Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 9 Q121-135
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Question 121
A company wants to store large volumes of archival data with infrequent access and lowest cost. Which storage class should be used?
A) Archive Storage
B) Standard Storage
C) Nearline Storage
D) Coldline Storage
Answer: A
Explanation:
Storing large volumes of archival data with infrequent access requires a storage solution optimized for long-term retention, minimal access frequency, and the lowest possible storage cost. Google Cloud Archive Storage is designed specifically for data that is rarely accessed but must be retained for extended periods, such as regulatory archives, historical logs, or backup datasets. Archive Storage provides extremely low cost per gigabyte while maintaining the same high durability and reliability as other Cloud Storage classes, with automatic redundancy across multiple locations depending on regional or multi-regional configuration. Data stored in Archive Storage is encrypted at rest by default, with support for customer-managed encryption keys (CMEK) through Cloud KMS, ensuring regulatory compliance and enterprise control over sensitive archival data. Access control is enforced via IAM policies, bucket-level permissions, and signed URLs to securely manage read and write operations. Though retrieval times are slightly higher than Standard or Nearline Storage, the service is suitable for infrequent access scenarios, and the cost savings outweigh latency considerations for archival workloads. Lifecycle management policies allow automatic transition of data from other storage classes, such as Standard or Nearline, to Archive Storage after a defined period, enabling seamless, automated data retention strategies without manual intervention. Logging and monitoring through Cloud Logging and Cloud Monitoring allow tracking of access requests, storage utilization, and operational events, supporting auditability and compliance. Standard Storage is intended for frequently accessed data, providing low-latency access but at a higher storage cost, making it unsuitable for infrequently accessed archival data. Nearline Storage is optimized for data accessed roughly once per month and is more expensive than Archive Storage for long-term retention. Coldline Storage is suitable for infrequent access, roughly once per quarter, but it still carries higher retrieval costs and latency compared to Archive Storage. Archive Storage supports immutability policies and retention locks, making it ideal for compliance-driven use cases where data cannot be altered or deleted before a specified period. Organizations can use Archive Storage for historical datasets, compliance records, long-term backups, or audit logs, ensuring durable, secure, and cost-effective storage. It integrates with BigQuery for analytical queries on historical data, Dataflow for batch processing, and Storage Transfer Service for automated migration from on-premises or other cloud systems. The combination of extremely low cost, high durability, encryption, access control, and lifecycle automation makes Archive Storage the optimal solution for storing large volumes of infrequently accessed data in Google Cloud. Enterprises can implement retention policies, automate transitions between storage classes, and maintain compliance with minimal operational overhead. Archive Storage ensures reliable access when needed, supports disaster recovery strategies, and provides a secure, cost-efficient solution for long-term archival storage. By leveraging Archive Storage, organizations achieve predictable costs, simplified management, high durability, and compliance readiness for infrequently accessed datasets, making it the ideal choice for archival storage in Google Cloud.
Question 122
A company wants to implement identity federation for single sign-on to Google Cloud without creating separate Google accounts. Which service should be used?
A) Cloud Identity
B) Cloud KMS
C) Cloud IAM
D) Cloud Functions
Answer: A
Explanation:
Implementing identity federation for single sign-on (SSO) to Google Cloud without creating separate Google accounts requires a service that manages identities, authentication, and access integration with external identity providers. Cloud Identity is a cloud-based identity management service that enables organizations to securely manage users, devices, and groups while providing SSO and identity federation. Cloud Identity supports SAML 2.0 and OpenID Connect protocols, allowing enterprises to integrate Google Cloud access with existing identity providers such as Active Directory, Okta, or Ping Identity. With identity federation, users can authenticate using their corporate credentials without requiring separate Google accounts, streamlining access management and reducing administrative overhead. IAM integration allows assigning fine-grained access policies to federated users, ensuring that only authorized personnel can access specific resources, projects, or services. Cloud Identity also supports multi-factor authentication, device management, context-aware access, and audit logging, enhancing security and compliance for enterprise environments. Organizations can manage users and groups centrally, provision access to Google Cloud resources automatically, and enforce security policies consistently. Cloud KMS manages encryption keys and does not handle user authentication or identity federation. Cloud IAM provides access control and permissions, but requires user identities to exist in Google Cloud, so it does not provide federation by itself. Cloud Functions is a serverless compute platform and does not provide identity management or authentication capabilities. Cloud Identity enables enterprises to achieve secure, centralized identity management, reducing password fatigue, improving security posture, and simplifying administration. Federated users can access Google Cloud projects, services, and resources seamlessly while the organization retains control over authentication, credential policies, and account lifecycle management. Integration with Security Assertion Markup Language (SAML) or OpenID Connect ensures compatibility with widely used enterprise identity providers, allowing users to log in with corporate credentials while maintaining compliance and security standards. Audit logging provides visibility into authentication attempts, access patterns, and user activity, supporting regulatory compliance and operational oversight. Administrators can create policies for group-based access, automatic provisioning and deprovisioning, and enforce context-aware access based on location, device, or risk profile. Cloud Identity provides reporting dashboards for user activity, device compliance, and application usage, enabling enterprises to monitor access patterns and detect anomalies. By leveraging Cloud Identity, organizations can simplify access management, provide secure SSO, and enforce enterprise-grade security policies while integrating seamlessly with Google Cloud services. It enables operational efficiency, consistent user experience, and centralized control over identities and access without requiring separate Google accounts. Cloud Identity is the recommended solution for implementing identity federation, single sign-on, and secure enterprise access to Google Cloud resources, providing scalability, security, and compliance while reducing administrative overhead.
Question 123
A company wants to run a batch analytics workflow on large datasets using Hadoop and Spark without managing infrastructure. Which service should be used?
A) Cloud Dataproc
B) BigQuery
C) Cloud SQL
D) Cloud Spanner
Answer: A
Explanation:
Running a batch analytics workflow on large datasets using Hadoop and Spark without managing infrastructure requires a managed service that abstracts cluster provisioning, scaling, and maintenance while supporting distributed data processing. Cloud Dataproc is a fully managed Hadoop and Spark service that allows enterprises to run large-scale batch analytics, ETL, and machine learning workflows efficiently. Dataproc automatically provisions clusters, manages resources, handles scaling, and integrates with Google Cloud services such as Cloud Storage, BigQuery, Pub/Sub, and AI/ML platforms. Users can define cluster configurations based on workload requirements, including node types, autoscaling policies, and preemptible instances for cost optimization. Cloud Dataproc supports job scheduling, workflow templates, and integration with Cloud Composer for orchestrated pipelines. Logging, monitoring, and metrics collection through Cloud Logging and Cloud Monitoring provide operational visibility, error tracking, and performance optimization for analytics workflows. Security is enforced through IAM policies, VPC integration, and Kerberos support for cluster authentication, ensuring data and workflow security. BigQuery is a serverless data warehouse optimized for analytical queries, but it does not support Hadoop or Spark natively for batch processing. Cloud SQL and Cloud Spanner are relational databases and are not suitable for distributed batch analytics with Hadoop or Spark. Using Cloud Dataproc, organizations can execute ETL jobs, transform raw datasets, perform machine learning preprocessing, and run large-scale analytical computations without managing clusters manually. Autoscaling ensures that compute resources are allocated dynamically based on workload demand, reducing cost and operational complexity. Integration with Cloud Storage enables data ingestion and output storage, while connectors to BigQuery allow analytical querying and reporting. Dataproc’s flexibility allows hybrid approaches where Spark jobs read from Cloud Storage, process data, and store intermediate results for further analysis or machine learning pipelines. By leveraging Cloud Dataproc, enterprises gain the benefits of fully managed cluster operations, simplified workflow management, integration with the Google Cloud ecosystem, secure processing, and operational efficiency. The service allows reproducible, scalable, and cost-effective batch analytics with Hadoop and Spark, enabling rapid data transformation, aggregation, and analysis for large datasets. Cloud Dataproc reduces manual cluster management, ensures high availability, provides operational monitoring, and enables secure, distributed computation for enterprise analytics workflows. Its integration with workflow orchestration, autoscaling, and preemptible nodes allows enterprises to optimize performance and cost simultaneously.
Question 124
A company wants to serve dynamic web content globally with automatic failover. Which service combination should be used?
A) Cloud Load Balancing and Cloud CDN
B) Cloud Functions and Cloud Storage
C) App Engine and Cloud SQL
D) Cloud Spanner and Pub/Sub
Answer: A
Explanation:
Serving dynamic web content globally with automatic failover requires a combination of services that can distribute traffic, provide low-latency responses, and maintain high availability. Cloud Load Balancing, combined with Cloud CDN, is designed to achieve these goals. Cloud Load Balancing distributes incoming traffic across multiple backend services or instances, supporting global deployment and ensuring high availability by automatically rerouting traffic to healthy backends in case of failures. Integration with Cloud CDN enables caching of content at edge locations, reducing latency for users worldwide and offloading repeated requests from origin servers. Cloud Load Balancing supports HTTP(S), SSL, TCP/UDP, and global anycast IP addressing, ensuring traffic is efficiently routed based on proximity, capacity, and health of backends. Cloud CDN enhances performance for dynamic and static content by caching partial responses, supporting custom cache keys, and integrating with HTTPS for secure content delivery. Logging and monitoring through Cloud Logging and Cloud Monitoring provide operational insights into traffic patterns, backend health, latency, and cache performance. IAM integration ensures secure access and management of load balancers and CDN configurations. Cloud Functions and Cloud Storage can serve content, but do not provide global load balancing with failover. App Engine and Cloud SQL handle dynamic web applications but require additional global load balancing configuration to achieve high availability across regions. Cloud Spanner and Pub/Sub do not provide content delivery or web traffic management capabilities. Using Cloud Load Balancing and Cloud CDN, enterprises can ensure their dynamic web applications are served efficiently, securely, and reliably worldwide. Automatic failover guarantees uninterrupted service in the event of regional outages, while edge caching improves response times for users. The combination supports traffic splitting, scaling, SSL termination, and observability, enabling enterprises to deliver consistent performance and high availability for global users.
Question 125
A company wants to run containerized applications with automated scaling and integrated monitoring. Which service should be used?
A) Kubernetes Engine
B) Cloud Run
C) Compute Engine
D) App Engine
Answer: A
Explanation:
Running containerized applications with automated scaling and integrated monitoring requires a managed container orchestration platform that handles deployments, scaling, health checks, and observability. Google Kubernetes Engine (GKE) is a fully managed Kubernetes service that allows organizations to run containerized workloads efficiently. GKE automatically provisions clusters, manages nodes, and orchestrates container deployments while supporting autoscaling for nodes and pods based on CPU, memory, or custom metrics. Integrated monitoring through Cloud Monitoring and Logging allows enterprises to observe container performance, resource utilization, and application metrics, providing operational visibility. GKE supports rolling updates, rollbacks, health checks, and service discovery, ensuring resilient, high-availability deployments. Security is enforced through IAM, private clusters, VPC-native networking, network policies, and secrets management. Cloud Run supports serverless containers but is designed for stateless, HTTP-triggered workloads. Compute Engine requires manual cluster and container management. App Engine Standard Environment is suitable for web applications but is less flexible for containerized workloads. GKE provides fine-grained control, workload isolation, orchestration, automated scaling, and monitoring integration, making it the preferred solution for containerized applications requiring resilience, observability, and scalable operations in Google Cloud. Enterprises can deploy microservices, batch jobs, or stateful applications with simplified operational overhead, ensuring predictable performance, scalability, and high availability.
Question 126
A company wants to store relational data with global consistency, horizontal scaling, and high availability. Which service should be used?
A) Cloud Spanner
B) Cloud SQL
C) BigQuery
D) Cloud Datastore
Answer: A
Explanation:
Storing relational data with global consistency, horizontal scaling, and high availability requires a service designed for distributed workloads that need strong transactional guarantees and multi-region support. Cloud Spanner is a fully managed, relational, horizontally scalable database that provides strong consistency across global deployments. It combines the benefits of relational databases with the horizontal scalability of NoSQL systems. Cloud Spanner automatically handles replication across regions, providing high availability and durability even in the event of regional failures. Data is stored in multiple geographically distributed locations with synchronous replication to maintain consistency, ensuring ACID transactions across nodes and regions. Cloud Spanner supports standard SQL queries, indexing, and transactions, allowing enterprises to migrate existing relational workloads without rewriting application logic. Integration with IAM provides fine-grained access control, while audit logging and monitoring via Cloud Logging and Cloud Monitoring allow operational oversight and compliance. Cloud Spanner supports automatic scaling, adjusting compute and storage resources to meet workload demand without downtime, making it suitable for growing applications or workloads with unpredictable traffic patterns. It also supports change streams, backups, and point-in-time recovery, ensuring data protection and enabling disaster recovery strategies. Cloud SQL, while fully managed and relational, is limited to regional deployments and does not provide global consistency or seamless horizontal scaling. BigQuery is designed for analytical and batch workloads, not transactional relational databases. Cloud Datastore is a NoSQL database optimized for document-based data and does not support full relational queries or ACID transactions across multiple regions. By using Cloud Spanner, enterprises can achieve a globally consistent, highly available, and scalable relational database solution capable of supporting mission-critical transactional applications. Cloud Spanner’s combination of distributed architecture, strong consistency, SQL compatibility, automated scaling, high availability, and integration with Google Cloud services provides operational simplicity and reduces the complexity of managing globally distributed relational data. The service supports hybrid workloads, seamless schema evolution, and multiple concurrency models, ensuring reliability and performance for enterprise applications. Cloud Spanner’s automatic replication, failover, and backup capabilities enable organizations to maintain data durability and availability without manual intervention or complex clustering configurations. By leveraging Cloud Spanner, enterprises gain a globally consistent, scalable, and resilient relational database platform, ideal for financial systems, e-commerce applications, and any use case requiring transactional integrity and global reach. Operational visibility, security, and cost optimization are enhanced through integrated monitoring, IAM, and scaling policies, ensuring that organizations can manage critical workloads efficiently while maintaining compliance, reliability, and high performance. Cloud Spanner eliminates the limitations of regional relational databases and provides an enterprise-ready solution for global, mission-critical applications requiring transactional consistency, high availability, and horizontal scalability.
Question 127
A company wants to process large datasets in a serverless environment using SQL queries. Which service should be used?
A) BigQuery
B) Cloud Dataproc
C) Cloud SQL
D) Cloud Storage
Answer: A
Explanation:
Processing large datasets in a serverless environment using SQL queries requires a data warehouse service optimized for analytical workloads, scalability, and ease of use without infrastructure management. BigQuery is a fully managed, serverless, petabyte-scale data warehouse that allows organizations to perform SQL-based analytics on massive datasets. BigQuery separates storage and compute, enabling independent scaling and fast query execution. Data can be ingested from Cloud Storage, streaming sources, or other Google Cloud services and queried using ANSI SQL syntax. BigQuery handles optimization, indexing, partitioning, and caching automatically, enabling high-performance analytics without manual tuning. Access control is managed via IAM, ensuring secure, role-based access to datasets, tables, and projects. Logging and monitoring via Cloud Logging and Cloud Monitoring allow operational oversight, query performance analysis, and cost management. BigQuery supports federated queries, materialized views, and machine learning integration with BigQuery ML, enabling advanced analytics and predictive modeling directly within the warehouse. Cloud Dataproc allows processing large datasets using Hadoop or Spark, but is not purely serverless and requires cluster management and configuration. Cloud SQL provides managed relational databases for transactional workloads, but lacks the scalability for massive analytical datasets. Cloud Storage is an object storage service and does not provide SQL query capabilities or serverless analytics. BigQuery is designed to handle batch and streaming data, enabling organizations to perform analytics across terabytes or petabytes of data in a cost-effective, serverless manner. Queries can be executed on partitioned or clustered tables to improve performance, while storage and compute scale independently to handle varying workloads efficiently. Integration with BI tools such as Looker Studio, Data Studio, or third-party analytics platforms allows the creation of dashboards, visualizations, and reports from BigQuery datasets. BigQuery also provides audit logging, job monitoring, and cost control mechanisms, ensuring visibility into query usage and operational efficiency. The service supports real-time streaming ingestion for low-latency analytics, enabling enterprises to analyze data as it arrives while maintaining the benefits of serverless operation. By using BigQuery, organizations can eliminate infrastructure management, achieve massive scalability, and perform complex SQL-based analytics efficiently. The service supports secure, compliant, and auditable data processing, making it suitable for regulatory, financial, or operational analytics. With its fully managed architecture, BigQuery allows teams to focus on data analysis and insights rather than cluster management, indexing, or query optimization, while ensuring high performance, reliability, and cost-effective analytics at scale.
Question 128
A company wants to integrate multiple microservices with asynchronous communication and guaranteed message delivery. Which service should be used?
A) Pub/Sub
B) Cloud SQL
C) Cloud Functions
D) Cloud Storage
Answer: A
Explanation:
Integrating multiple microservices with asynchronous communication and guaranteed message delivery requires a messaging service designed for decoupled, event-driven architectures with reliable message handling. Google Cloud Pub/Sub is a fully managed, scalable publish-subscribe messaging service that enables microservices to communicate asynchronously, decoupling producers from consumers. Publishers send messages to topics, which are then delivered to subscribers with at-least-once delivery guarantees and optional message ordering. Pub/Sub automatically scales to accommodate high message throughput and supports retries, dead-letter topics, and acknowledgement mechanisms to ensure reliable delivery even under network disruptions or consumer failures. IAM integration ensures that only authorized publishers and subscribers can access topics, enforcing security and compliance. Logging and monitoring through Cloud Logging and Cloud Monitoring provide visibility into message flow, latency, and throughput, enabling operational oversight and troubleshooting. Cloud SQL provides relational database functionality but is not designed for event-driven messaging between microservices. Cloud Functions can execute triggered functions, but require integration with a messaging service for asynchronous communication. Cloud Storage stores objects but does not provide messaging or event guarantees. Using Pub/Sub, enterprises can implement loosely coupled microservices that communicate reliably, scale automatically, and respond to events in near real-time. Pub/Sub supports integration with Cloud Functions, Cloud Run, Dataflow, and other services, enabling event-driven processing, streaming analytics, and automated workflows. By decoupling message producers and consumers, Pub/Sub improves system resilience, fault tolerance, and operational simplicity. Features such as message filtering, batching, and flow control allow fine-grained performance optimization and cost management. Enterprises can implement complex workflows, process events from multiple sources, and ensure that critical messages are delivered reliably across distributed systems. Pub/Sub’s serverless nature eliminates infrastructure management, allowing teams to focus on application logic rather than message queuing, scaling, or failover. The service also supports hybrid and multi-cloud scenarios, ensuring seamless message delivery across different environments. By leveraging Pub/Sub, organizations gain a scalable, reliable, and fully managed messaging platform, enabling asynchronous microservice communication with guaranteed message delivery, operational visibility, and seamless integration with Google Cloud services. Pub/Sub is ideal for event-driven architectures, real-time analytics, workflow orchestration, and decoupled service communication, ensuring that enterprises can build resilient, scalable, and maintainable cloud-native applications.
Question 129
A company wants to analyze large datasets using SQL with real-time and batch queries without managing infrastructure. Which service should be used?
A) BigQuery
B) Cloud SQL
C) Cloud Dataproc
D) Cloud Spanner
Answer: A
Explanation:
Analyzing large datasets using SQL with real-time and batch queries in a serverless environment requires a data warehouse optimized for analytical processing and scalability without infrastructure management. BigQuery is a fully managed, serverless, petabyte-scale data warehouse that allows enterprises to query massive datasets efficiently using standard SQL syntax. BigQuery separates storage and compute resources, allowing independent scaling and fast query execution regardless of dataset size. Data can be ingested from Cloud Storage, Pub/Sub, or other sources, and queried in near real-time using streaming inserts or batch loads. BigQuery handles query optimization, indexing, partitioning, and caching automatically, removing the need for manual tuning or infrastructure management. Security is enforced through IAM roles, dataset-level permissions, and audit logging, ensuring controlled access and regulatory compliance. Logging and monitoring via Cloud Logging and Cloud Monitoring provide insights into query performance, resource utilization, cost, and operational metrics. BigQuery supports advanced features like materialized views, federated queries, geospatial analysis, and BigQuery ML for machine learning directly within the warehouse. Cloud SQL provides transactional relational databases but lacks scalability for massive analytical workloads. Cloud Dataproc allows processing with Hadoop or Spark, but requires cluster management and is not serverless. Cloud Spanner provides globally consistent transactional data storage but is not optimized for analytical queries on large datasets. By using BigQuery, enterprises can perform both batch and streaming analytics, implement real-time dashboards, generate insights from historical data, and integrate with BI tools like Looker Studio, all without managing infrastructure. BigQuery automatically scales resources to handle workload fluctuations, optimizing performance and cost. The service supports secure data sharing, auditability, and retention policies, enabling enterprise-grade analytics. Streaming data ingestion allows near real-time analytics, while batch processing enables large-scale historical analysis. Integration with Dataflow, Pub/Sub, and Cloud Storage allows preprocessing and transformation before queries. BigQuery also provides predictable, cost-efficient pricing through on-demand or flat-rate options, supporting budget management for analytics workloads. Operational monitoring, job history, and query performance insights enable optimization and troubleshooting, ensuring high efficiency. By leveraging BigQuery, organizations can implement fast, scalable, and fully managed analytics solutions that combine real-time and batch processing, simplify operational management, and provide actionable insights from large datasets, making it the optimal choice for serverless SQL analytics.
Question 130
A company wants to schedule serverless tasks to trigger HTTP endpoints at specific times. Which service should be used?
A) Cloud Scheduler
B) Cloud Functions
C) App Engine
D) Compute Engine
Answer: A
Explanation:
Scheduling serverless tasks to trigger HTTP endpoints at specific times requires a fully managed cron-like service capable of executing requests reliably, securely, and without infrastructure management. Cloud Scheduler is a serverless service that allows enterprises to define schedules for invoking HTTP endpoints, Pub/Sub topics, or Cloud Functions at precise intervals. It supports flexible cron expressions, time zones, and repeatable schedules to accommodate a variety of periodic tasks such as report generation, workflow orchestration, API polling, or automated maintenance. Cloud Scheduler integrates with IAM for secure task execution, ensuring only authorized users or service accounts can define or execute scheduled jobs. Retry policies and dead-letter topics ensure reliable delivery in case of transient failures. Logging and monitoring through Cloud Logging and Cloud Monitoring provide visibility into execution success, failures, latency, and operational patterns. Cloud Functions, App Engine, or Compute Engine could execute tasks but would require manual scheduling, infrastructure setup, or additional orchestration, increasing operational overhead. Cloud Scheduler allows enterprises to implement automated, serverless workflows without provisioning servers, managing cron jobs manually, or handling failures. It ensures high reliability, operational simplicity, and scalability, as schedules can trigger thousands of tasks without concern for underlying infrastructure limits. Enterprises can monitor execution history, errors, and latency for compliance, auditing, and troubleshooting purposes. Integration with Pub/Sub or Cloud Functions enables event-driven processing, enabling complex workflows or chained execution triggered by scheduled events. Cloud Scheduler is fully managed, serverless, and integrates seamlessly with Google Cloud services, ensuring secure, timely, and reliable execution of scheduled tasks. Organizations can automate repetitive workloads, maintain operational efficiency, reduce manual intervention, and focus on business logic, while Cloud Scheduler ensures dependable execution. It supports high availability, regional redundancy, and operational observability, making it the ideal solution for triggering serverless HTTP tasks on a schedule. By leveraging Cloud Scheduler, companies can implement predictable, secure, scalable, and fully managed task automation for HTTP endpoints, reducing operational complexity, ensuring reliability, and enabling seamless integration with event-driven or serverless workflows in Google Cloud.
Question 131
A company wants to deploy a stateless web application that scales automatically based on traffic and requires no server management. Which service should be used?
A) App Engine Standard Environment
B) Compute Engine
C) Kubernetes Engine
D) Cloud Functions
Answer: A
Explanation:
Deploying a stateless web application that scales automatically based on traffic and requires no server management requires a fully managed platform that abstracts infrastructure concerns while providing elasticity and high availability. App Engine Standard Environment is a serverless platform designed specifically for web applications, allowing enterprises to deploy stateless applications without managing underlying virtual machines, networking, or load balancing. App Engine automatically provisions instances, scales them up or down based on incoming HTTP requests, and ensures high availability across multiple zones without manual intervention. Applications can be deployed using supported languages such as Python, Java, Node.js, PHP, and Go, with runtime environments managed by Google Cloud. Security features include HTTPS support, IAM integration for access control, and traffic splitting between versions for gradual deployment or A/B testing. Logging and monitoring are provided via Cloud Logging and Cloud Monitoring, enabling tracking of request latency, error rates, instance performance, and scaling behavior. The service supports automatic version management, instance scaling, environment variables, and configuration management for consistent deployments. App Engine Standard Environment is ideal for stateless web applications where operational simplicity, zero infrastructure management, automatic scaling, and high availability are critical. Compute Engine provides virtual machines that require provisioning, scaling configuration, patching, and maintenance, making it unsuitable for serverless stateless applications. Kubernetes Engine allows container orchestration but requires cluster management, node configuration, and scaling setup, adding operational complexity. Cloud Functions is designed for event-driven, short-lived functions rather than full web applications with HTTP request routing, session management, or versioning. By using App Engine, enterprises can focus on application logic rather than operational concerns, allowing rapid deployment and iteration of web applications while ensuring elasticity, performance, and reliability. App Engine’s autoscaling mechanism adjusts resources dynamically based on traffic, reducing cost and ensuring responsive user experiences during traffic spikes. Integration with other Google Cloud services, such as Cloud SQL, Firestore, or Cloud Storage, allows seamless management of application data without managing infrastructure. The environment automatically handles load balancing, routing, and failover, ensuring continuous availability and fault tolerance. App Engine Standard Environment provides operational insights through built-in metrics and monitoring, enabling administrators to detect issues, optimize performance, and troubleshoot errors efficiently. Version management and traffic splitting support continuous delivery practices, allowing new features to be deployed safely without impacting users. Enterprise-grade security, compliance, and observability features allow organizations to meet regulatory requirements while simplifying application operations. By leveraging App Engine Standard Environment, companies can implement a fully managed, serverless, stateless web application platform that scales automatically with demand, ensures high availability, maintains security, provides operational visibility, and eliminates the complexity of infrastructure management. It enables teams to focus on developing business logic and user-facing features while Google Cloud manages scaling, load balancing, availability, and runtime management. This combination of zero server management, automated scaling, integrated monitoring, and security makes App Engine Standard Environment the ideal choice for stateless web applications.
Question 132
A company wants to migrate an on-premises MySQL database to Google Cloud with minimal downtime and automatic replication. Which service should be used?
A) Cloud SQL
B) Cloud Spanner
C) Cloud Bigtable
D) Cloud Datastore
Answer: A
Explanation:
Migrating an on-premises MySQL database to Google Cloud with minimal downtime and automatic replication requires a fully managed relational database that provides MySQL compatibility, high availability, and tools for continuous migration. Cloud SQL is a managed relational database service that supports MySQL, PostgreSQL, and SQL Server, allowing organizations to migrate existing databases without rewriting applications. Cloud SQL offers automated backups, failover, patch management, and read replicas, enabling high availability and minimal downtime during migration. The service integrates with Database Migration Service (DMS) to support continuous replication from on-premises MySQL to Cloud SQL, allowing near-zero downtime migration by synchronizing data in real time while the source database remains operational. Once replication is complete, a cutover allows applications to connect to Cloud SQL without significant disruption. Cloud SQL provides encryption at rest and in transit, IAM-based access control, and audit logging to ensure data security and compliance. Monitoring and logging through Cloud Logging and Cloud Monitoring provide visibility into database performance, query latency, replication status, and resource utilization, enabling proactive operational management. Cloud Spanner provides global transactional consistency and scalability, but requires schema and application changes for MySQL compatibility, and is not necessary for a simple MySQL migration. Cloud Bigtable is optimized for NoSQL workloads and time-series data, unsuitable for relational MySQL applications. Cloud Datastore is a NoSQL document database and cannot run relational workloads. By using Cloud SQL, enterprises can migrate MySQL databases efficiently while minimizing downtime and operational complexity. The combination of automated management, replication, backup, high availability, and monitoring ensures continuity of business operations. Read replicas can offload read-heavy workloads, improve performance, and support scaling during migration or production use. Integration with DMS allows seamless incremental data migration, reducing the risk of data loss or inconsistency. Cloud SQL supports vertical and horizontal scaling, ensuring resources meet workload demands without downtime. Enterprises can manage users, permissions, and connections securely while meeting regulatory requirements. Backup policies and point-in-time recovery provide additional protection against accidental data loss or corruption. Cloud SQL also integrates with Cloud Storage, BigQuery, and analytics tools for reporting, analysis, and data transformation, enabling a complete cloud-native workflow post-migration. Using Cloud SQL simplifies database operations, reduces administrative overhead, and accelerates cloud adoption. The service ensures reliable performance, secure access, operational monitoring, and cost-efficient scaling. By leveraging Cloud SQL, organizations achieve a fully managed, secure, and highly available MySQL migration path, maintaining transactional integrity and business continuity while eliminating the need to manage underlying infrastructure. It is the definitive solution for migrating MySQL databases with minimal downtime, automated replication, and operational simplicity in Google Cloud.
Question 133
A company wants to process streaming sensor data for real-time analytics and anomaly detection. Which service combination should be used?
A) Pub/Sub and Dataflow
B) Cloud Storage and BigQuery
C) Cloud SQL and App Engine
D) Cloud Functions and Cloud Spanner
Answer: A
Explanation:
Processing streaming sensor data for real-time analytics and anomaly detection requires a combination of services capable of ingesting high-throughput events, processing them with low latency, and outputting analytics results for dashboards or alerts. Pub/Sub and Dataflow together provide a complete managed solution. Pub/Sub serves as the messaging backbone, enabling IoT devices or sensors to publish messages asynchronously to topics. It supports high-throughput messaging, at-least-once delivery guarantees, message ordering, retries, and dead-letter topics to ensure reliable message transmission. Dataflow is a fully managed, serverless data processing service built on Apache Beam, capable of real-time stream processing, batch processing, and complex transformations. Dataflow can consume messages from Pub/Sub, perform filtering, aggregations, enrichments, or anomaly detection computations, and write results to sinks such as BigQuery, Cloud Storage, or dashboards. It supports event-time processing, windowing, triggers, and watermarking to handle late-arriving or out-of-order events accurately. Logging and monitoring through Cloud Logging and Cloud Monitoring provide insights into processing latency, message throughput, errors, and system performance. IAM ensures secure access to Pub/Sub topics and Dataflow pipelines. Cloud Storage and BigQuery are suited for batch analytics and storage, but cannot provide low-latency real-time processing. Cloud SQL and App Engine do not support distributed stream processing at scale, and Cloud Functions with Cloud Spanner are insufficient for high-throughput, low-latency event processing and complex analytics. Pub/Sub decouples producers and consumers, enabling fault-tolerant message delivery and system resilience. Dataflow’s serverless architecture ensures automatic scaling based on workload volume, maintaining consistent performance under variable traffic conditions. The combination enables near-real-time insights, anomaly detection, and predictive analytics by processing events as they arrive. Enterprises can implement alerts, dashboards, or trigger downstream workflows in response to detected anomalies. Integration with AI/ML services or Vertex AI allows predictive modeling or scoring using streaming data, creating responsive, intelligent systems. By leveraging Pub/Sub and Dataflow, organizations can implement fully managed, scalable, and resilient streaming analytics pipelines, ensuring reliable message delivery, operational simplicity, and actionable insights. This approach minimizes infrastructure management, maximizes data processing efficiency, and enables real-time responsiveness for operational decision-making, predictive maintenance, or sensor-driven applications. Pub/Sub and Dataflow provide a complete event-driven architecture for real-time analytics and anomaly detection with low latency, high reliability, and operational visibility, making them the ideal solution for streaming sensor data processing.
Question 134
A company wants to implement global HTTP(S) load balancing with automatic failover and integration with caching. Which service combination should be used?
A) Cloud Load Balancing and Cloud CDN
B) App Engine and Cloud SQL
C) Cloud Functions and Cloud Storage
D) Cloud Spanner and Pub/Sub
Answer: A
Explanation:
Implementing global HTTP(S) load balancing with automatic failover and caching requires a combination of services capable of distributing traffic globally, handling failures, and optimizing content delivery. Cloud Load Balancing, combined with Cloud CD, N, achieves these objectives. Cloud Load Balancing distributes incoming HTTP(S) traffic across multiple backend services or regions, supporting automatic failover when unhealthy backends are detected, ensuring continuous availability. Integration with Cloud CDN enables caching of content at Google’s edge locations worldwide, reducing latency for users and offloading repeated requests from origin servers. Cloud Load Balancing supports global anycast IP, SSL termination, traffic splitting, and health checks, while Cloud CDN provides cache invalidation, HTTPS support, and configurable caching behavior for dynamic or static content. Logging and monitoring via Cloud Logging and Cloud Monitoring allow operational visibility into traffic distribution, cache hit ratios, latency, and backend health. IAM policies ensure secure management of load balancers and CDN configuration. App Engine with Cloud SQL can host web applications, but does not provide global load balancing or caching at edge locations out of the box. Cloud Functions and Cloud Storage lack integrated global load balancing and caching capabilities. Cloud Spanner and Pub/Sub do not handle HTTP traffic distribution or content delivery. Using Cloud Load Balancing and Cloud CDN, enterprises achieve scalable, secure, and globally available web applications with optimized performance. Edge caching reduces latency, automatic failover ensures reliability, and integration with backend services supports dynamic content delivery. This combination is ideal for serving web applications, APIs, or content globally while maintaining high availability, resilience, and operational simplicity. It also supports monitoring, logging, and security features to manage performance, troubleshoot issues, and ensure compliance. Enterprises can deliver low-latency experiences worldwide, reduce infrastructure load, and implement resilient, scalable web services using this combination.
Question 135
A company wants to implement event-driven workflows triggered by Cloud Storage object changes. Which service should be used?
A) Cloud Functions
B) Cloud Run
C) Cloud SQL
D) App Engine
Answer: A
Explanation:
Implementing event-driven workflows triggered by Cloud Storage object changes requires a serverless service that can automatically respond to storage events without manual intervention or infrastructure management. Cloud Functions is a fully managed, event-driven compute service capable of executing small, stateless functions in response to events such as object creation, deletion, or metadata updates in Cloud Storage. When a file is uploaded or modified, Cloud Functions can automatically trigger processing tasks such as data transformation, ETL pipelines, image processing, notifications, or downstream workflows. Cloud Functions scales from zero to handle spikes in event volume, ensuring cost efficiency and responsiveness. Security is enforced through IAM policies, ensuring that only authorized functions can access specific buckets or resources. Cloud Logging and Cloud Monitoring provide operational visibility, including execution latency, errors, invocations, and performance metrics. Cloud Run or App Engine could host services, but do not natively respond to storage events. Cloud SQL is a relational database and cannot trigger workflows based on object changes. Cloud Functions allows enterprises to implement reactive, serverless architectures that process data in real time, automate pipelines, and integrate seamlessly with other Google Cloud services such as Pub/Sub, Dataflow, or BigQuery. Retry policies, error handling, and logging ensure reliable execution of workflows and observability for audit and compliance purposes. By leveraging Cloud Functions, organizations can build event-driven, serverless architectures that respond to Cloud Storage changes with minimal operational complexity, high scalability, and cost efficiency. It provides an ideal solution for real-time processing, automation, and integration of storage events into broader workflows and analytics pipelines, ensuring operational efficiency, reliability, and maintainability across cloud applications. Cloud Functions eliminates infrastructure management while enabling secure, scalable, and fully managed event-driven workflows triggered by Cloud Storage.