Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 6 Q76-90
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Question 76
A company wants to store and manage container images with versioning and vulnerability scanning. Which Google Cloud service should be used?
A) Artifact Registry
B) Cloud Storage
C) Cloud SQL
D) Container Builder
Answer: A
Explanation:
Storing and managing container images with versioning and vulnerability scanning requires a service designed for container artifact management. Artifact Registry is a fully managed service that supports storing container images, language packages, and Helm charts with integrated security and version control. It provides fine-grained IAM policies, allowing administrators to define who can access, push, or pull images at the repository or project level. Artifact Registry supports both Docker images and OCI-compliant artifacts, enabling organizations to store multiple versions of container images securely. The service integrates with Container Analysis to perform vulnerability scanning on images, providing visibility into security risks and compliance issues. Vulnerability scanning can identify known CVEs, misconfigurations, or outdated dependencies, helping teams remediate security threats before deployment. Artifact Registry also supports replication across multiple regions, ensuring high availability and reducing latency for distributed teams. Integration with CI/CD pipelines allows automatic image promotion, tagging, and scanning during the build process, streamlining DevOps workflows. By storing images in Artifact Registry, organizations gain a secure, scalable, and operationally manageable solution for managing containerized workloads throughout their lifecycle.
Cloud Storage is a generic object storage solution. While it can store container image files, it lacks features such as vulnerability scanning, container-specific metadata, or fine-grained access control tailored for DevOps workflows. Using Cloud Storage for container images would require manual management of versions, tags, and security checks, increasing operational complexity and risk.
Cloud SQL is a relational database service. It is designed for structured data and transactional workloads, not for storing or managing container images. It lacks built-in features for versioning, scanning, or artifact management. Attempting to use Cloud SQL for container image storage would be inefficient, complex, and not scalable.
Container Builder, now integrated as Cloud Build, is a CI/CD service for building container images and deploying workloads. While it can create images, it is not a storage or registry service. Container Builder does not provide persistent storage, versioning, or vulnerability scanning for images after creation. Without a registry, teams would need to manage images elsewhere, losing operational consistency and security integration.
Artifact Registry is the correct solution because it provides a fully managed, secure, and scalable service to store, version, and scan container images. It allows teams to enforce policies, integrate with CI/CD pipelines, and track vulnerabilities before deployment. Replication, IAM-based access control, and automated scanning reduce risk and operational overhead. Artifact Registry enables organizations to maintain consistent image management practices, ensuring compliance, security, and availability for containerized workloads. Version control allows rollback and tracking of image changes, while vulnerability scanning integrates security into the software delivery process. Teams can replicate images across regions for low-latency access, providing global support for distributed teams. Artifact Registry’s integration with other Google Cloud services ensures seamless DevOps workflows, making it an enterprise-grade solution for managing containerized applications from development to production. By using Artifact Registry, organizations can ensure that container images are secure, properly versioned, and available whenever needed while maintaining operational efficiency, security, and compliance. It centralizes container image management, reduces risk, and enables teams to adopt best practices for DevOps and secure software delivery.
Question 77
A company needs a managed service for running Apache Spark and Hadoop jobs on large datasets stored in Cloud Storage. Which service should be used?
A) Dataproc
B) Cloud SQL
C) Cloud Functions
D) BigQuery
Answer: A
Explanation:
Running large-scale Apache Spark and Hadoop jobs on datasets stored in Cloud Storage requires a managed big data processing platform that supports distributed computation. Dataproc is a fully managed service that enables users to run Apache Hadoop, Spark, Hive, and Pig workloads without the operational complexity of managing clusters. Dataproc provides fast cluster provisioning, automatic scaling, and integration with Cloud Storage for direct data access. It allows organizations to process large datasets efficiently using familiar big data tools while handling infrastructure provisioning, monitoring, and cluster lifecycle management automatically. Clusters can be created on demand and deleted after job completion, reducing costs. Dataproc integrates with Cloud Logging and Cloud Monitoring to provide observability and operational insights into jobs, resource usage, and performance metrics. Users can submit batch or streaming jobs, schedule recurring workflows, and integrate with orchestration tools like Cloud Composer. Dataproc also supports GPUs and other specialized hardware for performance-intensive workloads. By using Dataproc, organizations can efficiently manage large-scale data processing pipelines while maintaining flexibility, security, and cost optimization.
Cloud SQL is a managed relational database service designed for structured transactional data. While it provides high availability and scaling within its use case, it cannot execute large distributed computation workloads like Spark or Hadoop. Attempting to run big data analytics on Cloud SQL would be inefficient, slow, and operationally complex, as it is not designed for batch processing or large-scale parallel computations.
Cloud Functions is a serverless compute service designed for event-driven workloads. Functions are short-lived and stateless, making them unsuitable for long-running distributed processing like Spark or Hadoop jobs. Cloud Functions cannot handle the large-scale data throughput or parallelization required for big data analytics and would require extensive orchestration to mimic cluster behavior.
BigQuery is a fully managed serverless data warehouse optimized for analytical queries on structured datasets. While BigQuery can process large-scale data and supports SQL-based analytics, it is not a general-purpose execution environment for Hadoop or Spark jobs. BigQuery cannot run custom Spark or Hadoop workflows, machine learning jobs in PySpark, or Pig/Hive scripts directly.
Dataproc is the correct solution because it provides a fully managed, scalable, and flexible platform for running distributed big data processing workloads on Google Cloud. It allows organizations to leverage familiar Hadoop and Spark ecosystems while integrating with Cloud Storage for high-performance input/output. Dataproc simplifies cluster management with on-demand provisioning, auto-scaling, and preemptible instances to reduce costs. Observability is integrated via Cloud Monitoring and Cloud Logging, allowing tracking of job performance, resource utilization, and error diagnostics. Dataproc also enables hybrid workflows, such as combining batch and streaming pipelines, leveraging GPUs or specialized hardware, and integrating with other Google Cloud services like Cloud Pub/Sub and BigQuery for downstream processing. Organizations can schedule jobs, automate workflows with Cloud Composer, and benefit from secure access via IAM. By using Dataproc, enterprises can run Spark, Hadoop, Hive, and Pig jobs efficiently, maintain operational simplicity, reduce costs, and ensure security and compliance. It enables rapid iteration on analytics workflows and seamless integration with data lakes stored in Cloud Storage, providing a robust, enterprise-grade solution for processing large datasets.
Question 78
A company wants to implement object versioning, retention policies, and lifecycle management for regulatory compliance. Which service should be used?
A) Cloud Storage
B) Cloud SQL
C) BigQuery
D) Firestore
Answer: A
Explanation:
Implementing object versioning, retention policies, and lifecycle management for regulatory compliance requires a storage solution that supports immutable object storage, policy enforcement, and automated transitions. Cloud Storage is a fully managed object storage service designed to meet these requirements. It allows organizations to enable versioning on buckets, automatically retaining prior versions of objects whenever they are updated or deleted. Lifecycle management policies allow automated transitions between storage classes (Standard, Nearline, Coldline, Archive) to optimize costs and enforce retention requirements. Object holds and retention policies ensure that objects cannot be deleted or overwritten until a defined period has elapsed, supporting compliance with legal or regulatory mandates. Integration with IAM provides fine-grained access control, ensuring that only authorized users can modify retention settings or access objects. Cloud Audit Logs capture all object operations, including access, modification, and deletion events, providing traceability and supporting compliance audits. Cloud Storage replication across regions enhances durability and availability for critical regulatory data. With these features, organizations can automate compliance processes, minimize operational risk, and maintain audit-ready storage solutions. Cloud Storage’s flexible API support allows integration with other cloud services and automation tools to implement policy-driven data retention and lifecycle management effectively. It also supports encryption at rest and in transit to secure sensitive regulatory data, meeting both internal and external security requirements.
Cloud SQL is a managed relational database service. While it provides encryption, access control, and auditing for structured data, it does not support object versioning, retention policies, or lifecycle management for large unstructured datasets. Using Cloud SQL to satisfy regulatory compliance for object storage is inefficient, operationally complex, and not scalable.
BigQuery is a serverless data warehouse optimized for analytics on structured data. While it supports data retention and access controls, it is not designed for object versioning or lifecycle management of individual files. Using BigQuery for regulatory storage of raw unstructured files would increase cost and operational complexity, as it is intended for analytical workloads, not compliance-focused object storage.
Firestore is a NoSQL document database optimized for real-time applications. While it provides some versioning-like capabilities through document updates, it does not support automated lifecycle management or retention policies suitable for large-scale regulatory compliance scenarios. It is not ideal for long-term storage of immutable objects with strict retention requirements.
Cloud Storage is the correct solution because it provides object versioning, retention policies, automated lifecycle management, and secure, durable storage across regions. It enables organizations to retain prior versions of objects, enforce retention periods, and automatically transition objects between storage classes to optimize costs while ensuring regulatory compliance. IAM and ACLs control access, and Cloud Audit Logs provide traceability and auditability. Lifecycle policies reduce manual effort, maintain consistency, and ensure that compliance requirements are met automatically. Integration with other Google Cloud services enables seamless automation of data workflows while maintaining secure, durable, and compliant storage. Cloud Storage allows businesses to meet industry regulations for data retention, archival, and secure access, providing a scalable, operationally efficient solution for managing regulatory-sensitive data at enterprise scale.
Question 79
A company needs to deliver content globally with caching at edge locations and SSL termination. Which Google Cloud service should be used?
A) Cloud CDN with HTTPS Load Balancer
B) Cloud SQL
C) App Engine Standard Environment
D) Cloud Functions
Answer: A
Explanation:
Delivering content globally with caching at edge locations and SSL termination requires a content delivery network integrated with a global load balancing solution. Cloud CDN with HTTPS Load Balancer provides this capability. Cloud CDN caches content at Google’s global edge points of presence, reducing latency by serving data closer to users. HTTPS Load Balancer terminates SSL connections at the edge, improving performance and offloading SSL processing from origin servers. The combination ensures secure, low-latency content delivery worldwide. Cloud CDN supports caching static and dynamic content, with cache invalidation policies to update content when needed. It integrates with Cloud Armor for DDoS protection, adding security at the edge. Traffic is routed through the closest edge location to the client, reducing round-trip time and server load. Cache hit ratios improve efficiency, reduce origin server costs, and enhance user experience. Cloud CDN also provides monitoring and logging through Cloud Monitoring and Cloud Logging for visibility into usage, performance, and errors. This fully managed architecture eliminates the need to manage individual servers in multiple regions and ensures high availability, scalability, and global reach.
Cloud SQL is a managed relational database. It is not a content delivery service and cannot cache content at edge locations or handle SSL termination. Using Cloud SQL for global content delivery is impractical because it is optimized for transactional database workloads rather than high-volume content delivery.
App Engine Standard Environment hosts web applications with autoscaling. While it can serve global users, it does not provide edge caching or built-in SSL termination at global points of presence. Latency for users far from the App Engine region may be higher, and additional infrastructure is needed for SSL and caching, increasing complexity.
Cloud Functions is a serverless compute platform. While it can serve dynamic content, it is not a global caching solution. SSL termination and edge caching are not natively integrated, and serving high-volume global traffic would require combining with other services, adding operational complexity.
Cloud CDN with HTTPS Load Balancer is the correct solution because it provides edge caching, SSL termination, and global load balancing. It improves content delivery performance, reduces latency, offloads origin servers, and enhances security. Integration with Cloud Armor and logging allows monitoring and DDoS protection. The service scales automatically and provides cost-efficient content distribution with centralized management. It is ideal for global content delivery while ensuring reliability, security, and fast access for users worldwide.
Question 80
A company wants to deploy serverless APIs that scale automatically and integrate with IAM for authentication. Which service should be used?
A) Cloud Run
B) Compute Engine
C) Cloud SQL
D) Kubernetes Engine
Answer: A
Explanation:
Deploying serverless APIs that scale automatically and integrate with IAM for authentication requires a managed service optimized for containerized applications and event-driven workloads. Cloud Run is a fully managed, serverless platform that runs stateless containers, automatically scaling from zero to accommodate traffic spikes. Each container can be deployed with HTTP endpoints to serve APIs, and Cloud Run integrates with IAM, allowing administrators to define who can invoke APIs at the project or service level. Security policies can enforce authentication for users or service accounts, ensuring only authorized clients access APIs. Cloud Run provides multiple revisions for applications, supporting traffic splitting and canary deployments for safe rollouts. Logging, monitoring, and metrics are integrated with Cloud Monitoring and Cloud Logging, providing operational visibility. Cloud Run abstracts infrastructure management, removing the need for provisioning servers, load balancers, or scaling configurations. Billing is based on usage, providing cost efficiency for APIs that may have sporadic traffic. Developers can deploy containerized applications using any language or runtime, making it flexible for modern cloud-native APIs. Cloud Run also integrates with Pub/Sub and Cloud Tasks, enabling event-driven workflows and asynchronous processing. By using Cloud Run, organizations can deploy secure, scalable, and resilient APIs without worrying about underlying infrastructure, ensuring high availability, low latency, and operational simplicity.
Compute Engine provides virtual machines for general-purpose computing. While it can host APIs, it requires manual provisioning, scaling, load balancing, and maintenance. Handling variable traffic patterns would require configuring managed instance groups, auto-scaling policies, and SSL termination manually, increasing operational overhead. Compute Engine does not natively integrate IAM-based invocation controls, requiring additional configuration or middleware for API authentication.
Cloud SQL is a relational database service for structured data. While APIs may interact with Cloud SQL as a backend, Cloud SQL itself cannot host or serve APIs. It provides storage and transaction capabilities but does not support scaling, HTTP endpoints, or event-driven execution needed for serverless APIs.
Kubernetes Engine provides a container orchestration platform. While APIs can be deployed on GKE, they require cluster management, node scaling, load balancing, and ingress configuration. Unlike Cloud Run, scaling is not fully serverless, and IAM-based authentication for API invocations requires additional setup. Kubernetes Engine is more suitable for complex microservices or multi-container orchestration rather than lightweight serverless APIs with automatic scaling.
Cloud Run is the correct solution because it provides a fully managed, serverless platform for containerized APIs. Automatic scaling from zero ensures cost efficiency and resilience to traffic spikes. IAM integration allows secure access control for users and service accounts. Revision management and traffic splitting enable safe deployments. Cloud Run integrates with monitoring, logging, and other Google Cloud services to provide operational visibility and seamless workflows. By using Cloud Run, organizations can deploy secure, resilient, and scalable APIs without managing infrastructure, ensuring simplicity, reliability, and performance. Its flexibility to run any containerized code, integration with Pub/Sub, and event-driven capabilities make it ideal for modern cloud-native applications, providing a secure and cost-effective solution for API deployment and operation. Cloud Run abstracts the complexity of server management, allows dynamic scaling, and supports security best practices, offering a fully managed serverless experience that meets enterprise API requirements effectively and efficiently.
Question 81
A company wants to migrate large amounts of data on-premises to Google Cloud with minimal network disruption. Which service should be used?
A) Transfer Appliance
B) Cloud Storage
C) Cloud SQL
D) Cloud Functions
Answer: A
Explanation:
Migrating large amounts of on-premises data to Google Cloud with minimal network disruption requires a solution that allows offline or high-capacity data transfer while minimizing bandwidth constraints. Transfer Appliance is a hardware appliance provided by Google Cloud specifically for large-scale data migrations. Organizations order the appliance, load their on-premises data directly onto it, and ship it to Google for upload into Cloud Storage or other designated cloud services. This method eliminates the need to transfer massive datasets over the internet, which could be slow or disruptive to daily network operations. Transfer Appliance supports secure data transfer with encryption at rest and in transit, ensuring data confidentiality and integrity. It is designed to handle petabyte-scale workloads efficiently and can integrate with Google Cloud’s storage, analytics, and processing pipelines upon arrival. Organizations can continue to operate normally while data is loaded onto the appliance, and once uploaded to the cloud, it becomes immediately available for analytics, storage, or processing. Transfer Appliance reduces operational complexity, network strain, and risk of incomplete or failed transfers for large datasets, making it suitable for enterprises looking to move significant volumes of data reliably and securely.
Cloud Storage is a target for the migrated data, but it does not facilitate large-scale offline migration. Direct internet transfers to Cloud Storage may be slow and could saturate on-premises networks, disrupting other operations. While Cloud Storage is essential for storing data, using it alone does not solve the challenge of efficiently moving large volumes without network disruption.
Cloud SQL is a relational database service, not a data migration service. While it can host structured data once migrated, it does not provide offline transfer capabilities or mechanisms to move large volumes from on-premises environments efficiently.
Cloud Functions is a serverless compute platform for event-driven tasks. It is not designed for large-scale data transfer or migration, and relying on it to move significant datasets would be impractical due to execution time limits and a lack of direct hardware integration.
Transfer Appliance is the correct solution because it provides a secure, scalable, and efficient way to migrate massive amounts of on-premises data with minimal network disruption. The appliance allows enterprises to load data offline, ensuring ongoing operations are unaffected by bandwidth constraints. Upon shipping to Google Cloud, data is securely uploaded to Cloud Storage or other designated services. This method reduces transfer times for petabyte-scale datasets compared to online transfers, supports encryption for security, and integrates with cloud analytics and processing workflows. Transfer Appliance provides detailed logging and monitoring of the migration process, ensuring accountability and transparency. Enterprises can combine Transfer Appliance with Cloud Storage lifecycle policies and IAM controls to manage data efficiently post-migration. By using Transfer Appliance, organizations can move large-scale datasets quickly, securely, and with minimal impact on existing network infrastructure, ensuring operational continuity while enabling cloud adoption and analytics readiness.
Question 82
A company needs a managed relational database with high availability and automatic backups for a production web application. Which service should be used?
A) Cloud SQL
B) Cloud Spanner
C) Firestore
D) BigQuery
Answer: A
Explanation:
For a managed relational database with high availability and automatic backups, Cloud SQL is a fully managed service that provides MySQL, PostgreSQL, and SQL Server instances. It ensures high availability through regional replication, failover mechanisms, and automatic backups to protect against data loss. Cloud SQL manages patching, maintenance, and updates, reducing operational overhead while maintaining reliability for production workloads. Automated backups allow point-in-time recovery, ensuring data integrity and compliance with organizational policies. Cloud SQL integrates seamlessly with Compute Engine, App Engine, Kubernetes Engine, and other Google Cloud services, providing secure access through IAM and network controls. Scaling is possible through read replicas or vertical scaling, supporting growth in traffic or data size. Monitoring, logging, and alerting are integrated to provide operational visibility and proactive management. Cloud SQL also supports encryption at rest and in transit, protecting sensitive production data. By using Cloud SQL, organizations can deploy highly available, resilient, and secure relational databases with minimal management, ensuring reliable operations for web applications and transactional workloads.
Cloud Spanner is a distributed relational database designed for global horizontal scaling and strong consistency. While highly available, it is often overkill for a single-region production web application and involves higher costs. Spanner is ideal for globally distributed transactional systems rather than standard web apps with regional requirements.
Firestore is a NoSQL document database, optimized for real-time applications and mobile/web synchronization. While it scales easily and provides high availability, it does not offer relational SQL features such as complex joins or transactional consistency at the relational level, making it less suitable for traditional web application databases.
BigQuery is a serverless data warehouse designed for analytical workloads. It is not intended for operational transactional databases. Query latency, schema rigidity, and lack of OLTP support make BigQuery unsuitable for live web application backend storage.
Cloud SQL is the correct solution because it offers a managed, reliable, and secure relational database platform. It provides high availability, automatic backups, patching, monitoring, and scalability to support production workloads. Integration with Google Cloud services allows secure, performant, and operationally efficient deployments. By using Cloud SQL, organizations can focus on application development without managing underlying database infrastructure, ensuring high reliability, automated maintenance, and secure, consistent operations for production web applications.
Question 83
A company wants to automate deployment pipelines with build, test, and deploy steps integrated with Google Cloud services. Which service should be used?
A) Cloud Build
B) Cloud Functions
C) Cloud Storage
D) App Engine Standard Environment
Answer: A
Explanation:
Automating deployment pipelines with build, test, and deploy steps requires a fully managed CI/CD service that integrates with Google Cloud services and version control systems. Cloud Build is a serverless CI/CD platform that allows teams to define workflows as build pipelines using YAML or JSON configuration files. Each pipeline can include multiple steps, such as building container images, running automated tests, scanning for vulnerabilities, and deploying applications to Compute Engine, Kubernetes Engine, Cloud Run, or App Engine. Cloud Build integrates with Git repositories such as Cloud Source Repositories, GitHub, and Bitbucket to trigger builds automatically on commits or pull requests. Security features allow integration with IAM, ensuring that only authorized users and service accounts can trigger builds or access build artifacts. Cloud Build provides build artifacts storage in Artifact Registry, supports caching for faster builds, and allows parallel execution of steps to optimize efficiency. Logging and monitoring are integrated with Cloud Logging and Cloud Monitoring, enabling teams to track build progress, detect failures, and debug issues efficiently. By using Cloud Build, organizations can implement reliable, repeatable, and scalable automation pipelines, ensuring continuous integration, continuous delivery, and rapid, secure software deployment across multiple environments.
Cloud Functions is a serverless compute platform for event-driven workloads. While Cloud Functions can automate specific tasks within a pipeline, it is not a full CI/CD platform. Functions lack integrated build orchestration, parallel execution, artifact management, and deployment automation across multiple targets, making it insufficient for end-to-end pipelines.
Cloud Storage is object storage for files and data. While it can store build artifacts, it cannot orchestrate the steps of a CI/CD pipeline, run automated tests, or deploy applications. Using Cloud Storage alone would require building extensive custom orchestration and automation logic, which would increase complexity and maintenance overhead.
App Engine Standard Environment is a managed platform for running web applications. While it can serve as a deployment target in a CI/CD pipeline, it does not provide the orchestration, build automation, or testing capabilities required for end-to-end automation. App Engine alone cannot replace a CI/CD platform for managing build, test, and deploy workflows.
Cloud Build is the correct solution because it provides a fully managed CI/CD service that automates the entire software delivery lifecycle. It integrates with repositories, triggers builds on code changes, executes parallel steps for testing and building, and deploys applications to multiple Google Cloud targets. Security, monitoring, logging, and artifact management are integrated, ensuring operational visibility and compliance. Cloud Build supports flexible configuration and workflow definitions, enabling organizations to implement complex pipelines with minimal overhead. By using Cloud Build, teams can ensure faster release cycles, reliable deployments, and reduced human error. Automated builds, tests, and deployments maintain consistency across environments, reduce operational risk, and enforce quality standards. Integration with Artifact Registry, IAM, and Cloud Monitoring provides secure, auditable, and observable pipelines. Cloud Build also supports caching, artifact versioning, and pipeline templates, making it reusable across projects and teams. Enterprises can implement modern DevOps practices with Cloud Build, achieving continuous integration, continuous delivery, and automation while reducing operational complexity and increasing deployment speed. It enables organizations to maintain high-quality software delivery processes, ensuring consistency, reliability, and secure software deployment in cloud-native environments.
Question 84
A company wants to run containerized microservices with custom networking, auto-scaling, and a managed control plane. Which Google Cloud service should be used?
A) Kubernetes Engine
B) Compute Engine
C) Cloud Functions
D) App Engine Standard Environment
Answer: A
Explanation:
Running containerized microservices with custom networking, auto-scaling, and a managed control plane requires a container orchestration platform that abstracts cluster management while providing operational flexibility. Kubernetes Engine (GKE) is a fully managed Kubernetes service that allows organizations to deploy, scale, and manage containerized applications efficiently. GKE provides a managed control plane, handling Kubernetes upgrades, patching, and high availability. Node pools allow organizations to configure custom machine types, GPU support, and autoscaling to match workload requirements. Kubernetes networking supports pod-to-pod communication, service discovery, and integration with VPC networks, enabling custom networking setups, security policies, and ingress control. GKE supports Horizontal Pod Autoscaling and Cluster Autoscaling, ensuring resources scale dynamically based on demand. Integration with Cloud Monitoring, Cloud Logging, and Cloud IAM provides observability, auditing, and secure access control. Kubernetes manifests, Helm charts, or operators can define complex microservice architectures, including dependencies, replicas, and secrets management. GKE integrates seamlessly with other Google Cloud services such as Cloud SQL, Cloud Storage, Pub/Sub, and Cloud Run, allowing hybrid architectures combining serverless and containerized workloads. By using GKE, organizations gain flexibility, reliability, and operational efficiency while offloading management of the control plane and cluster operations to Google Cloud, enabling teams to focus on deploying microservices securely, reliably, and at scale.
Compute Engine provides virtual machines for general-purpose workloads. While containerized applications can be run on VMs, managing clusters, networking, auto-scaling, and high availability requires manual configuration and significant operational effort. Compute Engine does not provide a managed Kubernetes control plane or container orchestration, increasing complexity for microservices deployments.
Cloud Functions is a serverless platform for event-driven workloads. It is not designed for orchestrating microservices or custom networking. Functions are stateless, short-lived, and cannot handle complex inter-service communication or deployment of multiple containerized services with scaling requirements. Using Cloud Functions would require rearchitecting applications to fit the serverless model, limiting flexibility.
App Engine Standard Environment is a platform for running web applications with automatic scaling. It is suitable for simple microservices but lacks support for containers, custom networking, and advanced orchestration features. Scaling and network configuration are abstracted, reducing control and flexibility for complex microservices architectures.
Kubernetes Engine is the correct solution because it provides a fully managed platform for deploying containerized microservices with operational flexibility, custom networking, auto-scaling, and integration with Google Cloud services. Managed control plane, monitoring, logging, and IAM security enable secure, reliable, and scalable deployments. Kubernetes manifests and Helm charts allow complex microservice orchestration, dependency management, and secret handling. Horizontal Pod Autoscaling, Cluster Autoscaling, and support for GPUs or custom machine types ensure workloads scale dynamically and efficiently. GKE abstracts infrastructure management, reduces operational burden, and provides high availability while maintaining flexibility for advanced networking and microservice configurations. Enterprises can deploy globally distributed microservices, integrate with managed cloud services, enforce security policies, and monitor performance seamlessly. By using GKE, organizations gain a powerful container orchestration platform for modern, scalable, and resilient microservice architectures, ensuring operational efficiency, high availability, and simplified management of complex distributed applications.
Question 85
A company wants to run a serverless function triggered by changes in Cloud Storage objects. Which service should be used?
A) Cloud Functions
B) Cloud Run
C) Compute Engine
D) App Engine Standard Environment
Answer: A
Explanation:
Running a serverless function triggered by changes in Cloud Storage requires an event-driven platform that executes code in response to cloud events. Cloud Functions is a fully managed, serverless compute platform that executes short-lived functions in response to events such as object creation, deletion, or updates in Cloud Storage. Functions can be written in multiple languages, including Node.js, Python, Go, and Java, allowing developers to handle events like file processing, notifications, and automated workflows. Cloud Functions automatically scales based on the number of incoming events, ensuring efficient resource usage without manual provisioning. Integration with IAM allows fine-grained control over which users or service accounts can trigger or access functions. Logging and monitoring are integrated with Cloud Logging and Cloud Monitoring, providing observability for function execution, error tracking, and performance metrics. Cloud Functions abstracts infrastructure management, providing a fully managed runtime environment that handles networking, scaling, and patching. Functions can also integrate with other Google Cloud services, such as Pub/Sub, Firestore, Cloud SQL, and BigQuery, enabling complex event-driven workflows. By using Cloud Functions, organizations can respond to Cloud Storage events efficiently, with minimal operational overhead and fully automated execution triggered by object changes.
Cloud Run can also run containerized workloads, but event triggers require additional orchestration, such as Pub/Sub subscriptions, and it is less tightly integrated with Cloud Storage events. While suitable for some serverless workloads, Cloud Run is not optimized for lightweight, single-purpose file-triggered functions.
Compute Engine provides virtual machines that can process Cloud Storage events, but it requires manual infrastructure setup, polling mechanisms or triggers, scaling, and patch management. Using Compute Engine for serverless event-driven workloads adds operational complexity and reduces cost efficiency.
App Engine Standard Environment can host applications that respond to HTTP requests, but triggering execution directly from Cloud Storage events requires a custom implementation with additional services like Pub/Sub. It does not provide a native serverless function model optimized for event-driven execution.
Cloud Functions is the correct solution because it provides a fully managed, serverless environment for executing code in response to Cloud Storage events. Automatic scaling, event-driven triggers, IAM-based security, logging, and integration with other Google Cloud services make it an ideal platform for lightweight, automated workflows. By using Cloud Functions, organizations can respond in real-time to object creation, modification, or deletion in Cloud Storage, enabling efficient file processing, notifications, or data transformations with minimal operational overhead. Functions handle infrastructure, scaling, and runtime management automatically while providing full observability and monitoring. This ensures a reliable, cost-effective, and scalable solution for event-driven workloads, allowing developers to focus on business logic rather than infrastructure. Cloud Functions’ tight integration with Cloud Storage and other Google Cloud services provides a seamless event-driven architecture for automated workflows and serverless operations, enabling rapid response to changes in cloud storage objects with minimal complexity and operational management.
Question 86
A company wants to build a globally distributed relational database with strong consistency for financial transactions. Which Google Cloud service should be used?
A) Cloud Spanner
B) Cloud SQL
C) Firestore
D) BigQuery
Answer: A
Explanation:
Building a globally distributed relational database with strong consistency for financial transactions requires a managed database service that can handle transactional integrity, automatic replication, and high availability across multiple regions. Cloud Spanner is a fully managed, horizontally scalable relational database that provides strong consistency, global distribution, and high availability. Spanner combines traditional relational database features, such as SQL support and ACID transactions, with global distribution capabilities, making it ideal for mission-critical financial workloads where data integrity is essential. It automatically replicates data across multiple regions, providing resilience to regional failures and maintaining transactional consistency across all replicas. Cloud Spanner supports synchronous replication for strong consistency while allowing reads from local replicas to reduce latency. It integrates with IAM for secure access control, Cloud Logging for auditability, and Cloud Monitoring for operational visibility. Automatic scaling ensures that workloads can grow horizontally without downtime, while schema changes can be applied online without interrupting operations. Cloud Spanner also supports backup and restore operations, point-in-time recovery, and encryption both at rest and in transit, ensuring security and compliance with regulatory standards. By using Cloud Spanner, organizations can deploy a globally consistent relational database that supports complex transactional operations while maintaining operational efficiency, security, and reliability across multiple regions.
Cloud SQL is a managed relational database service suitable for single-region workloads. It provides high availability through regional failover replicas and automated backups, but does not offer true global distribution or strong consistency across regions. Attempting to use Cloud SQL for a globally distributed financial application would require complex replication setups, manual failover management, and compromise on consistency, making it unsuitable for critical financial transactions.
Firestore is a NoSQL document database optimized for web and mobile applications. It provides horizontal scaling and multi-region replication, but is not a relational database. While it supports strong consistency within a single region and eventual consistency globally, it lacks ACID transactional guarantees for complex relational data models, making it unsuitable for financial transactional workloads that require relational integrity.
BigQuery is a serverless analytical data warehouse optimized for large-scale queries over structured datasets. While it offers global availability and high performance for analytics, it is not designed for transactional workloads or relational ACID guarantees. Using BigQuery for financial transactions would not provide the necessary consistency, low-latency writes, or transactional integrity required for critical financial operations.
Cloud Spanner is the correct solution because it combines relational database capabilities with global distribution and strong consistency. It allows enterprises to perform transactional operations with confidence, knowing that ACID guarantees are maintained across all regions. Automatic replication, failover, and scaling reduce operational complexity while ensuring high availability. Security and compliance features protect sensitive financial data. By using Cloud Spanner, organizations can deploy globally distributed transactional systems that meet performance, availability, and regulatory requirements. It enables low-latency read and write operations across continents while maintaining consistency, making it ideal for financial systems, e-commerce platforms, or globally distributed transactional applications. Cloud Spanner provides a unified solution for relational database management with enterprise-grade operational reliability, scalability, and security, ensuring financial data integrity and resilience in a distributed cloud environment.
Question 87
A company wants to schedule recurring data processing jobs in the cloud with managed orchestration and workflow tracking. Which service should be used?
A) Cloud Composer
B) Cloud Functions
C) Cloud Run
D) Cloud Storage
Answer: A
Explanation:
Scheduling recurring data processing jobs in the cloud with managed orchestration and workflow tracking requires a service that can define, monitor, and automate complex workflows with dependencies. Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, allowing organizations to define Directed Acyclic Graphs (DAGs) representing tasks and their dependencies. Composer provides a Python-based environment to define jobs, schedule recurring executions, and integrate with a wide range of Google Cloud services such as Cloud Storage, BigQuery, Pub/Sub, Cloud SQL, and external APIs. Workflow execution is monitored through built-in dashboards, providing visibility into job status, failures, and performance. Cloud Composer automatically handles environment management, scaling, dependency resolution, and scheduling, reducing operational overhead and ensuring reliable execution of data pipelines. It supports retries, error handling, and notification mechanisms for failed tasks, allowing organizations to implement robust workflows. Security and access control are integrated via IAM, ensuring that only authorized users can create, execute, or modify workflows. By using Cloud Composer, enterprises can implement repeatable, auditable, and scalable orchestration of data pipelines, ETL processes, and analytics workflows, providing operational efficiency and reliability for recurring processing tasks.
Cloud Functions is a serverless compute platform for event-driven tasks. While it can execute functions on a schedule using Cloud Scheduler, it does not provide comprehensive orchestration, dependency tracking, or complex workflow management. Functions are stateless, and managing multi-step pipelines would require additional logic, increasing complexity and operational overhead.
Cloud Run can host containerized workloads and can be triggered by Pub/Sub or HTTP events. However, orchestrating recurring, multi-step workflows with dependencies and retries requires additional infrastructure and logic. Cloud Run does not natively provide DAG visualization, workflow tracking, or scheduling capabilities needed for complex data pipelines.
Cloud Storage is an object storage service for files and data. It is not designed for orchestrating recurring jobs or managing workflows. While it can store input and output data, it provides no native mechanism for scheduling, dependency management, or monitoring task execution, making it unsuitable for workflow orchestration.
Cloud Composer is the correct solution because it provides a fully managed, scalable, and observable environment for scheduling recurring jobs and orchestrating multi-step workflows. DAGs allow clear representation of task dependencies, retries, and error handling. Composer integrates with Google Cloud services, enabling end-to-end pipelines for ETL, data analytics, and processing tasks. It supports security, monitoring, alerting, and operational visibility through dashboards and logs. Automatic environment management, scaling, and task scheduling reduce operational complexity while ensuring reliable and repeatable execution. By using Cloud Composer, organizations can automate complex workflows, enforce operational standards, and maintain compliance and auditability for recurring data processing tasks. It enables centralized control over pipelines, ensures tasks execute in the correct order, and provides failure notifications and retry mechanisms. Cloud Composer combines workflow management, scheduling, and monitoring into a single, fully managed solution, allowing organizations to orchestrate data workflows efficiently and reliably with minimal infrastructure management, operational risk, and manual intervention.
Question 88
A company wants to analyze large datasets with serverless, interactive SQL queries without managing infrastructure. Which Google Cloud service should be used?
A) BigQuery
B) Cloud SQL
C) Cloud Spanner
D) Firestore
Answer: A
Explanation:
Analyzing large datasets with serverless, interactive SQL queries without managing infrastructure requires a data warehouse optimized for high-performance analytics. BigQuery is a fully managed, serverless data warehouse that allows organizations to run fast, scalable SQL queries over structured and semi-structured datasets. BigQuery automatically handles resource provisioning, scaling, and query optimization, eliminating the need to manage servers or clusters. It supports petabyte-scale datasets, providing rapid response times for ad hoc queries, dashboards, and reporting. BigQuery integrates with Cloud Storage, Pub/Sub, Dataflow, and other services, enabling seamless data ingestion, transformation, and analysis. Security features include IAM-based access control, data encryption at rest and in transit, and audit logging for compliance. BigQuery supports federated queries, partitioned and clustered tables, and materialized views to improve performance and reduce costs. Billing is based on query data processed or flat-rate options for predictable costs. By using BigQuery, organizations can focus on analytics and insights rather than infrastructure management, achieving fast, cost-effective, and scalable data analysis across large datasets.
Cloud SQL is a managed relational database suitable for transactional workloads with structured data. While it supports SQL queries, it is not designed for large-scale analytics or petabyte-scale datasets. Query performance on massive datasets would be poor, and scaling requires manual configuration. Cloud SQL is more appropriate for OLTP workloads rather than analytics at scale.
Cloud Spanner is a globally distributed relational database optimized for transactional workloads. While it provides strong consistency and scalability for transactional data, it is not optimized for analytical queries across large datasets. Complex aggregation and ad hoc analytics may be inefficient and costly compared to BigQuery.
Firestore is a NoSQL document database optimized for web and mobile applications. It provides real-time data access but does not support SQL queries or large-scale analytics efficiently. It is unsuitable for batch analytics or querying terabytes of structured data using SQL.
BigQuery is the correct solution because it provides a fully managed, serverless environment for analyzing large datasets with interactive SQL queries. It automatically scales to handle complex queries over massive datasets, integrates with other Google Cloud services, and provides security, logging, and monitoring. Enterprises can use BigQuery for dashboards, reporting, ETL processes, machine learning integration, and ad hoc analytics without managing infrastructure. Partitioning, clustering, and materialized views optimize performance and cost. BigQuery enables fast insights, operational efficiency, and scalability while maintaining data security and compliance. Organizations can focus on data analysis, decision-making, and visualization rather than resource management. BigQuery supports federated queries across external datasets, integration with visualization tools like Looker, and machine learning using BigQuery ML. This serverless model ensures predictable performance, scalability, and cost-effectiveness for large-scale analytics workflows, making it an ideal choice for enterprise data analysis, business intelligence, and strategic insights across massive datasets in the cloud.
Question 89
A company wants to store structured transactional data with high availability in a regional setup. Which service should be used?
A) Cloud SQL
B) Cloud Spanner
C) Firestore
D) BigQuery
Answer: A
Explanation:
Storing structured transactional data with high availability in a regional setup requires a relational database service that can provide automatic backups, failover, and secure access. Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server. It provides automated high availability by configuring primary and standby instances in a regional setup. Failover is automatic in case of a primary instance failure, minimizing downtime for applications. Cloud SQL handles backups, point-in-time recovery, maintenance, patching, and updates automatically, reducing operational overhead. Integration with IAM allows fine-grained access control, ensuring only authorized users and service accounts can access the database. Monitoring and logging are provided through Cloud Monitoring and Cloud Logging, giving visibility into database performance, resource usage, and events. Cloud SQL supports vertical scaling, read replicas, and secure network connections through private IP or VPC peering. By using Cloud SQL, organizations can ensure reliable, consistent storage for transactional data with automated maintenance, high availability, and minimal operational complexity, making it suitable for production web applications and business-critical workloads.
Cloud Spanner is a globally distributed database optimized for multi-region workloads. While it provides strong consistency and high availability, it is more complex and costlier than Cloud SQL for a simple regional transactional setup. Spanner’s global capabilities are unnecessary if the workload is confined to a single region, making Cloud SQL more appropriate.
Firestore is a NoSQL document database. While it supports regional high availability and scaling, it is not a relational database and is less suitable for transactional workloads that require ACID guarantees, a structured schema, or SQL queries.
BigQuery is a serverless data warehouse optimized for analytical queries. It is not intended for transactional workloads, high-frequency inserts, updates, or relational schema enforcement. Using BigQuery for transactional data would not meet the performance or ACID requirements of production applications.
Cloud SQL is the correct solution because it provides a managed relational database with regional high availability, automatic failover, backups, patching, monitoring, and IAM-based access control. It ensures operational simplicity while maintaining transactional integrity, security, and reliability. Enterprises can deploy regional databases for production workloads with confidence in performance, availability, and operational efficiency, while reducing management overhead. It integrates with other Google Cloud services for application backends, analytics, and data pipelines, making it an ideal choice for structured transactional data in a regional setup.
Question 90
A company wants to store and query semi-structured JSON documents with real-time updates. Which Google Cloud service should be used?
A) Firestore
B) Cloud SQL
C) Cloud Spanner
D) BigQuery
Answer: A
Explanation:
Storing and querying semi-structured JSON documents with real-time updates requires a NoSQL document database designed for scalable, low-latency operations. Firestore is a fully managed, serverless document database that allows organizations to store JSON-like documents in collections with hierarchical data structures. Firestore supports real-time updates, ensuring that changes to documents are propagated instantly to clients subscribed to the data. This feature enables building responsive applications such as collaborative tools, chat applications, or dashboards. Firestore handles automatic scaling, high availability, and replication across regions to ensure durability and low-latency access. Security is enforced through IAM and Firestore security rules, providing fine-grained control over which users or applications can read or write data. Firestore also supports offline persistence for client applications, automatic synchronization when network connectivity resumes, and transaction support for multi-document updates. It integrates with Cloud Functions, enabling event-driven workflows and automated processing when documents change. Firestore’s query capabilities allow filtering, ordering, and indexing of documents, providing flexibility in data retrieval while maintaining low-latency access. By using Firestore, organizations can efficiently manage semi-structured JSON documents, ensure real-time data availability, and build scalable, responsive applications with minimal operational overhead.
Cloud SQL is a relational database designed for structured data. While it supports JSON data types, it is not optimized for real-time updates, hierarchical data structures, or automatic client synchronization. Using Cloud SQL for semi-structured, frequently updated documents would result in increased complexity and slower response times for real-time applications.
Cloud Spanner is a globally distributed relational database. While it can store structured data and provide strong consistency, it is not optimized for JSON document storage with real-time synchronization to clients. Spanner is more suitable for transactional workloads rather than event-driven document-centric applications.
BigQuery is a serverless data warehouse optimized for analytics. While it can store and query JSON data, it is intended for batch or interactive analytical queries, not real-time updates. It cannot provide low-latency document updates or client synchronization needed for responsive applications.
Firestore is the correct solution because it provides a fully managed, serverless, real-time NoSQL document database for semi-structured JSON documents. Automatic scaling, replication, security rules, offline persistence, real-time synchronization, and integration with event-driven workflows make it ideal for building responsive, client-facing applications. Firestore ensures low-latency access, operational simplicity, and scalability while allowing developers to focus on application logic. By using Firestore, organizations can efficiently manage semi-structured data, maintain real-time responsiveness, and ensure security and high availability for applications that rely on dynamic document updates and real-time user interactions.