Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 8 Q106-120
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Question 106
A company wants to ensure sensitive data stored in Cloud Storage is encrypted using keys they fully control. Which service should be used?
A) Cloud KMS
B) IAM
C) Cloud SQL
D) Cloud Functions
Answer: A
Explanation:
Ensuring sensitive data stored in Cloud Storage is encrypted using keys fully controlled by the company requires a service that provides encryption key creation, management, rotation, and access control independent of Google-managed keys. Cloud Key Management Service (KMS) is a fully managed service that allows organizations to create, store, rotate, and control encryption keys used to protect data across Google Cloud services. Cloud KMS supports symmetric and asymmetric keys, enabling encryption, decryption, signing, and verification operations. By using customer-managed encryption keys (CMEK) in Cloud Storage, organizations maintain full ownership of encryption keys, controlling who can use, manage, and revoke access. This provides a strong security posture, regulatory compliance, and protection against unauthorized access. Keys can be rotated on a schedule or manually to meet organizational policies or compliance requirements. Access to keys is controlled through IAM roles and service accounts, ensuring that only authorized users or applications can perform cryptographic operations. Cloud KMS integrates seamlessly with Cloud Storage, BigQuery, Cloud SQL, and other Google Cloud services, enabling enterprises to apply CMEK across multiple workloads consistently. Audit logging through Cloud Logging allows organizations to track key usage, monitor access, detect anomalies, and ensure compliance with security policies. Cloud KMS supports key versioning, allowing previous key versions to decrypt historical data while enforcing policies for new data encryption. By encrypting data with CMEK, organizations can meet regulatory requirements such as HIPAA, GDPR, and PCI-DSS while leveraging Google Cloud’s high availability and durability. Cloud KMS also supports key destruction policies, which allow secure deletion of keys no longer needed, and disaster recovery policies to ensure keys are protected in multi-region configurations. Integration with Cloud IAM enables fine-grained permission assignment, ensuring that least-privilege access is enforced across the organization. Cloud KMS is highly available, replicated, and resilient, allowing enterprises to rely on its secure infrastructure without managing physical key storage or replication manually. By using CMEK in conjunction with Cloud Storage, organizations can guarantee that data remains encrypted under keys they fully control, ensuring operational security, compliance, and auditability. Other solutions, like IAM, provide access control but cannot manage encryption keys or enforce CMEK. Cloud SQL is a managed database that supports encryption but requires integration with KMS to use customer-managed keys, and it is not designed for general object storage encryption. Cloud Functions can interact with KMS for encryption operations, but it cannot serve as a key management platform on its own. Cloud KMS is the definitive solution because it provides end-to-end management of encryption keys, integrates with multiple Google Cloud services, enforces security and access policies, supports key rotation, versioning, auditing, and ensures regulatory compliance. By using Cloud KMS, organizations maintain complete control over cryptographic keys while taking advantage of Google Cloud’s managed infrastructure, reliability, and performance. Enterprises can enforce encryption policies consistently across workloads, monitor key usage, prevent unauthorized access, rotate keys securely, and maintain a robust security posture for sensitive data stored in Cloud Storage, making Cloud KMS the correct choice for customer-controlled encryption at rest in Google Cloud.
Question 107
A company wants to deploy a multi-region relational database with horizontal scaling and strong consistency. Which service should be used?
A) Cloud Spanner
B) Cloud SQL
C) BigQuery
D) Cloud Firestore
Answer: A
Explanation:
Deploying a multi-region relational database with horizontal scaling and strong consistency requires a database service that provides a globally distributed architecture while maintaining ACID transactions. Cloud Spanner is a fully managed, horizontally scalable, relational database designed for transactional workloads that require global consistency and high availability. It automatically partitions data across nodes and regions while maintaining synchronous replication and strong consistency, ensuring that all clients see the same data regardless of their location. Spanner supports standard SQL for querying, schema management, and indexing, allowing developers to leverage familiar relational database concepts while benefiting from a distributed architecture. High availability is achieved through automatic replication, failover, and disaster recovery mechanisms, ensuring resilience in case of regional failures. Cloud Spanner handles scaling transparently by adding nodes or compute capacity without downtime, supporting high transaction throughput and large datasets. IAM integration provides fine-grained access control over database operations, while audit logging and monitoring via Cloud Logging and Cloud Monitoring enable operational visibility, anomaly detection, and compliance tracking. Spanner’s architecture supports global transactional workloads where multiple clients perform concurrent reads and writes without compromising consistency or performance. It is ideal for applications like financial systems, multi-region SaaS platforms, and e-commerce backends that require strict ACID guarantees and global low-latency access. Spanner provides automatic schema updates, online backups, and versioning, reducing operational overhead and simplifying database administration. Developers can configure read replicas for read-heavy workloads, optimizing query performance without affecting transactional consistency. It also integrates with other Google Cloud services such as BigQuery for analytics, Cloud Functions for triggers, and Dataflow for streaming or batch processing. Compared to Cloud SQL, which is a single-region managed relational database, Spanner can scale horizontally across multiple regions while maintaining strong consistency. BigQuery is optimized for analytical queries and is not suitable for transactional relational workloads. Cloud Firestore is a NoSQL document database providing high availability and low-latency reads, but does not offer full relational SQL support or global ACID transactions at the scale of Spanner. Cloud Spanner is the correct solution because it delivers a fully managed, globally distributed relational database that scales horizontally, ensures strong consistency, supports standard SQL, provides automatic replication and failover, integrates with IAM and observability tools, and reduces operational overhead. Enterprises can deploy mission-critical, globally distributed applications with high throughput, transactional integrity, and low-latency access across regions. Spanner allows organizations to scale elastically, maintain global consistency, perform online schema updates, and monitor database operations with minimal manual intervention. It provides a reliable, secure, and highly available platform for transactional workloads that need global reach and strong consistency, enabling modern applications to achieve scalability, performance, and resilience without compromising relational integrity or operational simplicity.
Question 108
A company wants to analyze large-scale historical datasets using SQL without provisioning infrastructure. Which service should be used?
A) BigQuery
B) Cloud SQL
C) Cloud Spanner
D) Cloud Dataproc
Answer: A
Explanation:
Analyzing large-scale historical datasets using SQL without provisioning infrastructure requires a fully managed, serverless, analytical data warehouse capable of handling petabyte-scale workloads efficiently. BigQuery is a serverless data warehouse that allows organizations to perform ad hoc and batch SQL queries on massive datasets without worrying about provisioning or managing servers. It automatically manages storage, compute, query optimization, scaling, and parallel execution, enabling rapid insights from large historical datasets. Data is stored in columnar format, allowing efficient compression and fast analytical queries. BigQuery supports partitioned and clustered tables, materialized views, and standard SQL for complex queries, aggregations, and joins. Integration with Cloud Storage, Cloud Pub/Sub, Dataflow, and other Google Cloud services enables easy data ingestion, ETL pipelines, and analytics workflows. Security is enforced through IAM roles, row-level and column-level access control, and encryption at rest and in transit. Query caching, automatic resource allocation, and cost controls such as slot reservations or flat-rate pricing allow efficient cost management. Logging and monitoring through Cloud Logging and Cloud Monitoring provide visibility into query execution, resource usage, and job performance. BigQuery supports federated queries, enabling analysis of data stored outside BigQuery, such as in Cloud Storage or Cloud SQL, without data movement. BigQuery ML allows building and training machine learning models directly on historical datasets without exporting data. Its serverless architecture ensures that organizations can scale analytics workloads seamlessly, even when dealing with multi-petabyte datasets, without managing infrastructure, clusters, or hardware. BigQuery enables enterprises to explore data, generate reports, dashboards, and insights, and support business intelligence and data-driven decision-making efficiently. Cloud SQL is designed for transactional workloads and cannot efficiently handle petabyte-scale analytical queries. Cloud Spanner provides globally consistent transactional databases but is not optimized for large-scale analytical queries. Cloud Dataproc is a managed Hadoop and Spark service that requires cluster provisioning and management, increasing operational complexity. BigQuery is the correct solution because it provides serverless, scalable, high-performance, and cost-efficient analytics on historical datasets, supports standard SQL, integrates with other Google Cloud services, ensures security and governance, and eliminates the need for infrastructure management. By using BigQuery, organizations can focus on data analysis and insights rather than cluster provisioning or maintenance, enabling rapid, reliable, and scalable analytics at enterprise scale.
Question 109
A company wants to run containerized applications triggered by HTTP requests with automatic scaling and no server management. Which service should be used?
A) Cloud Run
B) Compute Engine
C) Kubernetes Engine
D) App Engine
Answer: A
Explanation:
Running containerized applications triggered by HTTP requests with automatic scaling and no server management requires a serverless platform that abstracts infrastructure while providing rapid scaling, flexibility, and integration with Google Cloud services. Cloud Run is a fully managed serverless compute platform that allows developers to deploy stateless containers built from any language or runtime that respond to HTTP requests. Cloud Run automatically scales container instances from zero to handle traffic spikes and scales down to zero when no requests are present, optimizing cost efficiency and operational simplicity. Developers do not need to provision or manage servers, clusters, or virtual machines, as Cloud Run handles infrastructure management, load balancing, scaling, and networking automatically. Cloud Run integrates with Cloud IAM to enforce access control, ensuring that only authorized users can deploy, invoke, or manage containers, and integrates with Cloud Logging and Cloud Monitoring to provide observability into request latency, throughput, errors, and container performance. Revisions allow versioning of deployments, enabling gradual traffic shifts for canary releases or staged rollouts, and Cloud Run supports environment variables, secrets, and configuration management for secure application configuration. By using Cloud Run, enterprises can focus entirely on application logic, deploying APIs, microservices, or web applications in containers without managing clusters or servers. The platform supports containerized workflows integrated with other Google Cloud services, including Pub/Sub, Cloud Scheduler, and Cloud Tasks, enabling event-driven architectures or scheduled jobs. Unlike Compute Engine, which requires manual provisioning, scaling, and maintenance of VMs, Cloud Run abstracts all infrastructure management while automatically handling traffic spikes and providing elasticity. Kubernetes Engine offers container orchestration and scalability but requires cluster setup, node management, networking configuration, and operational maintenance, increasing complexity compared to Cloud Run. App Engine Standard Environment supports automatic scaling but is language-specific and less flexible for containerized workloads. Cloud Run is designed to provide serverless, stateless container execution with pay-per-use pricing, automatic scaling, high availability, and minimal operational overhead. Security is enforced via IAM, HTTPS endpoints, and VPC connectors for private networking. By leveraging Cloud Run, organizations can deploy microservices or APIs globally, respond to HTTP requests instantly, scale elastically based on demand, and reduce operational complexity and cost. Cloud Run also integrates with CI/CD pipelines for automated deployments, testing, and rollback, improving development velocity and operational reliability. Monitoring and alerting are built in, allowing proactive identification of performance bottlenecks or errors. Enterprises can use Cloud Run to implement modern cloud-native architectures without managing underlying infrastructure, focusing on innovation and delivering responsive applications. The service is suitable for APIs, lightweight web services, and stateless application components that require fast, elastic scaling, and global reach. Cloud Run’s combination of serverless execution, automatic scaling, container flexibility, and integration with Google Cloud services makes it the ideal solution for containerized applications triggered by HTTP requests, eliminating server management while maintaining security, reliability, and cost efficiency.
Question 110
A company wants to automate batch ETL jobs from Cloud Storage to BigQuery daily. Which service should be used?
A) Cloud Dataflow
B) Cloud Functions
C) App Engine
D) Cloud Run
Answer: A
Explanation:
Automating batch ETL jobs from Cloud Storage to BigQuery on a daily schedule requires a service that can efficiently process large datasets, transform them, and load them into a data warehouse with minimal operational overhead. Cloud Dataflow is a fully managed, serverless data processing service that allows organizations to build batch or streaming pipelines using Apache Beam. Dataflow pipelines can read raw data from Cloud Storage, apply transformations such as filtering, aggregation, and enrichment, and load processed data into BigQuery for analysis. Scheduling can be automated using Cloud Scheduler, triggering pipelines daily without manual intervention. Dataflow automatically handles resource provisioning, parallel processing, load balancing, and error handling, ensuring reliable ETL execution for large datasets. The service supports windowing, triggers, and watermarking for handling late-arriving data, making it suitable for both batch and streaming scenarios. Logging and monitoring through Cloud Logging and Cloud Monitoring provide visibility into pipeline performance, errors, and resource utilization, enabling operational oversight and troubleshooting. IAM integration ensures that only authorized users or service accounts can execute or modify pipelines, enforcing security and compliance. By using Dataflow, enterprises can implement scalable, repeatable, and maintainable ETL workflows, reducing operational complexity, improving reliability, and ensuring timely availability of data for analytics. Cloud Functions could be triggered by events, such as file uploads, but is not suitable for large-scale batch processing due to execution time limits and lack of advanced orchestration features. App Engine can host cron jobs but is not optimized for large-scale data processing, complex transformations, or integration with BigQuery at scale. Cloud Run can execute containerized workloads and scale automatically but requires orchestration and lacks the built-in ETL capabilities and optimizations for large batch jobs that Dataflow provides. Dataflow is specifically designed for high-throughput, parallelized, serverless data processing pipelines. It allows enterprises to focus on designing transformation logic and business rules while Dataflow handles execution, scaling, retries, and fault tolerance. By leveraging Dataflow, organizations can ensure reliable, efficient, and automated daily ETL operations from Cloud Storage to BigQuery, enabling analytics and reporting with minimal operational burden. Its serverless nature ensures cost efficiency, as resources are provisioned and released automatically based on workload requirements. Dataflow also supports integration with AI and ML frameworks for predictive analytics or anomaly detection as part of the pipeline, extending its utility beyond traditional ETL. The service ensures high availability, error recovery, and operational visibility, making it the preferred solution for automating large-scale batch ETL workflows.
Question 111
A company wants to provide real-time messaging between microservices with high throughput and low latency. Which service should be used?
A) Pub/Sub
B) Cloud SQL
C) Cloud Storage
D) Cloud Functions
Answer: A
Explanation:
Providing real-time messaging between microservices with high throughput and low latency requires a fully managed publish-subscribe messaging service designed to decouple applications and enable asynchronous communication. Google Cloud Pub/Sub is a globally distributed, scalable, and durable messaging service that allows applications to send and receive messages between independent systems reliably. Publishers send messages to topics, which are then delivered to subscribers in near real-time. Pub/Sub automatically scales to handle millions of messages per second while ensuring low-latency delivery and at-least-once message delivery guarantees. The service supports message ordering, acknowledgments, dead-letter queues, and retries to ensure reliable communication between microservices. IAM integration allows fine-grained access control, specifying which publishers or subscribers can interact with topics, enhancing security and governance. Pub/Sub can integrate with Cloud Functions, Cloud Run, and Dataflow to trigger processing pipelines, enabling serverless architectures and real-time data processing. Monitoring and logging provide operational visibility into message flow, delivery latency, and errors, allowing developers to optimize performance and ensure reliability. By decoupling producers and consumers, Pub/Sub improves system resilience, scalability, and maintainability, enabling microservices to operate independently without tight coupling or blocking dependencies. Cloud SQL and Cloud Storage are not messaging platforms; SQL databases handle transactional data and queries, while Cloud Storage stores objects but does not provide real-time message delivery. Cloud Functions can process events but cannot serve as a messaging bus with guaranteed high throughput and low latency between multiple microservices. Pub/Sub is designed specifically for real-time, asynchronous communication between services, providing fault tolerance, global distribution, scalability, and message persistence. By using Pub/Sub, enterprises can implement event-driven microservice architectures, real-time analytics pipelines, alerting systems, and workflow automation with minimal operational overhead, achieving high performance, reliability, and decoupling of system components. It ensures messages are delivered consistently and quickly, supports backpressure handling, and integrates with Google Cloud services to create flexible, event-driven workflows. Pub/Sub also enables batching and streaming of messages for efficient processing and cost optimization. By leveraging Pub/Sub, companies can focus on business logic rather than communication infrastructure, ensuring resilient, scalable, and responsive microservice interactions globally.
Question 112
A company wants to back up critical data from on-premises systems to Google Cloud with encryption at rest and high durability. Which service should be used?
A) Cloud Storage
B) Cloud SQL
C) Cloud Spanner
D) Cloud Functions
Answer: A
Explanation:
Backing up critical on-premises data to Google Cloud with encryption at rest and high durability requires a service designed for object storage with enterprise-grade reliability, security, and scalability. Cloud Storage is a fully managed, durable object storage service optimized for storing backups, archives, logs, and unstructured data. Data is automatically replicated across multiple locations depending on the chosen storage class, providing high durability and availability even in case of regional failures. Cloud Storage supports encryption at rest by default, including Google-managed keys, customer-supplied keys (CSEK), and customer-managed keys (CMEK) through Cloud KMS, allowing organizations to maintain control over encryption and meet regulatory compliance requirements such as HIPAA, GDPR, and PCI-DSS. Access control is enforced through IAM, bucket policies, and signed URLs, ensuring only authorized users and systems can read or write backup data. Cloud Storage offers multiple storage classes—Standard, Nearline, Coldline, and Archive—allowing cost optimization based on data access frequency and retention requirements. Lifecycle management policies enable automatic data transition between storage classes or deletion after a defined period, reducing operational overhead and storage costs. Cloud Storage integrates with transfer services and tools, such as Storage Transfer Service and gsutil, for seamless data migration from on-premises systems. It supports large object sizes, parallel uploads, resumable transfers, and robust error handling to ensure reliable backup operations. Monitoring and logging through Cloud Monitoring and Cloud Logging provide visibility into storage usage, access patterns, transfer success, and anomalies, supporting operational oversight and audit requirements. Cloud Storage also integrates with BigQuery, Dataflow, and AI/ML services, allowing analysis or processing of backup datasets if needed. High availability is achieved through automatic replication across zones or regions, ensuring backups are accessible when required. Cloud Storage also supports versioning, allowing restoration of previous object versions in case of accidental deletion or corruption, providing an additional safety layer. Backup automation can be implemented through Cloud Scheduler or scripts, enabling regular, repeatable, and reliable backups with minimal operational intervention. Cloud SQL and Cloud Spanner are database services and are suitable for transactional workloads but not for large-scale object storage or general-purpose backups of unstructured data. Cloud Functions is a serverless compute platform, not a storage solution, and cannot provide the durability, lifecycle management, or encryption required for backups. Cloud Storage is the definitive solution because it offers enterprise-grade durability, encryption options, global availability, scalable storage, lifecycle management, versioning, and seamless integration with Google Cloud services. It ensures that critical data from on-premises systems is safely stored, encrypted, and readily accessible for recovery, analysis, or migration. By using Cloud Storage, organizations gain operational simplicity, cost efficiency, regulatory compliance, and peace of mind that backup data is highly durable, secure, and manageable at scale. Enterprises can leverage Cloud Storage to maintain reliable backup workflows, implement disaster recovery strategies, and meet compliance requirements while eliminating the complexities of on-premises backup infrastructure. Cloud Storage’s combination of durability, security, flexibility, automation, and cost optimization makes it the ideal choice for storing critical backups with minimal operational effort.
Question 113
A company wants to create real-time dashboards from streaming sensor data. Which service combination should be used?
A) Pub/Sub and Dataflow
B) Cloud SQL and Cloud Storage
C) BigQuery and Cloud Functions
D) Cloud Spanner and App Engine
Answer: A
Explanation:
Creating real-time dashboards from streaming sensor data requires a combination of services capable of ingesting high-throughput events, processing and transforming the data in real time, and storing it for visualization or analytics. Google Cloud Pub/Sub and Dataflow provide a complete solution for building such real-time pipelines. Pub/Sub serves as the messaging backbone, allowing IoT devices, sensors, or applications to publish messages asynchronously to topics. It is highly scalable, supports low-latency delivery, at-least-once message guarantees, message ordering, and integration with other Google Cloud services. Dataflow, a fully managed serverless data processing service built on Apache Beam, consumes messages from Pub/Sub, applies transformations such as filtering, aggregations, enrichment, or anomaly detection, and writes processed results to sinks suitable for dashboards such as BigQuery, Cloud Storage, or other data visualization tools. Dataflow supports stream processing features such as windowing, triggers, and watermarks, ensuring accurate handling of late-arriving or out-of-order data. The combination of Pub/Sub and Dataflow allows for scalable, reliable, and fully managed real-time data pipelines without the need to provision or manage infrastructure. Logging and monitoring are available through Cloud Logging and Cloud Monitoring, providing operational visibility into message throughput, pipeline performance, and errors. IAM integration ensures secure access to both Pub/Sub topics and Dataflow pipelines. Once processed, data can be visualized in real-time dashboards using tools like Looker Studio or integrated into BI platforms for monitoring, alerting, or reporting. Pub/Sub decouples the data producers and consumers, improving system resiliency and fault tolerance. Dataflow’s serverless architecture ensures automatic scaling based on message volume and pipeline requirements, maintaining low latency and high throughput even during traffic spikes. Cloud SQL and Cloud Storage alone are not suitable for real-time processing; Cloud SQL is optimized for transactional workloads, while Cloud Storage is an object store without stream processing capabilities. BigQuery and Cloud Functions are suitable for batch analytics or event-driven tasks, but BigQuery does not handle real-time streaming at the ingestion layer efficiently for dashboards. Cloud Spanner and App Engine cannot process streaming data for real-time dashboards; they provide transactional and web application services rather than event stream processing. Using Pub/Sub with Dataflow, enterprises can implement end-to-end real-time analytics pipelines, ingesting sensor data, processing it immediately, and feeding dashboards or analytics platforms without manual intervention. This combination ensures scalability, reliability, operational simplicity, and flexibility to handle high-throughput, low-latency data streams. The architecture supports advanced features like windowed aggregations, anomaly detection, and alerting, enabling organizations to monitor IoT systems, business metrics, or operational KPIs in real time. Pub/Sub and Dataflow together form a serverless, fully managed, scalable, and reliable solution for real-time sensor data pipelines and dashboards. By leveraging these services, companies can focus on analytics and insights rather than infrastructure, ensuring timely, accurate, and actionable visualization of real-time data streams.
Question 114
A company wants to run a highly available, serverless web application with automatic scaling based on HTTP requests. Which service should be used?
A) App Engine Standard Environment
B) Compute Engine
C) Kubernetes Engine
D) Cloud Functions
Answer: A
Explanation:
Running a highly available, serverless web application that automatically scales based on HTTP requests requires a platform that abstracts infrastructure, provides elasticity, and integrates with Google Cloud services. App Engine Standard Environment is a fully managed, serverless platform that allows organizations to deploy web applications without managing servers, networking, or scaling configurations. It automatically scales instances up or down based on incoming HTTP traffic, ensuring optimal resource utilization, high availability, and responsiveness. Developers can deploy applications in supported languages such as Python, Java, Node.js, PHP, and Go, with runtime environments managed by Google Cloud. App Engine provides integrated security, including IAM roles, HTTPS support, and traffic splitting between versions for staged deployments. Built-in logging and monitoring allow tracking of request latency, error rates, throughput, and resource consumption, enabling operational visibility and troubleshooting. App Engine handles load balancing, scaling, and instance management transparently, eliminating the need for manual configuration or infrastructure management. It also supports environment variables, task queues, scheduled tasks, and integration with Cloud SQL, Firestore, or Cloud Storage for backend services. Compute Engine provides virtual machines but requires manual scaling and maintenance, making it less suitable for fully serverless applications. Kubernetes Engine enables container orchestration but requires cluster management, node configuration, and operational expertise. Cloud Functions can execute serverless event-driven workloads, but are designed for short-lived functions rather than full web applications requiring stateful routing, session handling, and HTTP request scaling. App Engine Standard Environment is optimized for web applications with a stateless architecture, automatic scaling, high availability, security, and seamless integration with Google Cloud services. Enterprises can deploy applications quickly, manage versions and traffic, and focus on application logic while App Engine handles operational concerns. The platform ensures low-latency response for end users, elastic scaling for variable workloads, and operational reliability across multiple regions. By using App Engine Standard Environment, organizations can achieve a fully managed, serverless, highly available web application environment, minimizing operational overhead while providing automatic scaling, integrated monitoring, security, and seamless cloud integration. It allows enterprises to focus on developing business logic and user-facing features while relying on Google Cloud to manage infrastructure, reliability, scaling, and maintenance, making it the optimal choice for serverless web applications.
Question 115
A company wants to migrate a legacy MySQL database to a fully managed, relational database in Google Cloud with minimal downtime. Which service should be used?
A) Cloud SQL
B) Cloud Spanner
C) BigQuery
D) Cloud Datastore
Answer: A
Explanation:
Migrating a legacy MySQL database to a fully managed, relational database in Google Cloud with minimal downtime requires a service that provides native MySQL compatibility, automated management, replication, and scaling features while reducing operational complexity. Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server, enabling organizations to migrate existing databases without rewriting application logic. Cloud SQL provides automated backups, replication, failover, patch management, and monitoring, allowing enterprises to maintain high availability and durability during migration. The service supports read replicas, enabling horizontal scaling for read-heavy workloads and minimizing performance impact during migration or production operations. Cloud SQL integrates with Database Migration Service (DMS), allowing continuous replication from on-premises MySQL databases to Cloud SQL, enabling near-zero downtime migrations by synchronizing data in real time while the source database remains operational. Once replication is complete, a short cutover window allows applications to connect to Cloud SQL without significant disruption. IAM integration and Cloud Audit Logs ensure secure access, operational monitoring, and compliance reporting. Cloud SQL offers encryption at rest and in transit, protecting sensitive data during migration and ongoing operations. Monitoring and alerting through Cloud Monitoring and Cloud Logging provide operational visibility into database performance, query latency, CPU usage, memory, disk space, and replication status, allowing proactive optimization. Unlike Cloud Spanner, which is globally distributed and designed for highly scalable transactional workloads, Cloud SQL is specifically optimized for MySQL-compatible applications, transactional consistency, and managed relational operations. BigQuery is designed for analytical workloads and is not suitable for transactional MySQL migration. Cloud Datastore is a NoSQL document database, which is not relational and does not support MySQL compatibility. By using Cloud SQL, enterprises can migrate legacy MySQL databases with minimal downtime, preserve existing application logic, maintain transactional consistency, and take advantage of Google Cloud’s fully managed infrastructure, including automated backups, failover, high availability, and security features. The combination of Database Migration Service with Cloud SQL ensures smooth, near-zero downtime migration, reducing risks and operational overhead while accelerating cloud adoption. Enterprises benefit from automated maintenance, monitoring, scaling, and security, enabling teams to focus on application development and business logic rather than database administration. Cloud SQL supports vertical scaling for CPU and memory, storage scaling, and high availability configurations across multiple zones, ensuring robust performance during peak workloads. The service integrates seamlessly with other Google Cloud services, such as Cloud Storage for backups, BigQuery for analytics, and Pub/Sub or Dataflow for downstream processing. By leveraging Cloud SQL, organizations can achieve a reliable, secure, and cost-effective migration path for legacy MySQL databases, ensuring continuity of business operations, high availability, and simplified cloud management. Cloud SQL’s managed nature reduces operational risk, improves scalability, supports disaster recovery, and provides compliance with regulatory standards, making it the optimal solution for migrating MySQL workloads to Google Cloud.
Question 116
A company wants to build a real-time recommendation system using streaming user activity data. 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:
Building a real-time recommendation system using streaming user activity data requires a highly scalable, low-latency, and fault-tolerant data ingestion and processing pipeline capable of handling large volumes of events. Pub/Sub and Dataflow together provide a complete solution for real-time event-driven pipelines. Pub/Sub acts as the messaging backbone, allowing applications and user activity events to be published asynchronously to topics. It is fully managed, scales automatically, supports low-latency delivery, message ordering, at-least-once delivery, dead-letter topics, and retries to ensure reliable message transmission. Dataflow, a serverless stream and batch processing service built on Apache Beam, consumes Pub/Sub messages and applies transformations such as aggregations, filtering, feature engineering, or enrichment required for recommendations. Dataflow supports event-time processing, windowing, triggers, and watermarking, ensuring accurate computation even with late-arriving or out-of-order events. The processed data can then be fed into a model serving layer, BigQuery, or other storage for real-time recommendations. Integration with Cloud ML or Vertex AI allows the processed streaming data to update machine learning models or scoring functions in near real time. Logging and monitoring through Cloud Logging and Cloud Monitoring enable observability into message throughput, processing latency, errors, and pipeline performance. IAM integration ensures secure access to topics, pipelines, and downstream storage. Cloud Storage and BigQuery are better suited for batch processing and historical analysis, not low-latency real-time pipelines. Cloud SQL and App Engine provide relational storage and web hosting, but lack scalable real-time streaming and event processing capabilities. Cloud Functions and Cloud Spanner cannot handle high-throughput streaming pipelines efficiently for complex recommendation calculations. Using Pub/Sub with Dataflow allows enterprises to decouple data producers from consumers, scale automatically based on workload, ensure fault tolerance, and process events reliably and quickly. This combination supports building robust recommendation systems that update recommendations in near real time based on user interactions, enabling personalized experiences. Dataflow’s serverless architecture eliminates the need to manage clusters, scaling, or infrastructure, allowing engineers to focus on processing logic, feature extraction, and recommendation algorithms. Pub/Sub ensures messages are delivered reliably and efficiently, even under variable traffic, while Dataflow maintains consistency, latency guarantees, and state management for streaming pipelines. By leveraging this combination, companies can implement fully managed, scalable, and resilient real-time recommendation systems, integrating seamlessly with downstream analytics, dashboards, or AI services. The architecture supports high availability, operational simplicity, low latency, and accurate computation, making Pub/Sub and Dataflow the ideal solution for building real-time recommendation systems with streaming data, enabling personalized user experiences, improved engagement, and actionable insights in a fully managed Google Cloud environment.
Question 117
A company wants to implement global, secure, and fast content delivery for a web application. Which service should be used?
A) Cloud CDN
B) Cloud Load Balancing
C) Cloud Functions
D) App Engine
Answer: A
Explanation:
Implementing global, secure, and fast content delivery for a web application requires a service that caches static and dynamic content at edge locations worldwide while integrating with Google Cloud services for security, scaling, and operational visibility. Cloud CDN provides fully managed content delivery by caching content at Google’s global edge points of presence, reducing latency by serving requests from the nearest location to the user. It works seamlessly with Cloud Load Balancing and backends such as Compute Engine, Cloud Storage, Cloud Run, or App Engine. By caching frequently accessed content, Cloud CDN reduces origin server load, increases scalability during traffic spikes, and improves user experience with low-latency responses. Cloud CDN supports HTTPS, ensuring secure content delivery, and integrates with IAM for access control. Custom cache control headers allow precise cache invalidation and content expiration management, ensuring users receive updated content without unnecessary backend requests. Logging and monitoring provide operational insights into cache hit ratios, request patterns, and performance metrics, enabling optimization and troubleshooting. Cloud CDN supports HTTP/2 and QUIC for faster content delivery and low latency. Cloud Load Balancing alone distributes traffic but does not cache content at edge locations, potentially increasing latency for distant users. Cloud Functions and App Engine are compute platforms and cannot provide global caching or edge content delivery on their own. Using Cloud CDN, enterprises can achieve global high availability, improved performance, scalability, and reduced operational load for web applications, serving content efficiently, securely, and reliably to users worldwide. It ensures smooth scaling, protection against traffic spikes, and integration with security policies, making it the optimal choice for content delivery at scale.
Question 118
A company wants to schedule periodic serverless tasks to trigger APIs at specific intervals. Which service should be used?
A) Cloud Scheduler
B) Cloud Functions
C) App Engine
D) Compute Engine
Answer: A
Explanation:
Scheduling periodic serverless tasks to trigger APIs at specific intervals requires a fully managed, cron-like service that can orchestrate task execution reliably without infrastructure management. Cloud Scheduler is a serverless service designed for this purpose, allowing organizations to define schedules for triggering HTTP endpoints, Pub/Sub messages, or Cloud Functions. It supports flexible cron syntax for precise scheduling, including time zones, intervals, and complex execution patterns. Cloud Scheduler integrates with IAM for secure execution, ensuring only authorized users or service accounts can create, modify, or invoke scheduled tasks. Retry policies, failure notifications, and dead-letter topics allow reliable execution of tasks and automated handling of failures. Cloud Scheduler can trigger serverless workflows without requiring dedicated servers or complex orchestration tools, providing operational simplicity and reducing maintenance overhead. Logging and monitoring through Cloud Logging and Cloud Monitoring give visibility into task execution, failure rates, and scheduling accuracy. Cloud Functions, App Engine, or Compute Engine could execute the tasks, but require manual scheduling, infrastructure, or operational management, increasing complexity and reducing scalability. Using Cloud Scheduler, enterprises can implement automated workflows, maintenance tasks, report generation, or API calls at predictable intervals, leveraging a fully managed serverless approach with high reliability, scalability, and operational transparency. The service ensures consistent execution, integrates seamlessly with other Google Cloud services, supports secure task invocation, and provides observability for auditing and compliance purposes. Cloud Scheduler is the definitive solution for serverless periodic task automation, allowing organizations to focus on business logic rather than infrastructure management.
Question 119
A company wants to implement event-driven workflows triggered by changes in Cloud Storage. 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 changes in Cloud Storage requires a serverless platform capable of responding to events such as object creation, deletion, or metadata changes. Cloud Functions is a fully managed, event-driven compute service that executes small, stateless functions in response to triggers from Cloud Storage, Pub/Sub, or HTTP requests. When an event occurs in Cloud Storage, such as a new file upload, Cloud Functions automatically invokes the corresponding function, allowing processing, transformation, or notification workflows without server management. Cloud Functions scales automatically from zero to handle spikes in events, ensuring low-latency, cost-efficient execution. IAM policies enforce secure access to the function and resources, while Cloud Logging and Cloud Monitoring provide observability into execution, errors, and performance. Cloud Run or App Engine can handle containerized or web workloads, but they are not inherently event-triggered by Cloud Storage changes. Cloud SQL is a managed relational database and cannot trigger event-driven workflows. Using Cloud Functions, enterprises can implement automated ETL, image processing, notification systems, or downstream data pipelines triggered by storage events, reducing operational complexity, ensuring scalability, and enabling reactive architectures. The serverless nature eliminates infrastructure management while providing high availability, fault tolerance, and integration with other Google Cloud services. Cloud Functions supports environment variables, secrets, and versioning, enabling secure, manageable, and maintainable event-driven workflows. It provides retry mechanisms, error handling, and logging to guarantee reliable execution and observability. By leveraging Cloud Functions, companies can implement responsive, scalable, event-driven architectures that react to Cloud Storage changes in real time, integrating seamlessly with downstream analytics, machine learning, and notification services. It ensures automated, timely processing of storage events, reducing manual intervention and operational risk while maximizing efficiency, reliability, and scalability. Cloud Functions provides a fully managed, serverless platform for event-driven workflows, making it the optimal solution for responding to Cloud Storage changes, enabling enterprises to implement real-time, reactive, and automated processes across their cloud environment.
Question 120
A company wants to monitor application performance and collect logs for analysis across Google Cloud services. Which service should be used?
A) Cloud Monitoring and Logging
B) Cloud SQL
C) Cloud Storage
D) Cloud Functions
Answer: A
Explanation:
Monitoring application performance and collecting logs for analysis across Google Cloud services requires a comprehensive, fully managed observability platform capable of aggregating metrics, logs, traces, and events from multiple sources. Cloud Monitoring and Logging provide integrated solutions to monitor infrastructure, applications, and services in real time, collect metrics, analyze logs, create dashboards, alerts, and support troubleshooting across Google Cloud. Cloud Logging captures structured and unstructured log entries from Compute Engine, App Engine, Cloud Functions, Cloud Run, Kubernetes Engine, and other services, storing them reliably and allowing filtering, search, and export to BigQuery or Cloud Storage for long-term retention and analysis. Cloud Monitoring collects metrics such as CPU, memory, network, latency, error rates, and throughput, visualizes them in dashboards, and enables alerts based on thresholds or anomalies. Together, they provide full-stack observability, proactive monitoring, and operational insights into system performance, usage trends, and error conditions. IAM integration secures access to logs and metrics, ensuring only authorized personnel can view, modify, or create monitoring policies. Cloud Monitoring supports alerting, incident management, SLO/SLA tracking, uptime monitoring, and automated notifications through email, SMS, or Pub/Sub, enabling timely responses to performance issues. Cloud SQL, Cloud Storage, and Cloud Functions provide specific compute, storage, or serverless capabilities but do not natively aggregate cross-service metrics or logs for centralized observability. Using Cloud Monitoring and Logging, enterprises gain end-to-end visibility, operational insights, and proactive incident detection, enabling performance optimization, troubleshooting, and compliance. Dashboards allow visualization of real-time and historical metrics, while logs provide detailed traceability and auditability. By leveraging these services, companies can monitor applications globally, identify bottlenecks, debug errors, correlate metrics with events, and optimize system performance. Cloud Monitoring and Logging enable anomaly detection, automated alerting, and integration with workflow automation, ensuring operational efficiency, reliability, and scalability. Enterprises can implement SLO/SLA tracking to meet business requirements, analyze trends for capacity planning, and improve service reliability. These services support multi-cloud and hybrid environments, allowing aggregation of metrics and logs from non-Google Cloud sources for unified observability. Cloud Monitoring and Logging are fully managed, scalable, and integrate seamlessly with Google Cloud services, providing secure, cost-effective, and reliable observability solutions. By centralizing monitoring and logging, organizations can make data-driven operational decisions, ensure application performance, maintain compliance, and quickly resolve issues. Cloud Monitoring and Logging together provide the foundation for operational excellence, visibility, and control across all Google Cloud workloads, making them the definitive solution for enterprise observability, performance monitoring, and log analysis.