Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 2 Q16-30

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Question 16

Your team wants to run a containerized application with minimal operational overhead and automatic scaling based on HTTP traffic. Which Google Cloud service should be used?

A) Cloud Run
B) Compute Engine
C) App Engine Flexible Environment
D) Kubernetes Engine with manual scaling

Answer: A

Explanation:

There is a fully managed serverless container platform that allows developers to deploy containers directly without needing to manage servers, clusters, or scaling policies manually. It automatically scales the container instances based on incoming HTTP requests, meaning it creates or terminates instances as demand fluctuates. This ensures efficient resource utilization and minimizes operational overhead because developers do not have to manually monitor load, set up load balancers, or handle instance provisioning. Billing is tied to actual usage, further reducing cost for sporadically accessed applications.

Virtual machines provide full control over the environment, including the operating system and software stack, but require manual setup for scaling. To achieve auto-scaling in this scenario, engineers would need to configure instance groups, load balancers, and scaling policies, increasing complexity and ongoing maintenance. This approach contradicts the requirement of minimal operational responsibility.

A platform designed for flexible applications supports deployment of containers, custom runtimes, and background services. Although it provides managed scaling, it often requires additional configuration and may not scale precisely based on incoming request patterns, especially HTTP-driven traffic. Its scaling is more oriented toward instances and resource allocations rather than pure request-level auto-scaling, making it slightly less optimized for a serverless approach.

Orchestrating containers on a cluster requires managing nodes, deployments, and scaling policies explicitly. Manual scaling of a cluster means that developers must monitor usage and intervene to add or remove nodes, which adds operational overhead. While Kubernetes is powerful and flexible, it does not inherently reduce administrative effort without automation layers such as Horizontal Pod Autoscalers and cluster autoscaling. Implementing these would involve additional configuration and monitoring.

Using the fully managed serverless container platform eliminates the need for direct infrastructure management, provides automatic HTTP-based scaling, and allows teams to focus entirely on application code rather than operational tasks. This platform also supports multiple revisions, traffic splitting, and rapid deployments, enhancing agility and reducing operational risk. It fulfills the goal of minimal administrative effort while ensuring responsiveness to variable traffic patterns, making it the most suitable choice.

Question 17

A company wants to ensure that a BigQuery dataset can only be queried by analysts during business hours and never modified. What combination of controls should be applied?

A) IAM roles for read-only access and scheduled query auditing
B) VPC Service Controls and firewall rules
C) Encryption keys with expiration
D) Multi-region replication

Answer: A

Explanation:

Controlling access to datasets requires both identity-based permissions and monitoring of activity. Granting read-only roles to analysts ensures that they cannot modify the dataset, preventing accidental or intentional write operations. This role restricts capabilities to querying and viewing metadata without allowing insertion, update, or deletion of records. By using identity and access management policies to assign these roles, organizations can enforce the principle of least privilege, ensuring only intended actions are permitted.

Enforcing temporal constraints requires monitoring or scheduled enforcement because native role assignments are not inherently time-bound. Scheduled auditing of queries provides visibility into access patterns, allowing administrators to detect and alert if queries occur outside permitted hours. This combination of role-based access control and audit monitoring ensures both functional restrictions and temporal enforcement, providing a robust mechanism to meet compliance requirements.

Network perimeter controls are designed to prevent data from leaving specific network boundaries. While effective for external exposure protection, they do not restrict who within the authorized environment can query or modify data, nor do they provide time-of-day constraints. Therefore, using network isolation alone does not satisfy the requirement of limiting query access to business hours.

Encryption keys can protect the underlying dataset by requiring decryption for access. However, simply expiring keys is operationally risky and does not provide granular role-based permissions or enforce temporal query restrictions. Keys are intended for protecting at-rest data, not controlling who can interact with the dataset in specific ways.

Replication across regions enhances availability and disaster recovery. While this is useful for continuity, it does not provide query control, read-only enforcement, or temporal access restrictions. Therefore, replication alone does not meet the security and operational requirements.

The correct approach leverages read-only roles to prevent modification and scheduled query auditing to enforce temporal access rules. This ensures analysts can access data for analysis only during approved hours, while all modification attempts are blocked and monitored. The combination of identity-based access controls and activity monitoring addresses both operational security and compliance needs, fulfilling the requirement for controlled read-only, time-constrained access to BigQuery datasets.

Question 18

A company needs to ensure high availability for a web application deployed across multiple regions in Google Cloud. Which architecture provides fault tolerance and minimal downtime?

A) Global Load Balancer with backend services in multiple regions
B) Single-region Managed Instance Group with autoscaling
C) Cloud Functions deployed in one region
D) Multi-zone Compute Engine VMs without a load balancer

Answer: A

Explanation:

Ensuring high availability requires distributing traffic across multiple geographically separated locations to prevent failure of a single region from affecting service. Using a global load balancing system allows requests to be intelligently routed to healthy backend services across multiple regions. If one region experiences downtime or latency issues, the load balancer automatically directs traffic to other healthy regions. This approach ensures minimal service interruption and maximizes resilience by leveraging multiple fault domains, network paths, and data center locations.

Deploying a managed instance group in a single region provides redundancy within that region, such as across multiple availability zones, but does not protect against regional outages. Regional disruptions such as network outages, natural disasters, or platform incidents would result in complete service unavailability, making this insufficient for global high-availability requirements.

Serverless functions deployed in a single region scale automatically within that region, but if the region fails, the application becomes completely inaccessible. While they provide fault tolerance locally, they do not address the need for cross-region availability or low-latency routing globally.

Deploying virtual machines across multiple zones within the same region without a load balancer does not provide automatic traffic distribution. Clients may attempt to connect to unavailable instances, resulting in failed requests and downtime. A load balancer is required to abstract endpoint availability and direct traffic to operational instances reliably.

The optimal architecture combines global traffic management, health checks, and regional backend services to provide continuous availability. This approach allows traffic to flow to the nearest or healthiest region, reduces latency, and eliminates single points of failure. It also enables rapid scaling, geographic failover, and high resilience. Using a global load balancer with multi-region backends satisfies fault tolerance and ensures minimal downtime for users worldwide.

Question 19

Your operations team needs to automatically scale a containerized application in Kubernetes based on CPU usage. Which resource should be configured?

A) Horizontal Pod Autoscaler
B) Node Pool Autoscaler
C) Pod Disruption Budget
D) StatefulSet

Answer: A

Explanation:

Managing containerized workloads efficiently requires mechanisms that dynamically adjust the number of running instances based on real-time resource consumption. The horizontal scaling mechanism in Kubernetes monitors metrics such as CPU usage and automatically increases or decreases the number of running pods. When CPU usage exceeds a defined threshold, new pods are launched to distribute the load evenly. Conversely, when usage drops below the threshold, excess pods are terminated to conserve resources and reduce operational costs. This allows applications to remain responsive under varying workloads without manual intervention. It also ensures that workloads are not constrained by insufficient capacity, preventing service degradation and bottlenecks while maintaining operational efficiency.

Node pool scaling adjusts the underlying virtual machine nodes of the cluster rather than individual pods. Although it affects capacity, it is at a lower level of abstraction. Scaling nodes does not directly respond to application-level metrics like CPU usage within containers. It addresses overall cluster resource availability, but without pod-level scaling, applications could still experience latency or resource exhaustion before node provisioning catches up. Therefore, relying solely on node-level scaling does not fully address dynamic CPU usage scaling needs.

Pod disruption budgets define the minimum number of pods that must remain operational during maintenance events. While important for availability, they do not scale the number of pods based on CPU or workload demands. Their primary purpose is to prevent excessive voluntary termination of pods during rolling updates, maintenance, or node drains, ensuring that a minimum level of service continuity is maintained. They are complementary to autoscaling but do not replace dynamic scaling mechanisms.

StatefulSets are used for workloads requiring stable network identities, persistent storage, or ordered deployment. Although they manage pod replicas, they are not inherently designed to scale automatically based on CPU or memory metrics. Scaling must be configured separately and does not happen dynamically in response to resource usage without additional autoscaler integration. Using StatefulSets alone does not achieve responsive, demand-driven scaling.

The horizontal pod scaling approach ensures that each containerized application can handle fluctuating workloads effectively by automatically adjusting the number of pod instances. It monitors specified metrics and applies scaling rules, enabling high availability, optimized resource utilization, and performance consistency. By coupling horizontal scaling with proper monitoring, workloads remain responsive while maintaining operational simplicity. This solution achieves automated, resource-driven scaling at the application layer, addressing CPU usage patterns precisely and efficiently, without requiring manual adjustments or intervention.

Question 20

A company wants to store log data from multiple projects for long-term retention and analysis while ensuring immutability. Which solution should be implemented?

A) Export logs to Cloud Storage with a retention policy and bucket lock
B) Use BigQuery external tables
C) Store logs in Cloud SQL
D) Stream logs to Pub/Sub only

Answer: A

Explanation:

Centralized log retention requires a storage system that is both durable and capable of enforcing strict access controls. Cloud Storage provides a highly available, durable solution capable of storing large volumes of log data. Applying retention policies ensures that data cannot be deleted before a specified period, guaranteeing compliance with regulatory or internal governance requirements. Enabling bucket lock enforces immutability, preventing any changes to the retention configuration, making logs tamper-proof. Logs can be exported automatically from multiple projects and written to the centralized storage bucket, simplifying management and ensuring consistency. This approach provides long-term, secure storage while protecting against accidental or malicious deletion.

BigQuery external tables allow query access to data stored elsewhere without ingesting it into BigQuery. While useful for analytics, external tables do not inherently enforce immutability or long-term retention. The external data source could still be modified or deleted, which does not satisfy the requirement for immutable storage. Additionally, BigQuery is better suited for analytical queries rather than acting as a long-term, tamper-proof archive.

Cloud SQL is a relational database service. While it can store structured logs, it is not designed for extremely large volumes of immutable log data. Enforcing immutability in Cloud SQL would require complex application logic or triggers, which adds operational overhead and does not provide the same durability guarantees as a storage service designed for archival. Therefore, using a relational database is less suitable for log retention and immutability.

Streaming logs to Pub/Sub enables real-time log delivery and event-driven processing. However, Pub/Sub is a messaging system, not persistent storage. Messages are retained temporarily and are intended for delivery to subscribers. Relying on Pub/Sub alone does not meet long-term retention requirements or guarantee immutability, making it unsuitable for archival purposes. Pub/Sub can be part of the pipeline, but storage with retention policies is necessary for compliance and long-term protection.

The combination of exporting logs to Cloud Storage, applying a retention policy, and enabling bucket lock provides durability, immutability, and centralized management. Logs from multiple projects can be collected in a single location, ensuring consistent governance and regulatory compliance. Automated exports reduce operational effort, and retention plus bucket lock ensures that even administrators cannot delete logs prematurely. This approach is highly scalable, cost-effective, and aligns with industry best practices for secure, long-term log retention. It guarantees that audit and operational data remain intact and retrievable for required retention periods, making it the correct solution.

Question 21

A team needs to deploy a database cluster with automatic failover, high availability, and minimal management overhead. Which service should be used?

A) Cloud SQL with high availability enabled
B) Compute Engine with manually configured MySQL cluster
C) Firestore in Native mode
D) Memorystore Redis

Answer: A

Explanation:

High availability for a relational database is best achieved using a fully managed service that handles replication, failover, backups, and maintenance automatically. Cloud SQL supports multiple relational engines and allows enabling high availability through regional replication. With this configuration, the system maintains a standby instance in a separate zone within the same region. In the event of a primary instance failure, automatic failover occurs within minutes without manual intervention. The managed service also automates patching, backup scheduling, and monitoring, significantly reducing operational overhead compared to self-managed clusters. This ensures continuity, resiliency, and minimal management responsibility.

Manually configuring a database cluster on virtual machines provides flexibility but requires the team to manage replication, failover scripts, backups, and recovery procedures. Any error in configuration or monitoring could result in downtime or data loss. Additionally, manual maintenance tasks such as software upgrades, scaling, and patching increase operational effort. This approach does not align with the requirement of minimal management overhead.

Firestore in Native mode is a NoSQL document database suitable for unstructured, schema-less data. While it is fully managed, it is not designed to provide traditional relational database capabilities or high availability in the relational sense. It cannot host transactional workloads requiring relational joins or structured SQL queries. Using it for relational workloads would require significant architectural changes and may not meet application requirements.

In-memory caching services such as Memorystore provide fast, ephemeral storage for key-value data. They do not provide persistent storage, transactional support, or relational database features. While useful for caching and temporary data, they cannot replace a high-availability relational database for primary data storage. Memorystore also does not include automatic failover for structured transactional data.

Using Cloud SQL with high availability enabled provides the complete solution by combining managed replication, failover, backups, and monitoring. This reduces operational overhead, ensures continuous availability, and aligns with best practices for database deployment in the cloud. The service abstracts complex infrastructure management while providing reliability, durability, and automatic failover, making it the most suitable option for mission-critical relational workloads.

Question 22

Your team wants to implement centralized identity and access management for Google Cloud resources across multiple projects. Which solution should be used?

A) Google Cloud Organization with IAM policies
B) Individual project-level IAM roles
C) VPC Service Controls
D) Cloud Armor

Answer: A

Explanation:

Centralized management of identities and permissions is best achieved by establishing an organizational structure that encompasses all projects within a company. A Cloud Organization provides a top-level hierarchy that enables uniform policies to be applied across all associated projects. With this structure, administrators can enforce consistent identity and access management rules, audit permissions, and streamline governance. By applying IAM policies at the organization level, roles can be inherited by underlying projects and resources, reducing the likelihood of misconfigured permissions and enhancing security posture. It also simplifies operational overhead by allowing administrators to manage policies centrally rather than on a per-project basis.

Using IAM roles only at the individual project level is less efficient for multi-project management. Each project would need to be managed separately, leading to potential inconsistencies and a higher chance of errors. While project-level roles can be effective for small-scale deployments, they do not scale well for organizations with numerous projects, making it difficult to enforce consistent access policies across the enterprise.

Network perimeter controls such as VPC Service Controls are designed to restrict data exfiltration and access from outside trusted networks. They do not manage user identity or permissions within cloud projects. While important for securing sensitive data, VPC Service Controls cannot replace IAM for centralized access management across multiple projects and resources.

Security policies such as Cloud Armor protect applications against external attacks and enforce traffic-based rules. Although useful for mitigating network threats and application-layer attacks, Cloud Armor is not designed to control user access or identity across cloud resources. It operates at the application or network level and does not address centralized identity management.

The correct approach uses a Cloud Organization hierarchy combined with IAM policies applied at the organization or folder level. This ensures uniformity, scalability, and auditability across all projects. It reduces the operational burden by eliminating repetitive configuration, provides clear inheritance of roles, and improves compliance with security standards. It is the recommended solution for enterprise-scale identity and access governance in Google Cloud.

Question 23

A company wants to store large volumes of unstructured data, such as images, videos, and logs, and ensure low-latency access for globally distributed users. Which service is most appropriate?

A) Cloud Storage with multi-region buckets
B) Cloud SQL with large tables
C) Firestore in Native mode
D) BigQuery

Answer: A

Explanation:

Storing large volumes of unstructured data requires a system optimized for durability, scalability, and performance. Cloud Storage provides a highly available object storage solution that can handle massive datasets without manual infrastructure management. By creating multi-region buckets, data is automatically replicated across multiple regions, providing resilience against regional failures and ensuring low-latency access for users worldwide. The service supports high throughput for uploads and downloads and integrates with Content Delivery Network services for optimized global distribution. It is cost-effective for storing various formats of unstructured data, such as media, logs, or backups, providing both flexibility and reliability.

Relational databases such as Cloud SQL are optimized for structured, transactional data. While they can store binary large objects, performance and cost degrade significantly as data volume increases. SQL databases require scaling operations, replication management, and backups, which add operational complexity. They are not suitable for large-scale global access or highly unstructured datasets, making them inefficient for this use case.

NoSQL databases like Firestore are designed for structured, document-oriented data with strong consistency and low latency for structured queries. They are excellent for transactional workloads or real-time application data, but are not optimized for storing large blobs of binary or multimedia data. Using Firestore for unstructured data storage at scale would be inefficient and costly, and it would not provide built-in multi-region replication optimized for media distribution.

Analytical warehouses like BigQuery are designed for structured and semi-structured data optimized for fast queries on large datasets. BigQuery is not intended for serving raw unstructured files like images or video. While it can ingest metadata about these objects, the actual storage and retrieval of large unstructured files are better suited to object storage systems.

Using Cloud Storage with multi-region replication provides automatic redundancy, scalability, global low-latency access, and integration with other Google Cloud services such as AI and analytics. It ensures data durability, accessibility, and cost efficiency while offloading operational management. This design meets the requirements for storing unstructured data and providing fast global access for users.

Question 24

A security team needs to enforce that all new Compute Engine instances in a project must have a specific set of labels for cost tracking. Which mechanism should be implemented?

A) Organization Policy constraints for required labels
B) Manual auditing of instance creation
C) VPC firewall rules
D) Cloud Armor policies

Answer: A

Explanation:

Enforcing organizational rules across cloud resources is best done using policy mechanisms built into the cloud platform. Organization Policy constraints allow administrators to define mandatory conditions, such as requiring specific labels on all newly created Compute Engine instances. When these constraints are applied, any creation request that does not meet the labeling requirements is automatically rejected. This ensures consistent metadata usage for cost tracking, compliance, and operational governance, eliminating reliance on manual enforcement and reducing human error. Using automated policies ensures that all resources adhere to organizational standards from the moment they are created.

Manual auditing is reactive rather than proactive. While reviewing instances periodically can help identify compliance gaps, it does not prevent instances from being created without required labels. This method introduces operational delays, additional effort, and the risk of inconsistent enforcement, making it less reliable than automated policy enforcement.

Firewall rules control network access by defining which sources and destinations can communicate. While critical for security, they do not govern metadata attributes like labels or ensure organizational compliance. Using firewalls to enforce labeling requirements is not technically feasible and does not address the intended governance objectives.

Cloud Armor policies protect applications against network attacks and provide traffic filtering based on IPs or layer 7 rules. They do not influence the creation or metadata of compute resources. Attempting to use Cloud Armor for enforcing labeling would be ineffective, as it is unrelated to resource configuration or metadata governance.

Organization Policy constraints provide proactive, automated enforcement of required labels, ensuring consistency, compliance, and accurate cost tracking. They reduce operational burden by preventing misconfigurations, providing clear feedback to users when policies are violated, and maintaining governance standards across all projects. This approach guarantees that all instances meet labeling requirements from creation, providing both accountability and operational efficiency.

Question 25

A company wants to schedule automated backups of Cloud SQL databases to prevent data loss. Which solution should be implemented?

A) Enable automated backups in Cloud SQL settings
B) Create manual SQL dumps and store in Cloud Storage
C) Use Cloud SQL replicas only
D) Use Cloud Spanner instead

Answer: A

Explanation:

Protecting critical relational data requires a solution that ensures consistent, automated, and reliable backups. Cloud SQL provides a built-in mechanism to schedule automated backups. Enabling this feature allows the service to create full daily backups at a specified time without user intervention. These backups are stored in durable storage managed by the platform, ensuring that even in the event of accidental deletion, corruption, or failure, data can be restored to a known state. Administrators can configure backup retention policies, automated failover support, and point-in-time recovery to maintain business continuity. Automated backups reduce operational overhead by removing the need for manual intervention while guaranteeing data availability, consistency, and compliance with internal or regulatory standards.

Creating manual SQL dumps and storing them in Cloud Storage is technically feasible, but it introduces operational risks. Manual processes are prone to human error, omissions, and scheduling inconsistencies. Additionally, managing storage lifecycle, retention, and ensuring consistency across multiple databases adds complexity. This approach requires continuous monitoring and operational overhead, which contradicts the goal of a fully managed automated solution for backup and recovery.

Using read replicas alone does not provide a true backup solution. Replicas are intended to distribute read workloads and provide high availability for operational continuity, but they replicate live data and do not protect against deletion, corruption, or accidental changes. A replica that receives an unintended modification will propagate that change, offering no recovery path. Thus, relying solely on replicas is insufficient for data protection and backup purposes.

Migrating to a different database service, such as Cloud Spanner, could provide managed scaling and high availability, but it does not directly solve the problem for existing Cloud SQL workloads. Re-platforming databases introduces complexity, operational effort, and potential application refactoring. It is not a practical solution for scheduling automated backups in the context of current Cloud SQL deployments.

The recommended approach is to enable automated backups in Cloud SQL. This feature provides consistent, reliable, and manageable protection of relational data, including support for retention periods and point-in-time recovery. It removes operational burden from administrators, ensures compliance with recovery objectives, and integrates seamlessly with Cloud SQL high availability configurations. By using automated backups, the organization ensures that all production and critical data is protected against accidental deletion, corruption, or infrastructure failure. This approach is aligned with best practices for database resilience and disaster recovery planning.

Question 26

A developer needs to run event-driven serverless code that responds to object creation in Cloud Storage. Which service should be used?

A) Cloud Functions
B) Compute Engine
C) App Engine Standard Environment
D) Cloud SQL

Answer: A

Explanation:

Event-driven workloads require a system that automatically responds to triggers from cloud services without requiring dedicated servers or infrastructure management. Cloud Functions is a serverless compute solution that integrates with a variety of Google Cloud event sources, including Cloud Storage. When an object is created or modified in a bucket, a function can be invoked automatically. This allows developers to write lightweight code that executes only in response to specific events, scaling automatically to handle variable workloads. Billing is based on execution time and resources consumed, reducing costs compared to always-on compute instances. This event-driven design eliminates operational overhead, simplifies scaling, and enables responsive automation across cloud environments.

Compute Engine provides full control over virtual machines, but it is not serverless and cannot automatically respond to Cloud Storage events without additional components. To implement event-driven behavior on Compute Engine, developers would need to create polling or message-processing logic, maintain uptime, and manage scaling manually. This adds operational complexity and cost, making it less suitable for lightweight serverless event handling.

App Engine Standard Environment can run web applications and background tasks, but integrating event triggers from Cloud Storage requires additional infrastructure, such as Pub/Sub topics or HTTP endpoints. While App Engine supports scaling and automation, it is primarily intended for request-driven web applications rather than direct event responses to object storage. The integration complexity makes it less ideal for immediate event-driven automation.

Cloud SQL is a relational database service and does not execute code or respond to events. While it can store metadata or application data, it is not a compute platform and cannot act as a trigger-based serverless system. Using it in this context would not solve the problem of responding to object creation events.

The correct solution is to use Cloud Functions, which natively supports triggers for Cloud Storage events. It provides automatic scaling, precise execution on demand, minimal operational overhead, and integration with other cloud services such as Pub/Sub, Firestore, and BigQuery. Cloud Functions enables developers to implement real-time workflows, automation, and processing pipelines that execute only when events occur. This ensures efficient resource usage, cost savings, and immediate response to storage events. Leveraging Cloud Functions allows teams to build responsive and serverless solutions aligned with modern event-driven architectures, achieving operational efficiency and reliability.

Question 27

Your organization needs to allow an on-premises application to securely call Google APIs without storing long-lived service account keys. Which solution should be used?

A) Workload Identity Federation
B) OAuth 2.0 for end users
C) Cloud SQL IAM authentication
D) Cloud CDN signed URLs

Answer: A

Explanation:

Workload Identity Federation allows workloads running outside Google Cloud to assume short-lived credentials from a trusted identity provider. By mapping the external identity to a service account, the workload can authenticate to Google APIs without storing long-lived service account keys locally. This reduces the risk of credential exposure while ensuring access to required APIs. It also supports standard identity protocols such as SAML, OIDC, or AWS STS, enabling seamless integration with on-premises systems, hybrid environments, or other cloud providers. Tokens issued are short-lived and automatically rotated, adhering to security best practices and minimizing operational overhead.

Using OAuth 2.0 for end users is intended for interactive applications where human users grant consent. While it provides token-based access to APIs, it is not suitable for automated service-to-service interactions, such as on-premises applications requiring programmatic access. User-based authentication introduces additional complexity, requires human interaction, and does not provide the automation needed for non-interactive workloads.

Authenticating to Cloud SQL using IAM-based credentials is specific to database connections. It allows applications to connect to Cloud SQL without storing passwords, but does not provide general API access for external workloads. Using this method would not allow secure authentication to other Google Cloud services beyond the database environment.

Cloud CDN signed URLs are used to provide controlled access to content delivered from caches at edge locations. While they can restrict access to specific resources or time periods, they do not provide general authentication to Google Cloud APIs. Signed URLs are designed for content delivery security, not service-to-service identity federation or token issuance.

The correct approach is Workload Identity Federation, which allows secure, keyless authentication for external workloads. It provides temporary credentials mapped to Google Cloud service accounts, enforces least-privilege access, and eliminates the need to store long-lived keys on-premises. This approach enhances security, reduces operational risk, and ensures compliance with credential management best practices. It also enables hybrid and multi-cloud architectures to securely access Google APIs, maintaining centralized control and auditability while minimizing exposure of sensitive credentials.

Question 28

A company wants to prevent accidental deletion of storage buckets containing critical logs and backups. Which configuration should be implemented?

A) Enable bucket lock with retention policy in Cloud Storage
B) Set bucket to regional location
C) Apply IAM role Owner to all users
D) Enable versioning without retention

Answer: A

Explanation:

Preventing accidental deletion of critical storage resources requires a mechanism that enforces immutability and retention policies. Cloud Storage provides a feature called bucket lock, which, when combined with a retention policy, prevents objects from being deleted or modified for a specified period. This ensures that once critical data, such as logs or backups, are written to the bucket, they remain immutable until the retention period expires. Bucket lock applies retention enforcement at the bucket level and cannot be overridden by any user, including administrators, providing a strong safeguard against accidental or malicious deletions. The combination of retention policy and bucket lock guarantees that compliance, regulatory, and audit requirements for data preservation are met, reducing operational risk and maintaining the integrity of critical storage.

Selecting a regional location ensures data is stored within a specific region, providing redundancy within that region, but does not prevent deletion. Regional buckets improve latency and availability but do not enforce immutability. Without additional retention enforcement, users with proper permissions can delete data at any time, making this option insufficient for preventing accidental deletions of critical resources.

Assigning owner roles to all users increases rather than reduces risk. Owner permissions allow full access, including the ability to delete buckets and objects. Granting such privileges broadly is counterproductive for data protection, as it removes safeguards against both accidental and intentional deletion. This approach would increase the likelihood of data loss rather than prevent it.

Enabling versioning allows old versions of objects to be preserved when new versions are written, providing a recovery mechanism for updates or overwrites. However, versioning alone does not prevent object or bucket deletion. Users can still delete all versions, removing access entirely. Without a retention policy or bucket lock, versioning does not ensure immutability or compliance enforcement, leaving critical data vulnerable.

The proper solution combines versioning with a retention policy enforced by bucket lock. This prevents deletion or modification of stored objects for a defined period, ensuring long-term preservation and compliance. It allows organizations to retain critical logs and backups reliably, minimizing the risk of data loss due to human error or administrative missteps. Bucket lock enforces immutability at the system level, and retention policies define the duration for which the data is preserved. Using this approach provides both operational safety and regulatory compliance. For organizations handling sensitive or regulated data, enabling bucket lock with a defined retention policy in Cloud Storage is the recommended practice for preventing accidental deletion of critical resources.

Question 29

Your company wants to enable centralized logging for multiple projects and automatically route logs to BigQuery for analysis. Which solution should be implemented?

A) Create log sinks for each project with BigQuery as the destination
B) Manually export logs from the console
C) Use Cloud Monitoring dashboards only
D) Enable Cloud Storage as the sole log destination

Answer: A

Explanation:

Centralized logging across multiple projects requires an automated, scalable, and consistent mechanism for collecting, storing, and analyzing logs. Creating log sinks for each project allows logs to be automatically exported to a specified destination. By selecting BigQuery as the destination, all logs are routed directly into a data warehouse optimized for analysis and querying. Log sinks can filter which logs to export based on severity, resource type, or labels, enabling organizations to manage the volume of logs and ensure only relevant information is stored. This setup eliminates manual intervention, ensures consistency, and allows real-time analysis of operational and security data across all projects, supporting compliance, auditing, and troubleshooting requirements.

Manually exporting logs from the console is labor-intensive and error-prone. It does not scale for organizations with multiple projects or large volumes of logs. Manual export introduces delays, inconsistencies, and risks of missing data. It also requires constant human effort, which increases operational overhead and reduces the reliability of centralized logging for analytics purposes.

Using Cloud Monitoring dashboards alone provides visualization and alerting, but dashboards do not provide a permanent, queryable repository for log data. They are designed for operational monitoring and metrics, not for long-term storage and analytics across multiple projects. Dashboards cannot replace the need for structured storage in BigQuery, where complex queries and data analysis can be performed efficiently.

Storing logs solely in Cloud Storage provides durable storage but lacks the native analytical capabilities of BigQuery. Logs would be stored as raw objects requiring additional processing to make them usable for analysis. While Cloud Storage is excellent for archival purposes, it is not optimized for querying, reporting, or performing analytics at scale. Organizations needing operational insights from logs would face additional operational overhead to extract, transform, and load the data into an analytical platform.

The correct solution is to create log sinks for each project and export logs directly to BigQuery. This approach provides automatic, centralized log collection and ensures that logs are available in a structured, queryable format for analysis. It supports scalable, cross-project operations, real-time insights, and operational visibility while reducing manual effort. Filters on the sink allow fine-grained control over which logs are stored, optimizing storage costs and analytical efficiency. Using log sinks with BigQuery as the destination ensures reliable, consistent, and centralized logging suitable for auditing, monitoring, and analytics.

Question 30

A company needs to deploy a microservices application with independent scaling, monitoring, and zero-downtime updates. Which Google Cloud architecture is most suitable?

A) Kubernetes Engine with multiple Deployments and services
B) Single Compute Engine VM with all services installed
C) App Engine Standard with monolithic deployment
D) Cloud Functions with all logic in one function

Answer: A

Explanation:

Microservices architectures require a deployment platform that supports independent scaling, monitoring, and lifecycle management for each service. Kubernetes Engine allows each microservice to run in its own container, organized into separate Deployments and exposed via Services. Each Deployment can be scaled independently based on traffic or resource metrics, ensuring efficient utilization. Health checks, readiness probes, and rolling updates allow zero-downtime deployments, maintaining continuous service availability. Observability is integrated through metrics, logging, and monitoring for each service, enabling proactive management and fault isolation. Kubernetes Engine provides orchestration, networking, security, and automated recovery, which are essential for microservices running in production environments.

Running all services on a single Compute Engine VM creates a monolithic environment that cannot scale individual components independently. Resource contention, difficulty in deploying updates, and failure of one service impacting the entire system make this approach unsuitable for microservices. Zero-downtime updates are particularly challenging, as the VM must be updated carefully, and scaling is limited by the underlying hardware.

Deploying a monolithic application in App Engine Standard Environment limits flexibility for individual microservices. While App Engine manages scaling automatically, a single application package reduces the ability to scale services independently and complicates monitoring. Updating one component without affecting others may require deploying the entire application, risking downtime or regression.

Using Cloud Functions with all logic in one function centralizes processing, which contradicts the microservices principle. Each function is designed for event-driven, stateless workloads and is not intended to host multiple tightly coupled services. This architecture cannot provide independent scaling, health management, or update isolation across multiple services.

The correct architecture uses Kubernetes Engine to orchestrate multiple Deployments, each representing a microservice. Independent scaling, automated rolling updates, observability, and service isolation ensure high reliability, operational efficiency, and adherence to microservices principles. It allows teams to maintain independent lifecycles for each component while supporting high availability and minimal operational overhead. This setup is highly suitable for deploying complex, production-grade microservices applications in a cloud-native environment.