Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 7 Q91-105

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Question 91

A company needs to deploy a scalable web application with automatic scaling, traffic splitting, and version management. Which Google Cloud service should be used?

A) App Engine Standard Environment
B) Compute Engine
C) Cloud Run
D) Kubernetes Engine

Answer: A

Explanation:

Deploying a scalable web application with automatic scaling, traffic splitting, and version management requires a fully managed platform that abstracts infrastructure operations while providing operational flexibility for web applications. App Engine Standard Environment is a serverless platform that allows developers to deploy applications written in supported languages, such as Python, Java, Go, and Node.js. The platform automatically handles resource provisioning, load balancing, and scaling based on traffic, ensuring that applications can scale from zero to accommodate high traffic without manual intervention. Version management allows developers to deploy multiple versions of the application simultaneously and route a percentage of traffic to each version, facilitating canary releases, gradual rollouts, and A/B testing. App Engine integrates with Cloud Monitoring and Cloud Logging to provide observability, including request metrics, latency, error rates, and health checks. Security is managed through IAM, ensuring that only authorized users can deploy or manage application versions. The platform also provides built-in service isolation, automatic patching, and managed runtime updates, reducing operational overhead. By using App Engine Standard Environment, organizations can focus on application logic and features while Google Cloud manages the underlying infrastructure, scaling, traffic routing, and monitoring, providing a reliable and efficient environment for modern web applications.

Compute Engine provides virtual machines that can host web applications but requires manual provisioning, configuration, load balancing, scaling, and maintenance. Developers must manage patches, OS updates, and resource allocation, which increases operational complexity. While Compute Engine is flexible and suitable for custom configurations, it does not natively provide automatic scaling or version management, requiring additional services and management effort for traffic routing and deployments.

Cloud Run is a serverless platform for containerized workloads that can scale automatically based on request load. While it supports container deployments and can handle HTTP requests, it does not natively support multiple concurrent versions with traffic splitting. While traffic routing can be managed with Cloud Run revisions, App Engine Standard Environment offers more mature support for version management and built-in deployment workflows for web applications without requiring containerization.

Kubernetes Engine provides a managed Kubernetes cluster for orchestrating containerized applications. While GKE can deploy scalable web applications and handle traffic splitting using Ingress or service meshes, it requires managing clusters, deployments, and configurations, which increases operational complexity. Kubernetes Engine is better suited for complex microservices architectures rather than simple web application deployments with automatic scaling and version management.

App Engine Standard Environment is the correct solution because it provides a fully managed platform that supports automatic scaling, traffic splitting, version management, monitoring, and security. Developers can deploy multiple versions of the application, gradually route traffic to new versions, and implement canary testing without manual infrastructure configuration. The platform automatically scales resources based on request load, ensuring cost efficiency and reliability. Observability and logging are integrated for application performance monitoring and debugging. Security is enforced through IAM roles and service accounts, ensuring only authorized access to deployments. App Engine also abstracts patch management, runtime updates, and service isolation, reducing operational overhead and risk. By using App Engine Standard Environment, organizations can deploy reliable, scalable web applications while focusing on features, application logic, and user experience, without worrying about managing infrastructure, scaling policies, or traffic routing. Its serverless model provides high availability, automated scaling, integrated security, and version control for rapid development, continuous deployment, and operational efficiency in cloud-native web application environments.

Question 92

A company wants to analyze real-time streaming data and perform aggregations before storing results in BigQuery. Which service should be used?

A) Dataflow
B) Dataproc
C) Cloud SQL
D) Cloud Functions

Answer: A

Explanation:

Analyzing real-time streaming data and performing aggregations before storing results in BigQuery requires a fully managed stream processing platform that supports event-time processing, windowed computations, and integration with storage systems. Dataflow is a serverless data processing service based on Apache Beam that enables both batch and stream processing with unified programming models. Dataflow can ingest data from sources such as Pub/Sub, Cloud Storage, or external APIs, apply transformations, perform aggregations, windowing, and filtering, and write results to sinks such as BigQuery, Cloud Storage, or Cloud Pub/Sub. The service automatically manages resource provisioning, scaling, parallelization, and job monitoring, allowing developers to focus on processing logic instead of infrastructure management. Dataflow also supports event-time and processing-time windows, watermarks, and triggers, enabling accurate, real-time aggregations even when data arrives late or out of order. Integration with Cloud Monitoring and Cloud Logging provides observability into pipeline execution, throughput, and error tracking. Security is managed through IAM roles and service accounts, ensuring only authorized access to data and pipeline operations. Dataflow pipelines can be developed in Java or Python, allowing teams to reuse code across batch and streaming use cases. By using Dataflow, organizations can build real-time analytics pipelines that process and aggregate streaming data efficiently, reduce operational complexity, and deliver insights to downstream systems such as BigQuery for reporting, dashboards, or machine learning applications.

Dataproc is a managed Hadoop and Spark service designed for batch and distributed data processing. While it can process large datasets, it is not optimized for low-latency, real-time stream processing. Implementing real-time aggregations with Dataproc would require additional orchestration and operational overhead to manage clusters and job scheduling, making it less suitable for streaming scenarios.

Cloud SQL is a managed relational database service. While it can store structured data, it is not a stream processing platform and cannot efficiently perform real-time aggregations on high-velocity streaming data. Using Cloud SQL for streaming analytics would lead to performance bottlenecks and operational complexity.

Cloud Functions is a serverless compute service for event-driven workloads. While it can respond to events from Pub/Sub or Cloud Storage, it is not suitable for processing large-scale streaming data with complex aggregations, windowing, or joins. Functions are stateless and have execution time limits, making them impractical for long-running stream processing pipelines.

Dataflow is the correct solution because it provides a serverless, managed platform for both batch and streaming data processing. It handles resource provisioning, scaling, and execution efficiently, enabling organizations to focus on processing logic, aggregations, and transformations. Event-time processing, windowing, and watermarking allow accurate computation even with late-arriving data. Dataflow integrates seamlessly with BigQuery, Cloud Storage, and Pub/Sub, providing end-to-end pipelines for real-time analytics. Monitoring, logging, and IAM-based security ensure operational visibility, access control, and compliance. By using Dataflow, enterprises can implement real-time analytics pipelines that process high-volume streams efficiently, aggregate data, and store results for downstream analysis, visualization, and reporting. It reduces operational overhead, ensures scalability, and supports both batch and streaming workloads within a single programming model, making it the ideal choice for real-time streaming analytics and integration with BigQuery.

Question 93

A company wants to securely connect on-premises networks to Google Cloud with high availability and minimal latency. Which service should be used?

A) Cloud VPN with HA VPN
B) Cloud Storage
C) Cloud Functions
D) Cloud Pub/Sub

Answer: A

Explanation:

Securely connecting on-premises networks to Google Cloud with high availability and minimal latency requires a network service that provides encrypted tunnels, redundancy, and reliable connectivity. Cloud VPN with HA VPN is a fully managed solution that establishes IPsec tunnels between on-premises networks and Google Cloud Virtual Private Cloud (VPC) networks. HA VPN provides multiple tunnels across different Google Cloud regions to ensure high availability and redundancy. Traffic is automatically routed through the available tunnels in case of failure, minimizing downtime and ensuring business continuity. The service supports dynamic routing with Border Gateway Protocol (BGP), enabling automatic route advertisement and seamless integration with on-premises network topology. HA VPN uses strong encryption standards to secure data in transit between on-premises sites and Google Cloud. It also integrates with Cloud Monitoring and Cloud Logging to provide operational visibility, traffic metrics, and health status of tunnels. Cloud VPN is cost-effective for moderate bandwidth requirements and can scale horizontally by adding multiple tunnels. By using HA VPN, organizations can maintain secure, reliable, and low-latency connectivity between on-premises and Google Cloud, enabling hybrid architectures, migration projects, and multi-cloud integrations without compromising security or availability.

Cloud Storage is object storage and does not provide secure connectivity between on-premises networks and cloud resources. While it can store and retrieve data over the internet, it does not offer low-latency, high-availability network connectivity for hybrid workloads, making it unsuitable for secure network integration.

Cloud Functions is a serverless compute platform for event-driven tasks. It cannot establish secure network tunnels or manage on-premises connectivity. Functions are designed for stateless execution and event triggers rather than continuous network connectivity, making them unsuitable for hybrid cloud network solutions.

Cloud Pub/Sub is a messaging and event ingestion service. While it can facilitate asynchronous communication between on-premises systems and cloud applications, it does not provide secure IPsec tunnels, high availability, or low-latency connectivity for full network integration. It is ideal for messaging but not for establishing hybrid network infrastructure.

Cloud VPN with HA VPN is the correct solution because it provides secure, encrypted connectivity between on-premises networks and Google Cloud VPCs with high availability, redundancy, and dynamic routing. HA VPN automatically manages tunnel failover, integrates with BGP for route advertisement, and supports monitoring for operational visibility. Enterprises can implement hybrid architectures, replicate data, and migrate workloads while maintaining low-latency, reliable connectivity. The solution ensures secure transmission of sensitive data, seamless integration with existing network topologies, and automated failover to maintain business continuity. By using Cloud VPN with HA VPN, organizations can establish a robust and secure network bridge between on-premises infrastructure and Google Cloud, enabling hybrid deployments, compliance with security standards, and operational reliability without manual intervention, while minimizing latency and downtime. HA VPN allows enterprises to achieve high availability, automated routing, encrypted tunnels, and operational monitoring, ensuring secure and resilient hybrid network connections for critical workloads and enterprise applications.

Question 94

A company wants to implement role-based access control for Google Cloud resources across multiple projects and enforce the principle of least privilege. Which service should be used?

A) IAM
B) Cloud KMS
C) Cloud Identity
D) Cloud Functions

Answer: A

Explanation:

Implementing role-based access control (RBAC) for Google Cloud resources across multiple projects while enforcing the principle of least privilege requires a centralized, identity-based access management system. Identity and Access Management (IAM) is a service that allows organizations to define who (users, groups, or service accounts) can perform which actions on specific resources. IAM provides predefined roles, custom roles, and primitive roles to grant granular permissions based on job function or security requirements. Predefined roles cover common use cases such as Compute Admin or Storage Admin, reducing the risk of over-permissioning. Custom roles allow organizations to tailor permissions to specific workflows, ensuring that users have only the access necessary to perform their duties, which is the core principle of least privilege. IAM integrates with all Google Cloud resources, including Compute Engine, Cloud Storage, Cloud SQL, BigQuery, and Kubernetes Engine, providing centralized control and visibility across multiple projects. Policies can be applied at the organization, folder, or project level, supporting inheritance of permissions and simplifying management for large enterprises. IAM also supports conditions that enable dynamic access control based on attributes such as time, resource type, or network location, enhancing security while providing operational flexibility. Auditing and logging are integrated through Cloud Logging, allowing organizations to monitor access, detect anomalies, and ensure compliance with regulatory requirements. By using IAM, enterprises can enforce strict access controls, reduce the risk of unauthorized actions, maintain visibility into who accesses resources, and implement least-privilege access models consistently across all projects. IAM also supports service accounts for programmatic access, ensuring that automation and workloads can securely interact with Google Cloud services without exposing sensitive credentials. Organizations can combine IAM with organizational policies, resource hierarchy, and groups to centralize management, minimize human error, and enforce consistent access patterns. IAM integrates with Cloud Identity or external identity providers for single sign-on (SSO), allowing enterprises to manage users and roles efficiently. By defining roles, permissions, and policies strategically, organizations can implement secure, scalable, and compliant access control across all Google Cloud resources. IAM is fully managed, reduces operational complexity, and provides auditing, monitoring, and visibility for governance, enabling enterprises to implement robust RBAC strategies without manual oversight, ensuring secure and controlled access for all users and service accounts.IAM provides the necessary tools for defining roles, granting permissions, enforcing least privilege, integrating with organizational policies, and monitoring access, making it the correct choice for centralized, secure, and scalable access management across multiple projects in Google Cloud.

Cloud KMS is a key management service for encryption keys. While it provides encryption, key rotation, and key access control, it is not designed for RBAC across Google Cloud resources and cannot manage permissions for users or service accounts at the project or organization level.

Cloud Identity is a user and device management service. It provides identity verification, single sign-on, and account lifecycle management but does not enforce resource-specific roles and permissions within Google Cloud resources, making it insufficient for RBAC enforcement.

Cloud Functions is a serverless compute service. While functions can be secured using IAM roles, the service itself does not provide centralized management for resource permissions, and it cannot implement organization-wide RBAC or least-privilege access control.

IAM is the correct solution because it enables centralized role-based access control across Google Cloud resources. It allows fine-grained permission assignment, supports inheritance in the resource hierarchy, integrates with logging and auditing for compliance, and enforces least-privilege principles. By combining predefined, custom, and conditional roles, organizations can secure their cloud environment, ensure operational efficiency, and maintain visibility and governance across multiple projects. IAM supports both human and programmatic access, making it comprehensive for enterprise-scale cloud access management.

Question 95

A company wants to perform batch ETL operations from Cloud Storage to BigQuery on a daily schedule. Which service should be used?

A) Cloud Dataflow
B) Cloud Functions
C) Cloud Run
D) App Engine

Answer: A

Explanation:

Performing batch ETL (Extract, Transform, Load) operations from Cloud Storage to BigQuery on a daily schedule requires a fully managed service that can process data efficiently, apply transformations, and load it into a data warehouse. Cloud Dataflow is a serverless data processing service built on Apache Beam that supports batch and streaming data pipelines. Dataflow can read files from Cloud Storage, apply transformations such as filtering, aggregation, or enrichment, and write processed data into BigQuery. Batch pipelines can be scheduled using Cloud Scheduler to run daily, enabling automated ETL without manual intervention. Dataflow automatically handles resource provisioning, scaling, parallel execution, retries, and monitoring, ensuring reliable and efficient processing even for large datasets. It supports windowing, triggers, and watermarking to handle late or delayed data, though this is more relevant for streaming pipelines. Logging and monitoring are integrated through Cloud Monitoring and Cloud Logging, providing visibility into pipeline execution, performance, and errors. IAM integration ensures secure access to data sources and sinks while allowing pipelines to execute under service accounts. By using Dataflow, organizations can build repeatable, scalable, and maintainable ETL pipelines that transform raw data from Cloud Storage and load it into BigQuery for analytics and reporting, reducing operational complexity and increasing reliability.

Cloud Functions is a serverless compute service that can be triggered by Cloud Storage events. While it can perform lightweight ETL tasks, it is not suitable for large batch processing or complex transformations. Functions have execution time limits, and orchestrating multi-step ETL workflows would require significant custom logic, making it less efficient for daily large-scale batch operations.

Cloud Run allows deployment of containerized applications triggered by HTTP requests or Pub/Sub events. While it can execute ETL tasks, it requires additional orchestration and container management. Cloud Run is better suited for event-driven workloads rather than scheduled, large-scale batch processing with complex transformations.

App Engine can host web applications and background services. While App Engine supports cron jobs, it is not optimized for large-scale batch ETL processing, complex transformations, or integration with scalable data pipelines. Running large batch jobs on App Engine can be inefficient and may require additional infrastructure or scheduling logic.

Cloud Dataflow is the correct solution because it provides a fully managed platform for batch ETL pipelines, automatically handling resource allocation, parallel processing, and scaling. It supports complex transformations, logging, monitoring, and integration with BigQuery and Cloud Storage. By scheduling pipelines with Cloud Scheduler, organizations can automate daily batch ETL operations, ensuring reliable and timely data processing. Dataflow reduces operational complexity, scales efficiently for large datasets, and integrates with IAM for secure execution. It enables repeatable, maintainable pipelines with retries, error handling, and observability. Organizations can focus on designing transformations and business logic while Dataflow manages execution, scaling, and monitoring. Its serverless nature ensures cost efficiency, as resources are automatically provisioned and released based on workload. Dataflow’s integration with BigQuery allows seamless data loading for analytics, reporting, and dashboards, providing enterprises with a robust, automated solution for daily ETL operations from Cloud Storage to BigQuery. The service supports both batch and streaming workloads, making it versatile for future analytics requirements, and ensures high reliability, performance, and observability for large-scale data pipelines.

Question 96

A company wants to encrypt data at rest with customer-managed encryption keys. Which service should be used?

A) Cloud KMS
B) Cloud IAM
C) Cloud Storage
D) Cloud Functions

Answer: A

Explanation:

Encrypting data at rest with customer-managed encryption keys (CMEK) requires a service that allows organizations to generate, manage, rotate, and control access to encryption keys independently from Google-managed keys. Cloud Key Management Service (KMS) is a fully managed service that enables secure key creation, storage, rotation, and auditing. Organizations can create symmetric and asymmetric keys, define key versions, and control which users or service accounts can use keys to encrypt or decrypt data. CMEK enables organizations to maintain ownership of encryption keys while storing data in Google Cloud services like Cloud Storage, BigQuery, Cloud SQL, or Compute Engine. Using KMS, encryption and decryption operations can be integrated directly with supported services, ensuring that all data written to storage is encrypted using keys controlled by the organization. KMS supports audit logging through Cloud Logging, allowing monitoring of key usage, access attempts, and changes. Key rotation policies can be defined to comply with regulatory or security requirements. IAM integration enables fine-grained access control for key operations, supporting principle-of-least-privilege enforcement. By using Cloud KMS, enterprises retain control over their encryption keys, meet compliance requirements, and ensure that sensitive data is protected at rest while leveraging the scalability and reliability of Google Cloud services. CMEK also supports key revocation, destruction, and recovery, enabling organizations to manage data lifecycle securely. Cloud KMS is highly available, replicated across regions, and fully integrated with Google Cloud, reducing operational overhead while providing enterprise-grade security for encryption key management. By managing encryption keys independently, organizations can ensure that even if data is stored in Google-managed storage, the keys remain under customer control. This approach provides enhanced security, compliance alignment, and operational visibility into key usage and lifecycle management.

Cloud IAM is used for access control and permissions management. While IAM can restrict who can access resources or encryption keys, it does not provide mechanisms for key generation, rotation, or encryption operations for data at rest. IAM alone cannot implement CMEK.

Cloud Storage is an object storage service. While it supports encryption at rest, by default it uses Google-managed keys. To use CMEK, it must integrate with Cloud KMS. Storage alone cannot generate or manage encryption keys for customer-managed control.

Cloud Functions is a serverless compute service for event-driven workloads. It cannot generate or manage customer-managed encryption keys and is not used for encrypting data at rest. Functions can interact with KMS to perform encryption operations but cannot serve as a key management solution on its own.

Cloud KMS is the correct solution because it provides enterprise-grade key management for customer-managed encryption. It allows secure key creation, rotation, access control, and auditing, ensuring that sensitive data stored across Google Cloud services is encrypted with keys fully controlled by the organization. Integration with IAM and audit logging enables compliance and operational visibility. CMEK ensures data ownership, regulatory compliance, and enhanced security for at-rest encryption, making Cloud KMS the definitive choice for managing customer-controlled encryption keys. By using KMS, organizations maintain key ownership, enforce strict access controls, implement key rotation policies, monitor key usage, and secure sensitive data across cloud workloads while benefiting from Google Cloud’s scalability, high availability, and operational reliability. KMS enables secure key lifecycle management, regulatory compliance, and cryptographic control, making it essential for enterprises seeking customer-managed encryption at rest in Google Cloud.

Question 97

A company wants to monitor CPU usage, memory, and disk metrics for their Google Cloud virtual machines in real time. Which service should be used?

A) Cloud Monitoring
B) Cloud Logging
C) Cloud Trace
D) Cloud Functions

Answer: A

Explanation:

Monitoring CPU usage, memory, and disk metrics for Google Cloud virtual machines (VMs) in real time requires a service that collects, aggregates, and visualizes performance and operational metrics. Cloud Monitoring is a fully managed observability service that provides real-time visibility into the performance, uptime, and health of cloud infrastructure, applications, and services. It automatically collects metrics from Google Cloud resources, including Compute Engine VMs, disks, networking, and load balancers, providing insights into CPU utilization, memory consumption, disk I/O, and network throughput. Cloud Monitoring allows users to create dashboards to visualize key metrics, set alerts for threshold breaches, and integrate with incident management systems to respond proactively. Alerting policies can be defined to notify operations teams via email, SMS, or third-party integrations when resource usage exceeds predefined thresholds, helping prevent service degradation or downtime. Cloud Monitoring also supports custom metrics, enabling organizations to monitor application-specific or business-specific KPIs alongside system metrics. The service integrates with Cloud Logging, providing a complete observability solution where logs and metrics are correlated for debugging and performance analysis. Users can create uptime checks, monitor endpoints, and generate service-level objectives (SLOs) to measure compliance with operational goals. Cloud Monitoring is highly scalable, supporting real-time ingestion of millions of metric points from various resources, ensuring accurate and timely visibility into system health. By using Cloud Monitoring, organizations gain centralized visibility into virtual machine performance, resource utilization, and operational health across multiple projects and regions. It reduces operational complexity, improves reliability, and supports proactive incident management. Metrics are automatically collected, stored, and retained according to organizational policies, allowing historical analysis for capacity planning, trend detection, and optimization. Cloud Monitoring integrates with automation tools, enabling dynamic scaling, self-healing scripts, and predictive analytics based on real-time metrics. It also provides predefined dashboards, charts, and insights to simplify monitoring for new users and allows customization for complex environments. IAM integration ensures that only authorized personnel can view or modify monitoring configurations, maintaining operational security. Cloud Monitoring is essential for DevOps and SRE teams seeking to maintain high availability, optimize performance, and detect anomalies in virtual machines and other resources. It allows correlation between resource metrics, application performance, and business outcomes, supporting operational decision-making. Organizations can visualize CPU usage trends, memory allocation patterns, disk I/O bottlenecks, and network utilization, and use this information to optimize workloads, scale resources appropriately, and prevent service disruptions. By leveraging Cloud Monitoring, enterprises can proactively manage cloud infrastructure, improve system reliability, and enhance user experience while minimizing downtime, operational risk, and inefficiencies. The combination of real-time visibility, alerting, dashboards, custom metrics, and historical analysis makes Cloud Monitoring the definitive solution for monitoring Compute Engine VM metrics and overall cloud performance. Cloud Monitoring ensures operational efficiency, security, and resilience in large-scale cloud deployments, providing actionable insights that support continuous optimization and reliable operations.

Cloud Logging collects and stores logs from applications and system components. While it provides operational visibility into events and errors, it does not automatically visualize real-time performance metrics such as CPU usage or memory. Logging complements monitoring but cannot replace it for metrics-based alerting and dashboards.

Cloud Trace collects and analyzes latency data for distributed applications. It is designed to track request flows and performance bottlenecks, helping optimize application response times, but it does not monitor VM resource usage or system-level metrics.

Cloud Functions is a serverless compute service that executes event-driven workloads. It does not provide VM metrics monitoring or visualization capabilities and is unsuitable for real-time infrastructure observability.

Cloud Monitoring is the correct solution because it provides centralized, scalable, real-time collection, visualization, and alerting for VM metrics, including CPU, memory, and disk utilization. It integrates with logging, supports custom metrics, dashboards, SLOs, alerting, and operational automation, enabling proactive, efficient, and secure management of Google Cloud infrastructure. By using Cloud Monitoring, enterprises can ensure high availability, optimize performance, and maintain observability across multiple VMs, projects, and regions while reducing operational complexity and enhancing system reliability.

Question 98

A company wants to move on-premises virtual machines to Google Cloud with minimal downtime and no reconfiguration of applications. Which service should be used?

A) Migrate for Compute Engine
B) Cloud Storage
C) Cloud Functions
D) Cloud SQL

Answer: A

Explanation:

Moving on-premises virtual machines (VMs) to Google Cloud with minimal downtime and no reconfiguration of applications requires a lift-and-shift migration tool that can replicate entire VM environments seamlessly. Migrate for Compute Engine is a fully managed service that enables organizations to migrate physical servers, virtual machines, and cloud workloads to Google Cloud without changes to applications or operating systems. The service automates replication of disk data, network configuration, and VM metadata, ensuring that workloads can run in Google Cloud with minimal manual intervention. Continuous replication keeps source workloads synchronized with cloud instances, reducing downtime during the final cutover. Migrate for Compute Engine supports incremental replication, enabling organizations to test workloads in Google Cloud before production migration. The service also integrates with Compute Engine to provide native cloud resources, including machine types, network configurations, and storage, allowing VMs to run seamlessly in the cloud environment. Security is maintained through IAM roles, encryption of replicated data, and integration with Cloud Logging and Cloud Monitoring for operational visibility. By using Migrate for Compute Engine, enterprises can implement a lift-and-shift strategy, avoiding the need to re-architect applications, refactor code, or modify operating systems, reducing migration risk, complexity, and downtime. It supports hybrid environments during migration, allowing workloads to continue operating on-premises while replicating to Google Cloud. Automation features streamline the migration process, including network configuration mapping, storage provisioning, and VM replication orchestration. Organizations can perform staged migrations to minimize business disruption, validate workloads post-migration, and adjust performance settings in the cloud to optimize resources. By leveraging Migrate for Compute Engine, enterprises gain a reliable, secure, and efficient method for moving workloads, maintaining operational continuity, and avoiding service interruptions. It simplifies migration planning, testing, and execution, enabling IT teams to focus on validation and optimization rather than manual configuration. Cloud integration ensures that migrated workloads can take advantage of Google Cloud services, scaling, monitoring, and management features post-migration.

Cloud Storage is object storage and cannot migrate entire VM environments or maintain application runtime environments, operating systems, and network configurations.

Cloud Functions is a serverless compute service designed for event-driven workloads. It cannot replicate or migrate full VM environments and is not suitable for lift-and-shift migrations.

Cloud SQL is a managed relational database service. While it can host database workloads, it cannot migrate entire virtual machines or their application environments, disk storage, and network configuration.

Migrate for Compute Engine is the correct solution because it allows organizations to replicate and migrate entire VMs with minimal downtime, no application reconfiguration, and continuous synchronization. It provides native integration with Compute Engine, handles networking, storage, and OS replication, supports testing, and ensures secure, reliable migration. This service minimizes risk, reduces downtime, and allows enterprises to migrate workloads efficiently while maintaining business continuity and operational reliability during cloud adoption.

Question 99

A company wants to deploy containerized applications that scale automatically based on HTTP request load with minimal operational management. Which service should be used?

A) Cloud Run
B) Compute Engine
C) Kubernetes Engine
D) App Engine Standard Environment

Answer: A

Explanation:

Deploying containerized applications that scale automatically based on HTTP request load with minimal operational management requires a fully managed serverless platform optimized for containers. Cloud Run is a serverless compute service that allows organizations to run stateless containers directly without provisioning servers or managing clusters. Cloud Run automatically scales container instances based on incoming request load, ensuring that applications can handle sudden traffic spikes without manual intervention. Developers can deploy containers built from any language or runtime that supports HTTP endpoints, and scaling occurs to zero when no requests are present, optimizing cost efficiency. Cloud Run integrates with Cloud IAM for secure access control, enabling fine-grained permission management for who can deploy, invoke, or manage containers. Logging and monitoring are integrated with Cloud Logging and Cloud Monitoring, providing operational visibility into request latency, throughput, error rates, and container performance. Cloud Run supports revisions, allowing versioning of deployments and gradual traffic shifts between versions for canary releases or testing. Integration with Pub/Sub, Cloud Scheduler, and other Google Cloud services enables event-driven workflows, asynchronous processing, and automated triggers. By using Cloud Run, organizations can focus on application logic and container development rather than infrastructure management, benefiting from automatic scaling, load balancing, and high availability. It reduces operational complexity compared to Kubernetes Engine, which requires cluster management, node pools, and scaling configurations. Cloud Run provides rapid deployment, serverless scaling, pay-per-use pricing, and simplified operational management, making it ideal for containerized web applications, APIs, or microservices that require high responsiveness and low latency under variable traffic patterns. Security is enforced through IAM and HTTPS endpoints, while developers retain flexibility in container design and dependencies. Cloud Run supports stateless microservices architectures and can integrate with other services such as Cloud SQL or Firestore for stateful operations. The serverless nature ensures resources are consumed only when needed, reducing operational cost, management effort, and complexity associated with provisioning, scaling, or patching underlying infrastructure. Monitoring, logging, and autoscaling are fully managed, enabling enterprises to deploy scalable, reliable, and secure containerized applications without manual intervention. Cloud Run also supports private networking, VPC integration, and custom domains, providing flexibility and enterprise-grade security. By deploying containerized applications on Cloud Run, organizations achieve operational simplicity, elasticity, and efficiency while focusing on application development and delivering responsive, scalable services.

Compute Engine provides VMs but requires manual scaling and operational management, unsuitable for fully automated serverless scaling.

Kubernetes Engine provides container orchestration but requires cluster management, making it more complex than Cloud Run for simple HTTP scaling.

App Engine Standard Environment supports automatic scaling but is language-specific and less flexible for containerized workloads compared to Cloud Run.

Cloud Run is the correct solution because it provides serverless, fully managed container hosting with automatic scaling, security, monitoring, and minimal operational overhead, ideal for HTTP-based workloads that require elasticity, high availability, and cost efficiency.

Question 100

A company wants to store large amounts of unstructured data with lifecycle management, versioning, and high durability. Which Google Cloud service should be used?

A) Cloud Storage
B) Cloud SQL
C) BigQuery
D) Cloud Spanner

Answer: A

Explanation:

Storing large amounts of unstructured data with lifecycle management, versioning, and high durability requires a service designed for object storage. Cloud Storage is a fully managed, scalable object storage service optimized for storing and retrieving any amount of unstructured data, such as images, videos, backups, logs, and archives. It provides high durability, with data replicated automatically across multiple locations depending on the storage class, including multi-region, dual-region, and regional options. Cloud Storage supports object versioning, allowing organizations to maintain historical copies of files, recover deleted or overwritten objects, and manage data retention policies. Lifecycle management policies can be defined to automatically transition objects between storage classes or delete old objects, reducing storage costs and optimizing long-term data retention. Cloud Storage provides fine-grained access control using IAM and signed URLs, enabling secure data access at the object or bucket level. Integration with Cloud Monitoring and Cloud Logging allows organizations to monitor access, storage usage, and performance metrics. Cloud Storage supports multiple storage classes, including Standard, Nearline, Coldline, and Archive, allowing cost optimization based on access frequency. Encryption is provided by default for data at rest, and customer-managed encryption keys (CMEK) can be used for enhanced security. Cloud Storage also integrates with other Google Cloud services such as BigQuery for data analysis, Dataflow for processing, and AI/ML services for predictive analytics or content processing. The service provides global availability and low-latency access, enabling organizations to distribute data to multiple regions or edge locations. Cloud Storage is highly reliable, with built-in redundancy and durability guarantees of 99.999999999% (eleven nines) over a given year. Organizations can upload data via multiple methods, including the web console, CLI, API, or client libraries, ensuring flexibility and operational efficiency. Data immutability and retention policies can also be enforced to meet regulatory requirements such as HIPAA or GDPR. By using Cloud Storage, enterprises gain a scalable, secure, and cost-effective solution for managing large unstructured datasets with automated lifecycle management, high durability, and fine-grained access control. Cloud Storage reduces operational overhead by eliminating the need for hardware provisioning, redundancy management, and backup maintenance. It supports large-scale analytics and processing workflows while maintaining high availability and performance. Organizations can leverage object storage for backup, disaster recovery, archival, content distribution, or machine learning datasets, all with automated scalability and operational simplicity. Cloud Storage also provides integration with edge caching and content delivery networks (CDNs) to enhance performance and reduce latency for global users. Cloud Storage offers enterprises a comprehensive, highly available, and durable solution for storing unstructured data with lifecycle policies, versioning, and security, making it the definitive choice for scalable object storage in Google Cloud.

Cloud SQL is a managed relational database suitable for structured data. It is not designed for unstructured object storage or lifecycle management of large datasets, and scaling to petabyte levels is not cost-effective.

BigQuery is a serverless data warehouse optimized for analytical queries. While it can store structured and semi-structured data, it is not suitable for unstructured data storage or versioning.

Cloud Spanner is a globally distributed relational database. It is optimized for transactional data with ACID guarantees but is not intended for unstructured object storage or lifecycle management.

Cloud Storage is the correct solution because it provides highly durable, scalable, and secure object storage with lifecycle management, versioning, multiple storage classes, and enterprise-grade security, perfectly suited for large-scale unstructured data storage.

Question 101

A company wants to perform ad hoc SQL queries on petabyte-scale structured data without managing infrastructure. Which service should be used?

A) BigQuery
B) Cloud SQL
C) Cloud Spanner
D) Cloud Dataproc

Answer: A

Explanation:

Performing ad hoc SQL queries on petabyte-scale structured data without managing infrastructure requires a serverless analytical data warehouse optimized for large-scale querying. BigQuery is a fully managed, serverless data warehouse that allows enterprises to run interactive and batch SQL queries on structured and semi-structured datasets at a petabyte scale. BigQuery automatically handles storage, compute, scaling, and query optimization, eliminating the need to manage infrastructure, clusters, or database servers. The service provides columnar storage and a distributed architecture that enables rapid query execution over massive datasets. It supports standard SQL, partitioned and clustered tables, and materialized views to improve query performance and reduce costs. Billing can be pay-per-query or flat-rate, enabling cost management based on query volume or workload patterns. BigQuery integrates with Cloud Storage, Pub/Sub, Dataflow, and other Google Cloud services for data ingestion, ETL, and analytics workflows. Security is enforced through IAM, row-level and column-level access controls, and encryption at rest and in transit. Monitoring, logging, and audit capabilities provide operational visibility and compliance tracking. By using BigQuery, organizations can perform interactive ad hoc analysis, generate reports, and visualize data using tools like Looker or Data Studio without provisioning or scaling servers. BigQuery supports federated queries, allowing analysis across external datasets in Cloud Storage, Cloud SQL, or Google Drive, enabling a unified analytics experience. The platform can handle complex analytical operations such as joins, aggregations, and machine learning directly using BigQuery ML. Automatic scaling ensures queries complete efficiently regardless of dataset size, allowing analysts to focus on insights rather than infrastructure management. BigQuery also provides caching for repeated queries, query execution optimization, and dynamic resource allocation to reduce latency and cost. Its serverless nature ensures operational simplicity, reliability, and elasticity. Enterprises can leverage BigQuery for business intelligence, dashboards, predictive analytics, and large-scale reporting without manual intervention or cluster management. It supports data governance and auditing through access logs, query logs, and resource usage reports. By using BigQuery, organizations can accelerate decision-making, improve data-driven strategies, and handle petabyte-scale workloads efficiently with minimal operational overhead. The service reduces complexity, supports compliance, integrates with data processing pipelines, and provides enterprise-grade performance, security, and availability.BigQuery enables ad hoc querying of massive structured datasets at scale with serverless convenience, making it the definitive solution for analytics without infrastructure management.

Cloud SQL is a managed relational database for transactional workloads. It is not optimized for petabyte-scale analytics or serverless query execution.

Cloud Spanner is a globally distributed relational database designed for transactional consistency. It is not intended for analytical workloads on massive datasets.

Cloud Dataproc is a managed Hadoop and Spark service. It can process large datasets but requires cluster management and is not serverless.

BigQuery is the correct solution because it enables serverless, scalable, fast, and cost-efficient analytics on petabyte-scale structured datasets without infrastructure management.

Question 102

A company wants to process real-time IoT sensor data and detect anomalies. Which Google Cloud service should be used?

A) Dataflow
B) Cloud SQL
C) Cloud Storage
D) BigQuery

Answer: A

Explanation:

Processing real-time IoT sensor data and detecting anomalies requires a managed stream processing service that can handle high-throughput, low-latency data ingestion and transformations. Dataflow is a serverless data processing service built on Apache Beam that supports both batch and streaming pipelines, making it ideal for real-time analytics and anomaly detection. Dataflow can ingest data from Pub/Sub or IoT Core, apply transformations, windowing, aggregations, filtering, and anomaly detection logic, and route results to sinks such as BigQuery, Cloud Storage, or Cloud Pub/Sub for further processing or visualization. Stream processing in Dataflow supports event-time and processing-time semantics, watermarks, and triggers, ensuring accurate results even when data arrives late or out of order. It automatically manages resource provisioning, parallelization, and scaling, allowing pipelines to handle high-volume IoT data with minimal operational management. Logging and monitoring are integrated through Cloud Logging and Cloud Monitoring, enabling visibility into pipeline performance, latency, errors, and throughput. Security is enforced via IAM roles and service accounts, ensuring only authorized access to data and pipelines. Dataflow also supports integration with AI and ML frameworks, enabling real-time anomaly detection using trained models or statistical thresholds. By using Dataflow, organizations can implement real-time IoT analytics pipelines that detect anomalies, trigger alerts, and provide actionable insights, reducing downtime, improving operational efficiency, and supporting predictive maintenance. The serverless nature of Dataflow reduces infrastructure management, automatically scales resources based on workload, and ensures high reliability. Pipelines can be tested, debugged, and deployed with minimal disruption, allowing organizations to iterate rapidly on detection algorithms. Dataflow also supports hybrid batch and streaming workflows, enabling consistent analytics across historical and real-time data. The service provides operational resilience through automated retries, fault tolerance, and monitoring dashboards. Integration with Pub/Sub allows ingestion of IoT data at scale, while BigQuery enables long-term analytics and reporting of anomalies for trend analysis. Custom transformations and functions can be implemented in Python or Java, providing flexibility for specialized detection logic. By using Dataflow, enterprises gain a scalable, reliable, and fully managed platform to process IoT sensor data in real time, detect anomalies, trigger alerts, and feed downstream analytics and decision-making systems. The service supports high-throughput, low-latency processing and seamless integration with Google Cloud storage and analytics services, enabling enterprises to implement robust, real-time IoT data pipelines with minimal operational overhead. Cloud SQL cannot handle high-throughput real-time stream processing efficiently. Cloud Storage is an object store, not suitable for real-time stream processing.BigQuery is for analytics queries, not for real-time event processing and anomaly detection. Dataflow is the correct solution because it provides serverless, scalable stream processing with low latency, windowing, and integration with analytics and storage, ideal for IoT anomaly detection.

Question 103

A company wants to distribute a web application globally with low latency and high availability. Which Google Cloud service should be used?

A) Cloud CDN
B) Cloud Load Balancing
C) Cloud Functions
D) App Engine

Answer: A

Explanation:

Distributing a web application globally with low latency and high availability requires a service that caches content close to end-users at edge locations while integrating seamlessly with Google Cloud’s infrastructure. Cloud CDN (Content Delivery Network) is a fully managed service that caches HTTP(S) content at Google’s edge locations worldwide, reducing latency for users by serving content from the nearest location instead of the origin server. Cloud CDN works with backend services such as Compute Engine, Cloud Storage, Cloud Run, or App Engine, allowing developers to leverage caching for static assets, dynamic content, and API responses. By caching frequently accessed content, Cloud CDN reduces load on origin servers, improves responsiveness, and increases scalability during traffic spikes. The service also supports SSL/TLS, ensuring secure delivery of content over HTTPS, and integrates with Cloud Load Balancing to automatically route user requests to the optimal backend based on proximity, health, and capacity. Cloud CDN provides logging, monitoring, and analytics, enabling organizations to understand traffic patterns, cache hit ratios, and performance metrics. Custom cache control headers allow precise management of content expiration and invalidation, ensuring that users receive up-to-date content while minimizing unnecessary requests to origin servers. Edge caching improves user experience by delivering content with minimal latency, while origin shielding reduces repeated requests and operational costs. Cloud CDN also supports HTTP/2 and QUIC, enhancing performance and reducing latency for global users. Integration with IAM and security policies ensures that access and content delivery are compliant with organizational requirements. By distributing content globally, enterprises can achieve high availability, reduce downtime, and maintain consistent performance for users in different regions. Cloud CDN works with dynamic content by caching partial responses or using cache keys to differentiate users, allowing tailored content delivery while maintaining performance. Enterprises can monitor the performance of cached content, tune caching policies, and configure invalidation strategies for updates or critical content changes. By leveraging Cloud CDN, companies can achieve optimal performance, scalability, and security for web applications, ensuring fast, reliable, and globally available content delivery while minimizing backend load and operational overhead. Cloud Load Balancing alone can distribute traffic and provide high availability, but does not provide edge caching, meaning content still has to be served from origin locations, which may result in higher latency for distant users. Cloud Functions and App Engine provide compute capabilities but do not inherently deliver globally cached content. Therefore, Cloud CDN is the correct solution because it combines edge caching, global distribution, security, integration with Google Cloud backends, and analytics to provide low-latency, highly available web application delivery worldwide. The service enables organizations to serve millions of requests efficiently, optimize user experience, reduce infrastructure cost, and scale without complex infrastructure management while maintaining secure, reliable, and performant web applications. By using Cloud CDN, enterprises can ensure predictable, fast, and resilient content delivery to users globally while leveraging Google Cloud’s edge network and integrated management features for monitoring, logging, and configuration.

Question 104

A company wants to schedule serverless tasks to run at specific times daily without managing servers. Which service should be used?

A) Cloud Scheduler
B) Cloud Functions
C) Compute Engine
D) App Engine

Answer: A

Explanation:

Scheduling serverless tasks to run at specific times daily without managing servers requires a fully managed service that can trigger workloads reliably, on schedule, and integrate with other serverless services or APIs. Cloud Scheduler is a fully managed cron job service that allows organizations to automate the execution of serverless functions, HTTP endpoints, and Pub/Sub messages according to defined schedules. Tasks can be set to run at specific intervals, such as hourly, daily, or weekly, or at complex cron schedules to meet business requirements. Cloud Scheduler integrates seamlessly with Cloud Functions and Cloud Run, enabling serverless tasks to be executed without provisioning or managing infrastructure, ensuring cost efficiency and operational simplicity. IAM integration provides secure control over who can create, modify, or execute scheduled tasks, and audit logging allows tracking of job execution, failures, and retries. Cloud Scheduler handles retries with exponential backoff and supports failure notifications via Pub/Sub, email, or third-party integrations, ensuring reliability of scheduled workflows. By using Cloud Scheduler, organizations can automate maintenance tasks, ETL pipelines, data processing jobs, or report generation, eliminating manual intervention and minimizing operational overhead. The service provides timezone support, allowing scheduling based on regional business hours and accommodating daylight saving changes automatically. Cloud Scheduler ensures precise execution timing, scaling automatically to handle large numbers of scheduled tasks across projects and regions. It reduces risk by providing repeatable, automated workflows that follow best practices for serverless architecture, and it can trigger tasks to any HTTP endpoint, enabling hybrid integration with on-premises systems or external APIs. Cloud Functions or Cloud Run handle task execution, while Cloud Scheduler manages the orchestration and timing, enabling a fully serverless workflow with minimal operational complexity. Compute Engine or App Engine can execute scheduled tasks but require server management, scaling configuration, and resource monitoring, making them less suitable for fully serverless cron-like jobs. Cloud Scheduler abstracts infrastructure concerns, provides centralized scheduling, integrates with serverless compute services, and ensures reliable, secure, and cost-efficient execution of recurring tasks. Enterprises can use Cloud Scheduler to orchestrate complex pipelines, trigger batch jobs, and automate repetitive operations without managing servers, ensuring consistent execution, reducing human error, and improving productivity. The service provides observability into scheduled tasks, enabling debugging, auditing, and compliance tracking. By leveraging Cloud Scheduler, organizations can focus on business logic and automation rather than infrastructure management, achieving operational efficiency, reliability, and cost savings in their serverless workflows. Cloud Scheduler’s integration with IAM, Pub/Sub, and serverless services allows secure, scalable, and flexible automation for enterprises seeking precise control over recurring cloud tasks, making it the ideal solution for scheduling serverless tasks reliably without server management.

Question 105

A company wants to store relational transactional data that requires global consistency and high availability. Which service should be used?

A) Cloud Spanner
B) Cloud SQL
C) BigQuery
D) Cloud Firestore

Answer: A

Explanation:

Storing relational transactional data that requires global consistency and high availability requires a distributed database designed for transactional workloads with strong consistency guarantees. Cloud Spanner is a fully managed, horizontally scalable, relational database that provides ACID transactions, SQL support, and global consistency across regions. It automatically shards data across nodes and regions while ensuring transactional consistency, making it ideal for applications that require high availability, fault tolerance, and predictable performance worldwide. Cloud Spanner supports standard SQL queries, schema management, and indexing, enabling developers to leverage familiar relational database concepts while benefiting from global scalability. The service provides automatic replication, failover, and backup capabilities, ensuring high durability and availability even in the event of regional outages. Integration with IAM allows fine-grained access control and secure connections, while monitoring and logging provide operational visibility into performance, usage, and error trends. Cloud Spanner handles infrastructure management, including patching, scaling, replication, and recovery, allowing enterprises to focus on application development rather than database administration. By using Cloud Spanner, organizations can implement globally distributed, transactional applications such as financial systems, e-commerce platforms, or multi-region SaaS applications with strict consistency and reliability requirements. It supports multi-region configurations with synchronous replication for strong consistency and can scale compute and storage independently to accommodate growing workloads. Cloud Spanner ensures low-latency reads and writes through intelligent data placement and network optimization, providing consistent performance across regions. It also integrates with other Google Cloud services for analytics, backup, monitoring, and machine learning workflows. Cloud SQL provides managed relational databases but is limited to single-region deployments and cannot provide the same level of global consistency and horizontal scalability as Spanner. BigQuery is optimized for analytical workloads rather than transactional relational data and is not suitable for OLTP scenarios. Cloud Firestore is a NoSQL document database that provides high availability and real-time updates, but does not support relational transactions with full ACID guarantees at a global scale. Cloud Spanner is the correct solution because it delivers a fully managed, globally consistent relational database with ACID transactions, automatic replication, high availability, and horizontal scalability. It allows enterprises to deploy mission-critical transactional applications worldwide with minimal operational overhead while ensuring consistency, reliability, and fault tolerance, meeting stringent performance and uptime requirements for modern global applications.