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Question 21
Which Google Cloud service is best suited for running containers without managing servers or infrastructure while supporting automatic scaling?
A) Cloud Run
B) GKE
C) App Engine Flexible
D) Compute Engine
Answer: A
Explanation:
A Cloud Run is a fully managed service that allows running containerized applications without the need to manage virtual machines, clusters, or operating systems. It automatically scales based on request volume, including scaling down to zero during idle periods, which provides cost efficiency. Cloud Run supports any container that follows the Open Container Initiative standard, enabling developers to bring custom runtimes and full flexibility. It also offers built-in traffic splitting, HTTPS endpoints, and integrations with Eventarc and Pub/Sub, making it suitable for APIs, microservices, lightweight event processing, and stateless applications. Unlike cluster-based solutions, Cloud Run eliminates infrastructure overhead entirely.
B GKE is a powerful container orchestration service based on Kubernetes, but it requires managing clusters, node pools, autoscaling configurations, upgrades, and operational overheaC Even though it offers maximum flexibility and customization, it is not serverless and therefore does not match Cloud Run’s simplicity for developers who want purely managed execution.
C App Engine Flexible supports container-based deployments but still requires managing VM instances underneath. It lacks the automatic scaling to zero and incurs higher operational cost and complexity. It is suitable for long-running services but not for lightweight stateless containers that need rapid scaling.
D Compute Engine provides complete VM control, allowing containers to run on VMs via Docker or container-optimized OS. However, this requires managing OS patches, compute instance sizes, autoscaling rules, load balancers, and security updates—making it far from serverless. Cloud Run is the best fit for running containers without infrastructure management and with fully automated scaling.
Question 22
Which service provides a fully managed NoSQL database optimized for massive throughput and low-latency reads at petabyte scale?
A) Bigtable
B) Firestore
C) Cloud Spanner
D) BigQuery
Answer: A
Explanation:
A Bigtable is a wide-column NoSQL database built for extremely large datasets requiring high throughput and consistently low-latency reads and writes. It is horizontally scalable and ideal for analytical workloads such as time-series data, telemetry, financial data, IoT device metrics, and ad-tech systems. Bigtable automatically partitions data to support petabyte-scale workloads and integrates well with Dataflow, Dataproc, and BigQuery. Its performance characteristics make it one of the best options for real-time analytic use cases where low latency is crucial.
B Firestore is also a NoSQL database but is designed for mobile and web application datA It offers strong consistency, flexible documents, and multi-region replication, but it is not designed for massive throughput at Bigtable’s scale. It focuses on application development and structured document storage rather than analytic workloads.
C Cloud Spanner is a globally distributed relational database offering SQL support with strong consistency. While it can scale horizontally, it is best suited for transactional workloads rather than NoSQL-style massive throughput analytics. Its emphasis is correctness and relational structure rather than raw high-speed ingestion.
D BigQuery is an analytics warehouse optimized for SQL-based queries across large datasets. It is not a NoSQL database and is not designed for high-speed operational reads and writes. Instead, it excels at batch analytics and reporting, not low-latency NoSQL access.Bigtable is the most ideal service for extremely large NoSQL datasets requiring both high throughput and low latency.
Question 23
Which Google Cloud service can route events between services using standardized event formats across multiple Google Cloud products?
A) Eventarc
B) Pub/Sub
C) Cloud Tasks
D) Cloud Scheduler
Answer: A
Explanation:
A Eventarc is a fully managed event routing service designed to route standardized CloudEvents between Google Cloud services, enabling robust event-driven architectures without requiring custom integrations. Eventarc allows developers to trigger Cloud Run services, Workflows, or other event targets using events from multiple sources, including Cloud Storage, Firestore, BigQuery, Cloud Audit Logs, and third-party SaaS applications. It enforces consistent event formats, ensuring that all events follow the CloudEvents specification, which simplifies event processing and orchestration. Eventarc supports advanced features such as event filtering, allowing subscribers to receive only relevant events, and guaranteed delivery to ensure reliable event propagation across services. By unifying event formats and providing centralized routing, Eventarc reduces the complexity of building and maintaining event-driven workflows, making it easier to integrate multiple cloud services and automate complex processes. This is particularly valuable in microservices and distributed architectures where events from various sources must be delivered consistently to multiple targets. Eventarc also integrates with Pub/Sub for message delivery, enabling large-scale, asynchronous processing of events while maintaining standardized structures. Its ability to handle dynamic routing, multiple event sources, and multi-target delivery makes it a core component for building scalable, resilient, and maintainable cloud-native applications.
B Pub/Sub provides messaging and broadcasting capabilities, allowing publishers to send messages to multiple subscribers. While it can carry events at scale, Pub/Sub does not enforce standardized event structures like CloudEvents. Developers are responsible for defining message formats, making integration across multiple services less consistent compared to EventarC
C Cloud Tasks is designed for asynchronous execution of background jobs with scheduling and retry capabilities. It does not support routing events between cloud services or handling standardized CloudEvents, and its focus is on delivering individual tasks to HTTP endpoints rather than orchestrating event flows.
D Cloud Scheduler provides time-based job execution, similar to a cron service. It cannot route events dynamically or integrate multiple event sources, and is therefore unsuitable for building event-driven architectures. Eventarc is the most complete, purpose-built event-routing service for standardized CloudEvents within Google Cloud, enabling reliable, scalable, and maintainable event-driven applications.
Question 24
Which service allows seamless querying of data stored in Cloud Storage using standard SQL without loading it into a database?
A) BigQuery External Tables
B) Dataproc
C) Cloud SQL
D) Firestore Export Queries
Answer: A
Explanation:
A BigQuery External Tables allow organizations to query data stored directly in Cloud Storage using standard SQL, without the need to ingest the data into BigQuery’s internal storage. They support a variety of file formats, including Parquet, Avro, CSV, and ORC, enabling seamless integration with data lakes and hybrid architectures. This approach is particularly useful for exploratory analytics, schema-on-read scenarios, and situations where datasets are large, frequently updated, or shared across multiple tools and teams. By eliminating the need for duplication or import into BigQuery storage, external tables reduce storage costs and simplify data management. They also allow analysts and data engineers to run ad hoc queries efficiently on external datasets while maintaining the flexibility to combine external data with native BigQuery tables for more complex analyses. External tables are fully compatible with BigQuery’s query engine, supporting joins, aggregations, filtering, and other SQL operations as if the data were stored internally, providing a powerful and familiar environment for data analytics.
B Dataproc is a managed service for running Hadoop, Spark, and other big data clusters. While it can process data from Cloud Storage, it requires provisioning clusters, managing jobs, and expertise in distributed programming. Dataproc provides more flexibility for custom processing pipelines but introduces operational complexity compared to the simplicity of querying external tables in BigQuery.
C Cloud SQL is a managed relational database that requires data to be loaded into tables before querying. It does not support direct scanning of raw Cloud Storage files, making it less suitable for analyzing large or semi-structured datasets stored externally.
C Firestore export queries allow exporting Firestore data to Cloud Storage for analysis. However, these exports do not provide native SQL querying on Cloud Storage files. Additional transformation or loading steps are required to analyze the data in SQL-compatible systems. BigQuery External Tables provide the most efficient, scalable, and cost-effective solution for running SQL queries directly on Cloud Storage data, enabling analytics without ingestion while maintaining flexibility, performance, and compatibility with existing BigQuery workflows.
Question 25
Which Google Cloud service provides a scalable in-memory cache that helps reduce latency for frequently accessed application data?
A) Memorystore
B) Cloud SQL
C) Cloud Storage
D) Bigtable
Answer: A
Explanation:
A Memorystore is a fully managed in-memory data store service that supports Redis and MemcacheC It is designed to deliver ultra-low-latency access to frequently used data, making it ideal for caching session states, user profiles, precomputed results, tokens, leaderboard information, metadata, and other transient datA By keeping data in memory rather than on disk, Memorystore drastically reduces read and write latency compared to traditional databases, often achieving sub-millisecond response times. This offloads backend databases, reducing CPU and I/O load, which improves overall application performance and scalability. Memorystore offers multiple tiers, including high availability configurations with automatic failover, replication, and seamless scaling, ensuring predictable performance even under heavy workloads. It integrates easily with Compute Engine, Google Kubernetes Engine, Cloud Functions, and App Engine, making it suitable for a wide range of cloud-native and hybrid architectures. Security features include private IP connectivity, IAM-based access controls, and encryption of data in transit. Memorystore enables developers to build responsive, high-performance applications by providing a reliable and scalable caching layer that reduces latency and improves user experience.
B Cloud SQL is a managed relational database service that stores data on disk. While it is highly reliable and supports ACID-compliant transactions, it is not optimized for ultra-low-latency caching workloads. Cloud SQL cannot deliver the microsecond-level response times required for performance-critical caching scenarios.
C Cloud Storage is a durable object storage service designed for storing backups, large files, media content, and analytics datasets. It provides high durability and scalability but is not optimized for rapid, repeated access or low-latency application caching.
C Bigtable is a NoSQL wide-column database designed for massive throughput and large-scale operational or analytical workloads. While it can handle high read/write volumes, it is still disk-backed and does not provide the sub-millisecond latency that in-memory caches require. Memorystore is the correct choice for caching frequently accessed data, reducing latency, offloading backend databases, and enabling high-performance, responsive applications.
Question 26
Which Google Cloud service provides a fully managed environment for running Apache Spark and Hadoop jobs with simplified cluster management?
A) Dataproc
B) Dataflow
C) BigQuery
D) Cloud Run
Answer: A
Explanation:
A Dataproc is the correct choice because it provides a fully managed environment specifically designed to run Apache Spark, Hadoop, Hive, and other open-source distributed data processing frameworks. It simplifies cluster creation, configuration, scaling, and management, allowing data engineers and analysts to focus on processing jobs rather than worrying about infrastructure. Dataproc clusters can be created in seconds, reducing the time to provision resources and accelerating analytics workflows. It integrates natively with Cloud Storage, BigQuery, and Vertex AI, allowing seamless data ingestion and processing pipelines. Dataproc supports autoscaling and preemptible instances, which helps reduce operational costs while maintaining high performance. Its compatibility with existing Hadoop and Spark scripts makes migration from on-premises big data environments straightforwarC Additionally, Dataproc allows flexible job submission via the console, CLI, REST APIs, or workflow automation tools, enabling reproducible, automated ETL and machine learning pipelines at scale.
B Dataflow is designed for stream and batch processing using Apache Beam. It abstracts infrastructure entirely and runs pipelines serverlessly. While powerful for event-driven processing, real-time data streams, and batch transformations, Dataflow does not natively run Spark or Hadoop jobs and does not provide the cluster-level management and orchestration required by organizations that depend on Hadoop ecosystems. Therefore, it cannot replace Dataproc when existing Spark/Hadoop workflows must be preserveC
C BigQuery is a serverless data warehouse optimized for SQL queries over massive datasets. It provides high-performance analytical querying but cannot execute Spark or Hadoop distributed jobs. It is intended for data analysis rather than big data cluster processing and does not offer cluster creation, configuration, or management capabilities.
D Cloud Run is a serverless platform for running stateless containers. It is ideal for microservices, APIs, or lightweight workloads but does not support the orchestration of large-scale distributed processing frameworks like Spark or Hadoop. It lacks cluster-level controls, node configuration, and resource management needed for big data pipelines. Dataproc is therefore the ideal choice for managed Spark and Hadoop workloads, providing fast, scalable, and cost-effective cluster management while reducing operational complexity.
Question 27
Which Google Cloud service provides network-level isolation to protect sensitive data and restrict access to cloud APIs and resources within defined boundaries?
A) VPC Service Controls
B) Cloud Armor
C) IAM
D) Firewall Rules
Answer: A
Explanation:
A VPC Service Controls is the correct answer because it provides a mechanism to create security perimeters around sensitive resources, preventing data exfiltration even if identity-based permissions are misconfigureC It allows organizations to define strong isolation boundaries for APIs, services, and resources, ensuring that data cannot be accessed or transferred outside these perimeters. This feature is essential for regulatory compliance, data residency requirements, and protecting mission-critical workloads from accidental or malicious leaks. VPC Service Controls can integrate with Access Context Manager to enforce context-aware access policies, considering user identity, location, device security posture, and other risk factors. Organizations can create layered perimeters, define audit logging, and combine them with IAM roles for enhanced security. The service is particularly useful for enterprise environments where multiple teams need to work on sensitive data while ensuring that critical services cannot be accessed outside defined trusted networks.
B Cloud Armor protects applications from DDoS attacks and enforces Web Application Firewall (WAF) policies. While it strengthens network security at the edge, it does not isolate services or prevent internal or API-level exfiltration. It focuses primarily on filtering traffic from external sources rather than enforcing internal perimeters for sensitive resources.
C IAM manages identity and access control at the resource level. It defines who can access which resources but cannot enforce network-level isolation or prevent data exfiltration if users operate from untrusted networks. IAM is crucial for permissions management but insufficient on its own to create secure service perimeters.
D Firewall Rules control traffic at the network layer, such as allowing or denying ingress and egress to VM instances or subnets. However, they cannot restrict access at the API level, nor can they prevent resource access from within Google-managed services. They are effective for VM-level traffic but cannot substitute for VPC Service Controls in protecting sensitive cloud services. VPC Service Controls is therefore the best choice for organizations that need strong network-level isolation and protection against unauthorized access to sensitive datA
Question 28
Which feature allows automatic creation of persistent disk snapshots on a schedule for backup and recovery?
A) Resource Scheduler Snapshots
B) Compute Engine Snapshot Schedules
C) Backup and DR for VM Instances
D) Cloud Storage Lifecycle Rules
Answer: B
Explanation:
A Resource Scheduler Snapshots is not an actual Google Cloud feature. It does not exist as a managed service or tool within the platform and therefore cannot be used for creating scheduled backups of persistent disks. This option is often confused with actual snapshot scheduling services but is invalid in practice.
B Compute Engine Snapshot Schedules is the correct answer because it enables administrators to automatically create snapshots of persistent disks according to defined schedules. These schedules can be hourly, daily, weekly, or custom, ensuring regular backups for disaster recovery and operational resilience. Snapshots are incremental, which reduces storage usage and costs while maintaining versioned backups that can be restored at any time. Multiple disks can be included in a single schedule, and retention policies can be configured to automatically delete older snapshots, keeping storage efficient. Snapshot Schedules integrate seamlessly with Compute Engine, allowing VMs to be backed up without downtime, and support both zonal and regional disks for high availability. By automating backups, organizations can ensure compliance, mitigate data loss, and maintain operational continuity with minimal manual intervention.
C Backup and DR for VM Instances is a separate enterprise-level backup solution that also provides VM recovery and backup functionality. While it is useful for comprehensive disaster recovery planning, the question specifically asks for scheduled persistent disk snapshots, which are handled natively by Compute Engine Snapshot Schedules. Using Backup and DR involves additional setup, licensing, and configuration overhead, making it less straightforward for scheduled snapshots.
D Cloud Storage Lifecycle Rules manage object lifecycle operations such as transitioning objects between storage classes or deleting them after a certain perioC They are designed for object storage optimization and cannot create persistent disk snapshots or manage VM backups. Compute Engine Snapshot Schedules is therefore the correct, simple, and automated method for regular persistent disk backups in Google ClouC
Question 29
Which Google Cloud service provides a fully managed, low-latency in-memory caching solution using Redis and Memcached?
A) Memorystore
B) Cloud SQL
C) Bigtable
D) Cloud Storage
Answer: A
Explanation:
A Memorystore is the correct answer because it provides fully managed in-memory caching using Redis and Memcached, designed for ultra-low latency and high throughput. It is ideal for caching frequently accessed data such as session information, configuration settings, tokens, leaderboards, and precomputed results. By storing data in memory, Memorystore significantly reduces the load on backend databases and improves application responsiveness. The service supports high availability with replication across zones, automated failover, monitoring, and scaling, ensuring reliability for mission-critical workloads. Integration with App Engine, GKE, and Compute Engine enables applications to achieve faster data retrieval and enhanced performance for both web and backend services.
B Cloud SQL is a managed relational database service with persistent storage. While reliable and scalable, it cannot provide the microsecond-level access times offered by in-memory caches. It is optimized for structured data and transactional operations rather than rapid retrieval of frequently accessed datA
C Bigtable is a high-throughput NoSQL database, suitable for very large datasets and analytical workloads. While it can handle high volume reads and writes, it is disk-based and cannot provide the ultra-low latency necessary for caching applications or reducing database load in real time.
D Cloud Storage is object storage optimized for durability, large files, and high availability. It is not designed for microsecond-level access and cannot function as a caching layer for fast, repetitive data retrieval. Memorystore is therefore the best option for managed in-memory caching, providing high-speed access, low latency, and operational simplicity.
Question 30
Which Google Cloud service provides container orchestration using Kubernetes with full control over nodes, scaling, and configuration?
A) GKE
B) Cloud Run
C) App Engine
D) Compute Engine
Answer: A
Explanation:
A GKE (Google Kubernetes Engine) is the correct choice because it provides fully managed Kubernetes clusters with complete control over nodes, autoscaling, network configuration, workload management, and resource allocation. It supports advanced orchestration features such as StatefulSets, DaemonSets, node pools, and custom resource scheduling. GKE is ideal for deploying microservices, containerized applications, hybrid workloads, and large-scale distributed systems that require high reliability and resilience. It also integrates with Google Cloud monitoring, logging, and identity management tools, enabling efficient management of complex applications. GKE clusters can be dynamically scaled based on demand, reducing costs and improving performance. It provides the flexibility to customize node types, machine sizes, and GPU usage to meet workload-specific requirements, making it suitable for enterprises with demanding container orchestration needs.
B Cloud Run is serverless and abstracts away infrastructure and cluster management. While it is easy to deploy containerized workloads, it does not provide node-level control, Kubernetes orchestration, or advanced deployment strategies. It is better suited for stateless microservices rather than complex multi-service applications.
C App Engine is a fully managed PaaS platform. It simplifies application deployment but does not provide Kubernetes orchestration, container-level management, or fine-grained node control. It is ideal for web applications and APIs but cannot replace Kubernetes clusters.
D Compute Engine provides raw VM instances for running containers manually. While it gives control over VM resources, it does not include orchestration, scaling, or advanced cluster management. Managing multiple containers across many VMs requires significant operational effort. GKE is the best choice for organizations needing full Kubernetes orchestration with cluster-level control, flexibility, and automated scaling.
Question 31
Which Google Cloud service allows you to deploy serverless containerized applications that automatically scale based on incoming HTTP traffic?
A) Cloud Run
B) App Engine
C) GKE Autopilot
D) Compute Engine
Answer: A
Explanation:
A Cloud Run is the correct choice because it provides a fully managed, serverless environment for running containerized applications without managing the underlying infrastructure. Cloud Run automatically scales the application based on incoming HTTP requests, including scaling down to zero when no traffic is present, which reduces operational costs. It supports any container that listens for HTTP requests and integrates seamlessly with Cloud Build for CI/CD pipelines, Cloud Logging, and Cloud Monitoring for observability. Cloud Run abstracts away cluster management, networking, and scaling complexity, allowing developers to focus purely on writing and deploying applications. Its pay-per-use model ensures that organizations only pay for the resources consumed by active requests, optimizing cost-efficiency.
B App Engine is also a serverless platform but is more opinionated and restricts language runtimes to supported environments unless using flexible environment containers. While App Engine can scale automatically, it is less flexible than Cloud Run in running arbitrary containerized workloads and does not offer full HTTP-based concurrency scaling in the same way.
C GKE Autopilot provides a managed Kubernetes environment where Google manages infrastructure but still requires some understanding of Kubernetes concepts. It allows scaling based on workloads but does not abstract clusters as fully as Cloud Run and may require more operational oversight.
D Compute Engine provides virtual machines where containers can be deployed manually. It requires managing clusters, scaling, load balancing, and network configuration, making it significantly less convenient for serverless-style auto-scaling container deployments. Cloud Run is therefore the best solution for fully managed, serverless containerized applications with automatic HTTP-based scaling and minimal operational overheaC
Question 32
Which Google Cloud service provides a global, fully managed message bus that enables asynchronous communication between decoupled applications?
A) Pub/Sub
B) Cloud Tasks
C) Eventarc
D) Cloud Scheduler
Answer: A
Explanation:
A Pub/Sub is the correct service because it provides a globally distributed, fully managed messaging system designed for asynchronous communication between decoupled applications. It allows publishers to send messages to topics, which subscribers can then consume independently, enabling event-driven architectures and reliable message delivery at scale. Pub/Sub supports features like message ordering, dead-letter topics, exactly-once delivery semantics, and filtering, which make it ideal for microservices, streaming analytics, IoT, and real-time event processing. It can handle high-throughput workloads and integrates with services such as Dataflow, Cloud Functions, Cloud Run, and BigQuery for further processing and analytics.
B Cloud Tasks is primarily for managing background jobs and deferred execution, such as delivering tasks reliably to HTTP endpoints. It does not act as a global message bus for asynchronous communication across multiple services.
C Eventarc routes standardized CloudEvents between services but relies on Pub/Sub for message delivery. It is an event router, not a global messaging bus itself.
D Cloud Scheduler is for time-based scheduling of jobs, similar to cron. It is not designed for asynchronous message delivery or decoupling applications. Pub/Sub is therefore the ideal choice for asynchronous, global, scalable messaging between decoupled systems.
Question 33
Which Google Cloud service provides real-time observability, metrics, and alerting for applications and infrastructure?
A) Cloud Monitoring
B) Cloud Logging
C) Cloud Trace
D) Cloud Debugger
Answer: A
Explanation:
A Cloud Monitoring is a fully managed observability service that provides real-time visibility into Google Cloud, on-premises, and hybrid environments. It collects metrics from a wide variety of sources, including Compute Engine instances, Google Kubernetes Engine clusters, serverless workloads such as Cloud Functions and Cloud Run, and custom applications. Cloud Monitoring enables engineers to visualize system performance through dynamic dashboards, monitor uptime and latency, track CPU, memory, and network usage, and correlate metrics across multiple services. It supports custom metrics and user-defined dashboards, allowing organizations to tailor monitoring to their specific workloads. Cloud Monitoring also provides automatic anomaly detection, alerting based on thresholds, and integration with incident management platforms such as PagerDuty and ServiceNow, enabling rapid response to performance or reliability issues. In addition, Cloud Monitoring supports service-level objectives (SLOs) and service-level agreements (SLAs), helping teams measure reliability against business expectations and regulatory requirements. By consolidating metrics from multiple environments, Cloud Monitoring allows engineers and operations teams to identify trends, detect performance degradation, and proactively optimize systems, making it essential for reliability engineering and operational excellence.
B Cloud Logging is focused on collecting, storing, and analyzing log data from Google Cloud and hybrid environments. While invaluable for auditing, troubleshooting, and forensic investigations, it does not provide proactive performance monitoring, real-time alerting, or infrastructure-wide metrics visualization.
C Cloud Trace captures distributed latency data for individual requests, helping identify bottlenecks and slow operations within applications. Although useful for pinpointing performance issues, it does not provide comprehensive observability across infrastructure, metrics dashboards, or alerting for system-wide issues.
C Cloud Debugger allows developers to inspect live application code and variables without stopping execution. It is intended for debugging purposes rather than monitoring overall system health or performance metrics.
Question 34
Which Google Cloud service allows you to enforce organization-wide security policies such as IAM restrictions, API restrictions, and audit configurations?
A) Organization Policy Service
B) IAM
C) VPC Service Controls
D) Cloud Armor
Answer: A
Explanation:
A Organization Policy Service is a fully managed governance tool that allows administrators to define and enforce organization-wide policies across Google Cloud projects, folders, and resources. It provides centralized control to set constraints on which APIs can be enabled, the geographic locations where resources may be deployed, allowed service accounts, and other operational or security configurations. By establishing policies at the organization or folder level, administrators can enforce consistent rules across all projects, reducing misconfigurations, ensuring compliance with internal or regulatory standards, and maintaining a unified security posture. Organization Policy Service supports both explicit and inherited policies, allowing fine-grained control at every level of the resource hierarchy. It integrates with Cloud Audit Logs, providing a detailed record of policy changes, enforcement, and violations, which is critical for auditing and compliance. The service is particularly valuable in large, dynamic environments with multiple teams and projects, ensuring that developers and administrators adhere to organizational governance standards without needing to manually enforce rules in each project. Policies can be automatically evaluated, and violations can trigger alerts or remediation workflows. Organization Policy Service complements other security and governance tools such as IAM, VPC Service Controls, and Cloud Armor by providing broad, policy-driven enforcement that spans the entire organization, rather than focusing on individual resources or network perimeters.
B IAM (Identity and Access Management) allows administrators to define permissions for users, groups, and service accounts but does not provide organization-wide constraints, inheritance policies, or centralized governance. It focuses on granting access rather than enforcing operational rules across multiple projects.
C VPC Service Controls create security perimeters to prevent data exfiltration between services and networks, but they operate at the resource boundary level rather than enforcing organization-wide governance policies.
C Cloud Armor provides network security features such as DDoS protection and Web Application Firewall policies, but it does not manage IAM roles, API access, or organization-wide operational policies. Organization Policy Service is essential for centralized governance, consistent policy enforcement, compliance, and reducing operational risks across large Google Cloud environments.
Question 35
Which Google Cloud service provides a managed NoSQL database optimized for low-latency, high-throughput applications with wide-column data storage?
A) Bigtable
B) Firestore
C) Cloud SQL
D) Datastore
Answer: A
Explanation:
A Bigtable is the correct choice because it is a fully managed, highly scalable NoSQL database designed for large analytical and operational workloads. It supports wide-column storage, making it ideal for time-series data, IoT, financial data, and real-time analytics. Bigtable provides consistent low-latency reads and writes, horizontal scalability, and integration with Dataflow, Spark, and Hadoop for processing large datasets. It is optimized for massive throughput and can handle millions of operations per second with predictable performance. Its replication and regional configuration options ensure high availability and disaster recovery capabilities.
B Firestore is a document-based NoSQL database designed for mobile and web applications. While it supports strong consistency and real-time synchronization, it is not optimized for massive high-throughput workloads with wide-column storage.
C Cloud SQL is a managed relational database, suitable for transactional workloads but not for wide-column NoSQL use cases. It cannot scale horizontally to the same extent as Bigtable.
D Datastore (now integrated with Firestore in Datastore mode) is suitable for document-based applications but does not offer the performance or scale of Bigtable for high-throughput operational workloads. Bigtable is therefore the ideal choice for applications requiring large-scale, low-latency, high-throughput NoSQL storage with wide-column data support.
Question 36
Which Google Cloud service allows you to automate deployment, scaling, and management of containerized applications without managing the underlying infrastructure?
A) Cloud Run
B) App Engine
C) GKE Autopilot
D) Compute Engine
Answer: A
Explanation:
A Cloud Run is the correct answer because it provides a fully managed, serverless platform to run containerized applications without the need to manage infrastructure, clusters, or scaling. Cloud Run automatically scales applications up or down based on incoming HTTP requests, even down to zero when no requests are present, optimizing cost efficiency. Developers can deploy any container that listens for HTTP requests, which allows flexibility for different languages, runtimes, and dependencies. Cloud Run also integrates seamlessly with Cloud Build, enabling continuous integration and deployment workflows. It supports built-in logging and monitoring through Cloud Logging and Cloud Monitoring, providing observability for troubleshooting and performance tracking. Additionally, Cloud Run ensures traffic splitting and revision management, allowing safe rollouts and A/B testing of container versions. It is ideal for stateless microservices and APIs, providing a combination of simplicity, performance, and cost efficiency.
B App Engine is a fully managed PaaS platform that supports scaling applications automatically. While it can host containerized workloads in the flexible environment, it is more opinionated regarding language runtime, scaling rules, and deployment structure. App Engine is better suited for web applications rather than arbitrary containerized services.
C GKE Autopilot manages Kubernetes clusters automatically, abstracting some infrastructure management. However, developers still need to understand Kubernetes concepts, configure pods, manage workloads, and handle scaling policies, which adds operational complexity compared to Cloud Run’s fully serverless experience.
D Compute Engine provides raw VMs, giving full control over servers but requiring administrators to manage scaling, container orchestration, networking, and patching. It is not suitable for fully serverless, automated container management. Cloud Run is therefore the optimal choice for fully automated, serverless deployment of containerized applications with minimal operational overheaC
Question 37
Which Google Cloud service allows you to store, process, and analyze petabyte-scale structured data using standard SQL queries without managing infrastructure?
A) BigQuery
B) Dataproc
C) Cloud SQL
D) Bigtable
Answer: A
Explanation:
A BigQuery is the correct service because it is a fully managed, serverless, high-performance data warehouse designed for analyzing petabyte-scale structured data using standard SQL. BigQuery automatically manages storage, query optimization, scaling, and infrastructure, allowing users to focus entirely on data analysis rather than system maintenance. It supports real-time analytics, streaming ingestion, partitioned tables, and clustering to optimize query performance. BigQuery also integrates seamlessly with AI and ML tools, Dataflow, and other Google Cloud services to support advanced analytics and machine learning workflows. Security features include IAM integration, encryption at rest and in transit, and fine-grained access controls. It is ideal for organizations that require massive-scale analytics without worrying about managing clusters, nodes, or storage systems.
B Dataproc can process large datasets using Hadoop or Spark but requires cluster management and infrastructure setup. It is suitable for batch processing and ETL but does not offer a serverless SQL query interface or fully automated scaling like BigQuery.
C Cloud SQL is a managed relational database for transactional workloads but cannot scale to petabyte-level data or provide the same level of analytical query optimization. It is intended for OLTP workloads, not massive-scale analytics.
D Bigtable is a NoSQL wide-column database optimized for high-throughput operational workloads. While it handles massive amounts of data efficiently, it does not support standard SQL queries and is not designed for ad-hoc analytics. BigQuery is therefore the most suitable service for large-scale, serverless, SQL-based analytics on structured datA
Question 38
Which Google Cloud service allows you to route events between services using standardized CloudEvents and provides filtering, delivery guarantees, and event-driven architecture support?
A) Eventarc
B) Pub/Sub
C) Cloud Scheduler
D) Cloud Tasks
Answer: A
Explanation:
A Eventarc is the correct answer because it is designed to route standardized CloudEvents between Google Cloud services and third-party SaaS providers. Eventarc allows developers to build fully event-driven architectures without manually implementing message formats or delivery pipelines. It supports triggers for Cloud Run, Workflows, and other endpoints based on events from Cloud Storage, Firestore, BigQuery, and Audit Logs. Eventarc provides filtering, guaranteed delivery, retry policies, and consistent event structure across services. Its integration with Cloud Logging and IAM ensures secure and auditable workflows, making it ideal for automated pipelines, workflow orchestration, and decoupled microservices. Eventarc simplifies distributed system design by enforcing CloudEvents standards, ensuring predictable metadata and payload structure for all events.
B Pub/Sub is a global messaging backbone that delivers asynchronous messages to decoupled applications but does not enforce standardized event formats like CloudEvents. It is ideal for messaging but not for direct CloudEvents routing or event-driven orchestration.
C Cloud Scheduler triggers jobs on time-based schedules. While useful for cron-like tasks, it cannot route CloudEvents or manage dynamic, event-driven workflows.
D Cloud Tasks handles asynchronous background task execution and retries. It cannot ingest events from multiple sources or enforce standardized event structures. Eventarc is therefore the ideal choice for reliable, event-driven workflows and CloudEvents routing.
Question 39
Which Google Cloud service provides real-time observability, alerting, and dashboards for applications, infrastructure, and custom metrics?
A) Cloud Monitoring
B) Cloud Logging
C) Cloud Trace
D) Cloud Debugger
Answer: A
Explanation:
A Cloud Monitoring is the correct choice because it provides comprehensive real-time observability for Google Cloud and hybrid environments. It collects system, application, and custom metrics, visualizes them in dashboards, triggers alerts based on thresholds or anomalies, and integrates with incident management systems. Cloud Monitoring enables proactive detection of performance degradation, latency issues, or resource bottlenecks. It supports SLO/SLA monitoring, dynamic dashboards, and integration with Cloud Logging for end-to-end observability. It is essential for reliability engineering, ensuring applications and infrastructure perform optimally under varying loads.
B Cloud Logging collects logs from systems and applications. While important for auditing, debugging, and troubleshooting, it does not provide proactive alerting or performance monitoring like Cloud Monitoring.
C Cloud Trace measures request latency in distributed applications, useful for identifying bottlenecks, but it does not provide infrastructure-wide monitoring or alerting.
D Cloud Debugger connects to live applications for code inspection and variable analysis without stopping execution. It is a debugging tool, not a monitoring solution. Cloud Monitoring is therefore the most complete solution for monitoring, observability, and alerting across applications and infrastructure.
Question 40
Which Google Cloud service provides a fully managed NoSQL document database with real-time synchronization for mobile and web applications?
A) Firestore
B) Bigtable
C) Cloud SQL
D) Datastore
Answer: A
Explanation:
A Firestore is a fully managed, document-oriented NoSQL database designed for real-time, scalable applications across mobile, web, and serverless environments. It provides automatic real-time synchronization between devices and clients, enabling applications to reflect updates instantly without requiring manual refreshes. Firestore supports ACID transactions, offline data persistence, and hierarchical data structures, which allow developers to organize data in collections and documents for efficient querying and storage. It integrates seamlessly with Firebase, enabling rapid development of mobile applications with features like authentication, notifications, and analytics. Firestore also works with Google Cloud Functions to trigger serverless workflows in response to database changes, facilitating reactive and event-driven architectures. Fine-grained security is enforced using IAM roles and Firestore Security Rules, allowing administrators to define access control at the document or collection level. Firestore automatically scales horizontally to handle millions of concurrent users while maintaining low-latency read and write operations, making it ideal for chat applications, collaborative tools, real-time dashboards, gaming backends, and other interactive platforms. Its serverless nature eliminates the need for manual infrastructure management, enabling teams to focus on application logic rather than database maintenance.
B Bigtable is a NoSQL wide-column database designed for extremely high-throughput workloads and analytical processing. While it offers low-latency access for large-scale datasets, it does not support document-based data models, hierarchical structures, or real-time synchronization required for mobile and web applications.
C Cloud SQL is a managed relational database service optimized for transactional workloads. It provides strong ACID guarantees but does not natively support real-time synchronization across distributed client applications or hierarchical document structures.
C Datastore (Firestore in Datastore mode) is a legacy document database suitable for server-side applications but lacks many modern Firestore features such as real-time updates, offline persistence, and seamless integration with Firebase for client-side applications. Firestore is therefore the ideal choice for real-time, document-oriented, scalable applications, delivering low-latency synchronization, seamless integration with serverless and mobile platforms, and robust security and scalability for modern web and mobile workloads.
