our vehicle models.
Michael Knight, KnightMotives CEO
For this question, refer to the KnightMotives Automotive case study. As part of its development of fully autonomous driving vehicles. KnightMotives wants to analyze all vehicle sensor data during test drives. The analysis will enable KnightMotives to improve its software based on insights from this data.
• Different event types, such as parking, overtaking and navigating, need to be analyzed. Each test vehicle and event type has an ID.
• Different categories of sensors, such as cameras, radars, and ultrasonic beams, also need to be analyzed. During each autonomously-initiated event, data from multiple sensors will be captured and sent to Google Cloud where the data will be stored in Bigtable.
During data analysis, you want to be able to retrieve all sensor data of occurrences of the same event type for a specific vehicle within a specific interval of time. You need to design a Bigtable schema that is optimized for read performance. What should you do?
A. Use the sensor category, event ID, and timestamp (in that order) as the row key. Create a column family for each individual sensor and a column qualifier for each vehicle.
B. Use the timestamp, vehicle ID, and event ID (in that order) as the row key. Create a column family per sensor category, and use a column qualifier for each individual sensor within its respective category.
C. Use the vehicle ID, event ID, and timestamp (in that order) as the row key. Create a column family per sensor category, and use a column qualifier for each individual sensor within its respective category.
D. Use the vehicle ID and event ID (in that order) as the row key. Create a column family per sensor category, and use a column qualifier for each individual sensor within its respective category. Utilize the timestamped versions of a cell to distinguish between different moments in time of similar events.
Show Answer
Correct Answer: C
Explanation: Bigtable row keys should be designed to match the primary access pattern because rows are stored lexicographically by key. The required query is: specific vehicle -> specific event type -> time interval. A row key of vehicle ID, then event ID, then timestamp allows efficient prefix scans over the desired time range. Organizing data with column families by sensor category and column qualifiers for individual sensors is an appropriate schema. Using timestamps first causes inefficient reads for vehicle-specific queries and can create write hotspotting. Using only vehicle ID and event ID as the row key would overwrite data or rely on cell versions rather than representing distinct event occurrences.
Question 38
Your company is rapidly deploying containerized microservices on Google Kubernetes Engine (GKE) using a robust CI/CD pipeline. Security is a top priority, and you need to implement a comprehensive and efficient strategy to prevent container image vulnerabilities from reaching your GKE production environment. What should you do? (Choose two.)
A. Review the security reports generated by Artifact Analysis for each container image before deployment to GKE.
B. Incorporate vulnerability scanning before building container images, and use Google-maintained base images for your container deployments.
C. Enable Artifact Analysis for the container images, and stop deployment if critical vulnerabilities are found.
D. Use a custom security policy within your container image that restricts access to specific network ports and resources.
E. Enable Shielded GKE Nodes on the production cluster to automatically block the execution of container images with known vulnerabilities.
Show Answer
Correct Answer: A, C
Explanation: Artifact Analysis provides automated vulnerability scanning and reports for container images. Reviewing those reports before deployment and enforcing a deployment gate that blocks images with critical vulnerabilities is the most comprehensive strategy described. Option B is flawed because vulnerability scanning is performed on built images rather than before image build, even though using Google-maintained base images is a good practice. Options D and E do not prevent vulnerable images from reaching production.
Sources:
https://docs.cloud.google.com/kubernetes-engine/docs/concepts/gke-and-cloud-run
Question 39
Your company runs a critical, revenue-generating ecommerce application that is served by a regional managed instance group (MIG) behind an external HTTP(S) Load Balancer. The operations team is currently overwhelmed with low-priority notifications and is starting to ignore alerts. Your team's service level objective (SLO) is to maintain 99.9% availability, which is measured by the ratio of successful requests (2xx status codes) to total requests. You want to minimize noise from non-critical events and ensure that the team is only notified of issues that are actionable and threaten the SLO. What should you do?
A. Focus on cause-based alerts, creating alerting policies with thresholds for the Compute Engine instances, including CPU utilization, memory usage, disk I/O, and network traffic.
B. Create log-based alerts for only the WARN and ERROR log entries generated by the application to ensure that no potential issue is missed.
C. Implement an error budget policy based on the availability of the SLO. Create a "page” alert that triggers only when the rate of burn of the error budget predicts a full exhaustion within the next 24 hours.
D. Configure alerts based on predictive metrics. Use the instance count of the MIG as the primary metric to trigger an alert.
Show Answer
Correct Answer: C
Explanation: The best practice is to alert on symptoms that threaten the SLO rather than underlying infrastructure causes. An error budget burn-rate alert tied to the 99.9% availability SLO pages only when the error budget is being consumed fast enough to predict exhaustion within the configured window, reducing alert fatigue while ensuring actionable notifications. Infrastructure metrics, log severity, or instance count can generate noisy or indirect alerts that do not necessarily reflect user impact.
Question 40
Company Overview -
KnightMotives is a car manufacturer specializing in autonomous, self-driving vehicles, including Battery Electric Vehicles (BEVs), hybrids and traditional internal combustion engine (ICE) vehicles. While KnightMotives has made strides with the in-vehicle experience in their BEV fleet, the hybrid and ICE vehicles have yet to implement these new systems and are viewed poorly by critics and drivers. The lack of modern in-vehicle technology in hybrid and ICE vehicles has resulted in declining sales and customer satisfaction.
KnightMotives wants to modernize the consumer experience across all vehicles within five years Artificial Intelligence offers a unique opportunity to revolutionize the in-vehicle experience, as well as the shopping buying and service/maintenance experience. Investment in this new technology will require a shift in financial priorities on a global scale.
KnightMotives also wants to improve their online ordering system, which is unreliable. Systems for customers to build their vehicle online for acquisition through a dealer are not delivering the data or reliability that dealers need, causing. A strain in the relationship between KnightMotives and dealers. Service technicians and sales staff need better tooling to enhance dealer successes, including built-to-order vehicles.
Solution Concept -
KnightMotives wants to shift from manufacturing cars to creating a complete and compelling “automotive experience.” Then strategy prioritizes delivering a consistent experience across all models, developing AI-powered features, generating new revenue from data monetization, adopting a digital focus to differentiate their brand from competitors, and developing better tools for mechanics and salespeople.
Existing Technical Environment -
KnightMotives's IT is largely on-premises with some applications on major cloud platforms. Their supply chain runs on an outdated mainframe, and Enterprise Resource Planning (ERP) is also outdated, making new promotions and dealer discounts difficult to implement. Dealers have no budget for new equipment. There is fragmentation across vehicles with multiple code bases, and significant technical debt from supporting backwards compatibility. Network connectivity to manufacturing plants and vehicle connectivity in rural areas are challenges.
Business Requirements -
Key business requirements include fostering a personalized relationship with the driver and delivering a cohesive experience across all models. Creating a better build-to-order model will reduce time on the lot and provide transparency for both dealers and customers. Additionally, KnightMotives seeks to monetize corporate data to finance new technology investments, as their current AI infrastructure is obsolete and corporate data remains siloed. Security is a paramount concern due to past data breaches Adherence to European Union (EU) data protection regulations, especially for emerging autonomous platforms, is critical.
KnightMotives plans to make significant investments in fully autonomous driving capabilities, with initial implementation targeting regions with favorable regulatory environments. Prioritizing employee upskilling, attracting top-tier talent, and fostering better communication between business and technical teams are also critical objectives.
Technical Requirements -
• Modernizing the in-vehicle experience includes developing a consistent user experience (UX) that seamlessly integrates AI-powered features across all models, updating in-vehicle hardware and software in legacy models to support new UX features and AI capabilities, and ensuring reliable network connectivity, especially in rural areas, to support real-time AI features and data transmission.
• Network upgrades are necessary to support increased data traffic and improve connectivity between plants and headquarters.
• IT infrastructure modernization requires adopting a hybrid cloud strategy to leverage the benefits of both on-premises and cloud infrastructure, and gradually modernizing or replacing legacy systems to improve efficiency and agility.
• Autonomous vehicle development and testing requires investing in cutting-edge AI and machine learning technologies, building a robust simulation environment, and ensuring compliance with evolving regulations related to autonomous vehicles.
• Data monetization and insights requires implementing a robust data management platform, strict data security and privacy measures, and a scalable AI/ML infrastructure.
• Increased focus on security and risk management involves implementing a comprehensive security framework to protect against cyber threats and data breaches, developing an incident response plan, and providing security awareness training to employees.
• Providing a delightful experience for dealers and customers requires improving the online build-to-order system; developing modern dealer tools to streamline dealer operations, including sales, service, and inventory management; and implementing a comprehensive Customer Relationship Management (CRM) system to track customer interactions personalize experiences, and improve customer satisfaction.
Executive Statement -
KnightMotives is committed to enhancing safety and saving lives by leveraging an extensive body of data — encompassing driving, road conditions, behavioral studies, and crash safety statistics — to create compelling digital experiences for drivers. Our AI consistently outperforms national safety statistics, ensuring the unique and coveted KnightMotives experience is aligned across all our vehicle models.
Michael Knight, KnightMotives CEO
For this question, refer to the KnightMotives Automotive case study. KnightMotives wants to personalize the dealer experience for its customers and has decided to train its own AI models for personalized recommendations. The company will start collecting personally identifiable information (PII) from its customers to use as part of the models’ training data. KnightMotives wants to ensure maximum security and compliance worldwide. You need to ensure the data is encrypted both at rest and during AI model training without impacting the models’ accuracy. What should you do?
A. Store the training data in BigQuery using column-level encryption. Train the model using Confidential GKE Nodes.
B. Store the training data in BigQuery using column-level encryption Train the model on VertexAI notebooks using customer-managed encryption keys.
C. Process all data with Sensitive Data Protection’s de-identification service. Replace any PII with a random string before storing it. Train the model using Confidential GKE Nodes.
D. Process all data with Sensitive Data Protection's de-identification service. Replace any PII with a random string before storing it. Train the model on VertexAI notebooks using customer-managed encryption keys.
Show Answer
Correct Answer: A
Explanation: The requirement is to protect PII at rest and during model training (data in use) without reducing model accuracy. Column-level encryption in BigQuery protects sensitive fields at rest. Confidential GKE Nodes use confidential computing to protect data while it is being processed in memory during training. De-identification by replacing PII with random strings would reduce the usefulness of the data for personalized recommendations, and CMEK on Vertex AI notebooks protects storage but does not provide encryption in use during computation.
Question 42
To improve governance and security, your organization has structured the Google Cloud environment using folders for different business units. Each business unit folder has subfolders for development, staging, and production environments, which must comply with internal security controls:
• Production workloads must be protected from direct internet ingress by default unless explicitly tagged.
• The application must be accessible to customers over HTTPS.
You need to design a scalable and enforceable model that blocks internet ingress traffic to the production folders while selectively allowing direct HTTPS traffic to the necessary virtual machines. You must also ensure that individual project teams cannot overwrite these controls once they are implemented for all current and future production projects. What should you do?
A. At each production folder, apply a hierarchical firewall policy to deny all ingress except for HTTPS to tagged VMs.
B. Mandate the application teams to deploy a Terraform module to create VPC firewall rules in each project that deny ingress and allow HTTPS.
C. At the organization root, apply a hierarchical firewall policy to deny all ingress except for HTTPS to tagged VMs.
D. At each production folder, use an organization policy to block all external IPs and require teams to use external HTTPS load balancers.
Show Answer
Correct Answer: A
Explanation: Hierarchical firewall policies applied at the production folder provide centralized, inherited enforcement for all current and future projects in those folders. They are evaluated before VPC firewall rules, preventing project teams from overriding the controls. Secure tags can be used to selectively allow HTTPS ingress only to tagged VMs while denying other direct internet ingress by default.
Question 43
You are migrating a critical on-premises inventory management application to Google Cloud. The application is a monolith with a traditional relational database, and the immediate business goal is a rapid data center exit. The monolith is exposing an API to other business critical applications.
The long-term vision is to modernize the application into globally distributed, cloud-native services to support the company’s expansion. You need to design the initial cloud architecture to ensure that future modernization causes the least possible disruption to other applications that depend on inventory data. The future modernization might require the API to change structure. What should you do?
A. Use Service Directory to register the monolith's endpoint, allowing dependent applications to look up its address and connect directly.
B. Implement a managed API facade with Apigee to handle all requests from dependent applications on behalf of the monolith’s backend.
C. Use an internal load balancer to provide a stable IP for dependent applications to connect directly to the monolith's native API.
D. Provide dependent applications with direct database access by creating secured SQL VIEWs on Cloud SQL for them to query.
Show Answer
Correct Answer: B
Explanation: A managed API facade using Apigee decouples client applications from the backend implementation. It enables a rapid lift-and-shift by fronting the monolith immediately, then supports gradual modernization by routing, versioning, and transforming requests as the backend evolves into microservices, minimizing disruption even if the backend API changes. Service Directory and an internal load balancer only provide discovery or stable connectivity, not API abstraction. Direct database access tightly couples consumers to the data model and bypasses the application API.
Question 44
You are planning to migrate your on-premises compute and SAP workloads to Google Cloud. You want to follow Google-recommended practices to quickly create a cost estimate for running these workloads in Google Cloud. What should you do?
A. Leverage Cloud Asset Inventory to gather data and generate a cost estimate.
B. Use the Google Cloud pricing calculator, and input the estimated resource to generate a cost estimate.
C. Engage with a Google Cloud partner to perform a comprehensive assessment and provide a customized cost estimate.
D. Gather data about your current environment, and leverage Google Cloud Migration Center to generate a cost estimate.
Show Answer
Correct Answer: D
Explanation: Google recommends using Migration Center to assess existing on-premises environments, including compute and SAP workloads. After gathering inventory data, Migration Center can generate infrastructure assessments and cost estimates for running workloads on Google Cloud, making it the recommended approach for rapid migration planning.
Question 45
Your team is running applications on a Google Kubernetes Engine (GKE) cluster with a private endpoint. You've set up a Cloud Deploy pipeline, but deployments to the GKE cluster are failing. You need to resolve the issue. What should you do?
A. Use VPC Service Controls with Cloud Build Update the Cloud pipeline to use Cloud Build as its execution environment.
B. Create a Cloud Build private pool in the default VPC. Use Cloud Build to deploy the applications to the GKE cluster.
C. Create a Cloud Build private pool that is peered with the same VPC network as your GKE cluster. Update the Cloud Deploy pipeline to use this private pool as its execution environment.
D. Create a custom target in Cloud Deploy Update the deploy pipeline to use the custom target for the application deployment.
Show Answer
Correct Answer: C
Explanation: Cloud Deploy uses Cloud Build to execute render and deploy operations. A private GKE control plane is not reachable from the default Google-managed Cloud Build workers. Configuring a Cloud Build private pool that is connected via VPC Network Peering to the same VPC as the private GKE cluster provides network reachability to the cluster's private endpoint, and updating Cloud Deploy to use that private pool resolves the deployment failures.
Question 46
A Cloud Run service running your serverless application is unable to connect to an AlloyDB database created with default configurations. You need to troubleshoot and resolve the issue as quickly as possible. What should you do?
A. Verify that the Cloud Run service and AlloyDB instance are in the same region.
B. Enable Direct VPC egress for the Cloud Run service, and send traffic directly to a VPC.
C. Disable the default public IP address of the AlloyDB instance, and use the private IP address in the connection string.
D. Create a Cloud SQL instance, and migrate the AlloyDB database to PostgreSQL on Cloud SQL.
Show Answer
Correct Answer: B
Explanation: AlloyDB uses private IP by default within a VPC. Cloud Run does not have VPC access by default, so it cannot reach the database unless configured for VPC connectivity. Enabling Direct VPC egress allows Cloud Run to send traffic into the VPC and connect to the AlloyDB private IP. Simply being in the same region does not establish connectivity, AlloyDB does not have a default public IP to disable, and migrating to Cloud SQL is unnecessary.
Question 47
You are managing a few hundred Ubuntu and Red Hat Enterprise Linux virtual machines running on Google Compute Engine (GCE). You need to patch the operating systems for those VMs periodically in a secure and scalable way. What should you do?
A. Create a Cloud Scheduler job to run a Bash script that securely connects to each VM and applies the patches.
B. Configure Config Sync, and install the Ops Agent on each VM. Schedule a patch job to apply patches on each VM.
C. Schedule a Cloud Build job, and use Cloud Deploy to run a patch job that applies patches on each VM.
D. Set up VM Manager, and install the OS Config agent on each VM. Schedule a patch job to apply patches on each VM.
Show Answer
Correct Answer: D
Explanation: VM Manager is Google Cloud's native service for managing VM operating systems at scale, including patch management. It uses the OS Config agent installed on Compute Engine VMs to execute scheduled patch jobs securely across fleets of Linux and Windows instances. Config Sync is for Kubernetes configuration management, Ops Agent is for logging/monitoring, and Cloud Build/Cloud Deploy are CI/CD tools rather than OS patch management.
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