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Question 1
Your company is migrating to Google Cloud Platform and needs to choose a storage solution for frequently accessed structured data with strong consistency requirements. Which GCP storage service should you use?
A) Cloud Storage
B) Cloud Spanner
C) Cloud Bigtable
D) Cloud Datastore
Answer: B
Explanation:
Selecting the appropriate storage solution in Google Cloud Platform requires understanding the characteristics of each service and matching them to application requirements including data structure, consistency needs, access patterns, and scale requirements. GCP offers multiple storage services optimized for different use cases. Cloud Spanner provides globally distributed relational database capabilities with strong consistency, while Cloud Storage offers object storage for unstructured data, Cloud Bigtable provides NoSQL wide-column storage for analytical workloads, and Cloud Datastore offers document-based NoSQL storage.
Cloud Spanner is the optimal choice for frequently accessed structured data with strong consistency requirements. It provides horizontally scalable relational database functionality with ACID transaction support and strong consistency across global deployments. Cloud Spanner combines the benefits of traditional relational databases like SQL support and schema enforcement with the scalability of NoSQL databases. It offers external consistency which is the strongest consistency level, automatic replication and high availability, support for standard SQL queries and transactions, and horizontal scaling to handle petabytes of data. For applications requiring structured data with relational integrity and strong consistency such as financial systems, inventory management, or customer relationship management, Cloud Spanner provides the right balance of consistency, performance, and scalability.
A is incorrect because Cloud Storage is designed for unstructured object storage like images, videos, and backups rather than structured data requiring strong consistency and transaction support. C is incorrect because Cloud Bigtable is optimized for large analytical and operational workloads with eventual consistency, not strong consistency requirements for transactional data. D is incorrect because Cloud Datastore provides eventual consistency by default and is designed for semi-structured document data rather than the strongly consistent structured data requirements specified in the question.
Question 2
You need to grant a user the ability to create and delete Compute Engine instances but not modify existing instances. Which predefined IAM role should you assign?
A) roles/compute.admin
B) roles/compute.instanceAdmin.v1
C) roles/compute.viewer
D) Custom role with specific permissions
Answer: D
Explanation:
Identity and Access Management in Google Cloud Platform follows the principle of least privilege where users receive only the permissions necessary to perform their specific job functions. Predefined IAM roles bundle related permissions for common use cases, but sometimes these roles provide either too much or too little access for specific requirements. Understanding when to use predefined roles versus custom roles is essential for secure cloud administration.
The requirement to allow creating and deleting instances but not modifying existing instances is highly specific and not covered by any predefined Compute Engine role. All predefined roles that allow instance creation and deletion also include permissions to modify existing instances. Therefore, a custom role with specific permissions is necessary. Custom roles allow administrators to create precise permission sets matching exact requirements. For this scenario, the custom role would include permissions like compute.instances.create, compute.instances.delete, and related permissions for disk and network attachment needed during creation, while explicitly excluding permissions like compute.instances.setMetadata, compute.instances.stop, compute.instances.start, and other modification permissions. This granular control ensures the user can perform required tasks without excessive privileges.
A is incorrect because roles/compute.admin provides full administrative access to all Compute Engine resources including modifying, starting, stopping, and configuring existing instances, which exceeds the requirements. B is incorrect because roles/compute.instanceAdmin.v1 includes permissions to modify existing instances including start, stop, reset, update, and set metadata operations. C is incorrect because roles/compute.viewer only provides read access without any ability to create or delete instances.
Question 3
Your application running on Compute Engine needs to access Cloud Storage buckets. What is the most secure way to authenticate the application?
A) Create a service account key and store it in the instance
B) Use the default Compute Engine service account with appropriate IAM roles
C) Embed Cloud Storage credentials in the application code
D) Use a user account with Cloud Storage permissions
Answer: B
Explanation:
Authentication and authorization for applications running in Google Cloud Platform should follow security best practices that minimize credential exposure and simplify key management. Compute Engine instances can authenticate to GCP services using service accounts which are special accounts intended for applications and services rather than individual users. Each Compute Engine instance runs with an associated service account that provides identity for authentication.
Using the default Compute Engine service account with appropriate IAM roles is the most secure approach because it eliminates the need to manage service account keys. When an instance uses its service account, it automatically obtains short-lived access tokens from the metadata server without requiring stored credentials. The application uses Google Cloud client libraries which automatically retrieve these tokens transparently. This approach provides several security advantages including no service account keys to manage or secure, automatic token rotation without application intervention, audit logging of service account activity, and simple permission management through IAM roles attached to the service account. The Compute Engine instance metadata server provides authentication tokens only to code running on that specific instance, preventing token theft. For Cloud Storage access, you would assign roles like roles/storage.objectViewer or roles/storage.objectAdmin to the service account.
A is incorrect because storing service account keys on instances creates security risks from key theft, requires key rotation management, and represents unnecessary credential exposure when the metadata server provides automatic authentication. C is incorrect because embedding credentials in code creates severe security vulnerabilities including credential exposure in version control, difficulty updating credentials, and potential unauthorized access if code is compromised. D is incorrect because user accounts are intended for human users, not application authentication, and don’t provide the automatic credential management that service accounts offer.
Question 4
You need to ensure high availability for a web application deployed on Compute Engine. Which approach should you implement?
A) Deploy the application on a single large instance
B) Use a managed instance group with autoscaling across multiple zones
C) Deploy on a single instance with regular snapshots
D) Use preemptible VMs to reduce costs
Answer: B
Explanation:
High availability in cloud environments requires architecting applications to survive infrastructure failures including zone outages, hardware failures, and maintenance events. Google Cloud Platform provides several features for building resilient applications, with managed instance groups being the primary mechanism for achieving high availability for stateless applications on Compute Engine.
Using a managed instance group with autoscaling across multiple zones provides comprehensive high availability by distributing instances across different failure domains. Managed instance groups automatically maintain a specified number of instances based on an instance template, automatically replace failed instances, distribute instances across multiple zones within a region for zone-level redundancy, and integrate with load balancers for traffic distribution. Autoscaling adds the ability to automatically adjust instance count based on metrics like CPU utilization, load balancing traffic, or custom metrics. Multi-zone deployment ensures that if an entire zone becomes unavailable, instances in other zones continue serving traffic. The health check mechanism automatically detects and replaces unhealthy instances, maintaining application availability. This architecture provides fault tolerance at multiple levels including instance-level through automatic replacement, zone-level through multi-zone distribution, and capacity-level through autoscaling to handle traffic variations.
A is incorrect because a single large instance creates a single point of failure where any instance failure causes complete application unavailability, and provides no mechanism for handling traffic spikes beyond the instance’s capacity. C is incorrect because while snapshots enable recovery, they don’t provide high availability since recovery from snapshots requires manual intervention and results in downtime during the recovery process. D is incorrect because preemptible VMs can be terminated by Google Cloud at any time and are unsuitable as the sole foundation for high availability, though they can be mixed with regular instances in some architectures.
Question 5
Your organization wants to minimize data egress costs while accessing frequently used data from Cloud Storage. Which storage class should you use?
A) Standard
B) Nearline
C) Coldline
D) Archive
Answer: A
Explanation:
Google Cloud Storage offers different storage classes optimized for various access patterns and cost requirements. Each storage class has different pricing for storage costs, data retrieval costs, and operations. Understanding these trade-offs helps optimize costs based on actual usage patterns. The storage classes range from Standard for frequently accessed data to Archive for rarely accessed data, with corresponding differences in per-GB storage costs and retrieval costs.
Standard storage class is optimal for minimizing data egress costs with frequently accessed data because it has no retrieval fees and the lowest per-operation costs. While Standard storage has higher per-GB storage costs compared to other classes, it charges nothing for data retrieval or early deletion, making it cost-effective for data accessed frequently. For frequently accessed data, the costs from retrieval fees in other storage classes would exceed the higher storage costs of Standard. Standard storage provides high availability and low latency, making it suitable for active workloads including website content, streaming videos, mobile applications, and frequently analyzed data. The cost model is straightforward with predictable monthly storage charges based on data volume and standard network egress pricing without additional retrieval penalties.
B is incorrect because Nearline storage is designed for data accessed less than once per month and charges retrieval fees that would accumulate significantly for frequently accessed data, making it more expensive overall than Standard despite lower storage costs. C is incorrect because Coldline storage targets data accessed less than once per quarter with higher retrieval fees than Nearline, making it inappropriate and expensive for frequent access. D is incorrect because Archive storage is intended for data accessed less than once per year with the highest retrieval fees and minimum storage duration requirements, making it completely unsuitable for frequently accessed data.
Question 6
You need to provide temporary access to a Cloud Storage object for external users without requiring them to have Google accounts. What should you use?
A) IAM roles
B) Signed URLs
C) Public bucket access
D) Service account keys
Answer: B
Explanation:
Sharing Cloud Storage objects with external users requires mechanisms that provide controlled access without exposing the entire bucket or requiring complex authentication. Google Cloud Storage offers several access control methods, each appropriate for different scenarios. For temporary access to specific objects without requiring user accounts, signed URLs provide the ideal solution.
Signed URLs provide temporary access to specific Cloud Storage objects for external users without requiring Google accounts by encoding authentication information directly in the URL. A signed URL contains cryptographic signature created using service account credentials that grants temporary access to a specific object for a defined time period. The creator specifies the HTTP method allowed, the expiration time, and the specific object path. Users with the signed URL can access the object by simply making HTTP requests to that URL without any additional authentication. This approach is commonly used for allowing users to download files, providing temporary upload access for file submissions, sharing time-limited content, and integrating with external systems that need temporary access. Signed URLs can be configured with various restrictions including time limits from minutes to days, specific HTTP methods like GET or PUT, and IP address restrictions for additional security.
A is incorrect because IAM roles require users to have Google accounts and authenticate before accessing resources, which doesn’t meet the requirement for external users without Google accounts. C is incorrect because making buckets public exposes all objects to everyone indefinitely without time limits or granular control, creating security risks and not providing the controlled temporary access required. D is incorrect because service account keys would require external users to implement authentication logic and manage keys, adding complexity and security concerns rather than providing simple temporary access.
Question 7
Your application needs to store session data that requires sub-millisecond latency and automatic expiration. Which GCP service should you use?
A) Cloud SQL
B) Cloud Memorystore
C) Cloud Storage
D) Cloud Firestore
Answer: B
Explanation:
Different applications have different performance and functionality requirements for data storage. Session data typically requires very low latency access since it’s accessed on every user request, and automatic expiration to remove stale sessions without manual cleanup. Google Cloud Platform offers various data storage services optimized for different latency requirements and feature sets.
Cloud Memorystore is the optimal choice for storing session data requiring sub-millisecond latency and automatic expiration because it provides fully managed Redis or Memcached instances optimized for caching and low-latency operations. Cloud Memorystore delivers sub-millisecond read and write latency making it suitable for session management where every millisecond of latency affects user experience. It natively supports TTL (time-to-live) for automatic expiration of session data, eliminating the need for manual cleanup logic. Cloud Memorystore provides high availability with replication, automatic failover, easy integration with applications through standard Redis or Memcached protocols, and the ability to handle millions of operations per second. For session management, applications typically store serialized session objects in Memorystore with appropriate TTL values, and the service automatically removes expired sessions. This approach significantly improves application performance compared to database-backed sessions.
A is incorrect because Cloud SQL is a relational database service that provides latency in the milliseconds range, not sub-millisecond, and while it can implement TTL through scheduled jobs, it’s not optimized for the high-throughput, low-latency access patterns of session management. C is incorrect because Cloud Storage is object storage with latency in tens of milliseconds and doesn’t provide automatic expiration or the performance characteristics needed for session data. D is incorrect because Cloud Firestore, while offering good performance, typically has single-digit millisecond latency rather than sub-millisecond, and isn’t optimized for the specific session management use case.
Question 8
You need to run a batch processing job that processes large amounts of data but can tolerate interruptions. Which Compute Engine instance type provides the most cost-effective solution?
A) Standard instances
B) Preemptible VMs
C) High-memory instances
D) Sole-tenant nodes
Answer: B
Explanation:
Compute Engine offers various instance types with different pricing models and characteristics. Selecting the appropriate instance type requires understanding workload characteristics including fault tolerance, duration, performance requirements, and budget constraints. For workloads that can tolerate interruptions, preemptible VMs offer significant cost savings compared to standard instances.
Preemptible VMs provide the most cost-effective solution for batch processing jobs that can tolerate interruptions because they offer up to 80 percent discount compared to standard instances. Preemptible VMs are Compute Engine instances that Google Cloud can terminate at any time if the capacity is needed elsewhere, with a maximum lifetime of 24 hours. They receive a 30-second termination notice allowing graceful shutdown. For batch processing that can checkpoint progress and resume after interruption, preemptible VMs dramatically reduce costs. Best practices include designing jobs to handle preemption gracefully by saving state periodically, using managed instance groups to automatically replace preempted instances, breaking large jobs into smaller tasks that complete within the 24-hour limit, and implementing retry logic. Many batch processing frameworks like Apache Spark and data processing pipelines naturally support this interruption model, making preemptible VMs ideal for big data processing, rendering, scientific computing, and other fault-tolerant workloads.
A is incorrect because standard instances cost significantly more than preemptible VMs without providing additional benefit for workloads that can tolerate interruptions. C is incorrect because high-memory instances address memory requirements, not cost optimization, and are more expensive than standard instances of similar CPU capacity. D is incorrect because sole-tenant nodes provide physical server isolation for compliance or licensing requirements and are the most expensive option, inappropriate for cost-sensitive batch processing.
Question 9
Your company needs to analyze streaming data from IoT devices in real-time. Which GCP service should you use to ingest the data?
A) Cloud Storage
B) Cloud Pub/Sub
C) Cloud SQL
D) BigQuery
Answer: B
Explanation:
Real-time data streaming architectures require services that can handle high-volume data ingestion with low latency and reliable delivery. Google Cloud Platform provides Cloud Pub/Sub as a fully managed messaging service designed specifically for streaming data ingestion and real-time event distribution. Understanding the role of each component in a streaming architecture is essential for proper design.
Cloud Pub/Sub is the correct service for ingesting streaming data from IoT devices because it provides scalable, reliable, real-time messaging infrastructure designed for high-volume data ingestion. Cloud Pub/Sub operates as a globally distributed message queue that decouples data producers like IoT devices from data consumers like analytics systems. It supports at-least-once message delivery, automatically scales to handle millions of messages per second, provides global endpoints for low-latency ingestion from anywhere, and integrates with downstream processing services. IoT devices publish messages to Pub/Sub topics, and subscribers like Cloud Dataflow, Cloud Functions, or custom applications consume these messages for processing. This architecture provides fault tolerance through message persistence, flexibility to add or remove subscribers without affecting publishers, and the ability to handle traffic spikes without data loss. Pub/Sub is commonly used with Cloud Dataflow for stream processing and BigQuery for storing analyzed results.
A is incorrect because Cloud Storage is designed for object storage, not real-time streaming data ingestion, and doesn’t provide the messaging semantics or low-latency characteristics needed for streaming IoT data. C is incorrect because Cloud SQL is a relational database not designed for high-volume message ingestion from streaming sources, and would become a bottleneck for thousands of concurrent IoT device connections. D is incorrect because BigQuery is a data warehouse for analytics, not a data ingestion service, though it’s commonly used as a destination for data after processing streamed through Pub/Sub.
Question 10
You need to execute a function in response to HTTP requests without managing servers. Which GCP service should you use?
A) Compute Engine
B) Google Kubernetes Engine
C) Cloud Functions
D) App Engine
Answer: C
Explanation:
Serverless computing allows developers to run code without managing infrastructure, with automatic scaling and pay-per-use pricing. Google Cloud Platform offers several compute options ranging from fully managed infrastructure to serverless, each appropriate for different use cases. Selecting the right service depends on workload characteristics, management preferences, and architectural requirements.
Cloud Functions is the optimal choice for executing functions in response to HTTP requests without managing servers because it provides fully serverless, event-driven compute that automatically scales and charges only for actual execution time. Cloud Functions allows deploying individual functions triggered by HTTP requests, Cloud Pub/Sub messages, Cloud Storage changes, or other events. For HTTP-triggered functions, Cloud Functions automatically provisions HTTPS endpoints, handles scaling from zero to thousands of concurrent executions, manages all infrastructure including servers, networking, and load balancing, and charges based on invocation count and execution duration. Functions execute in isolated environments with automatic resource allocation based on configured memory limits. This serverless model is ideal for lightweight API endpoints, webhooks, microservices, and event handlers where you want to focus purely on code without infrastructure management. Cloud Functions supports multiple runtimes including Node.js, Python, Go, Java, and .NET.
A is incorrect because Compute Engine requires managing virtual machine instances including provisioning, configuration, scaling, and maintenance, which contradicts the requirement for serverless execution. B is incorrect because Google Kubernetes Engine requires managing container orchestration and clusters even though it’s more managed than Compute Engine. D is incorrect because while App Engine is a Platform-as-a-Service that reduces management overhead compared to Compute Engine, it still requires more configuration and management than Cloud Functions, and doesn’t provide the same event-driven, pay-per-invocation model for simple function execution.
Question 11
Your application deployed on Google Kubernetes Engine needs persistent storage that can be accessed from multiple pods simultaneously with read-write access. Which storage solution should you use?
A) Persistent Volume with ReadWriteOnce access mode
B) Persistent Volume with ReadWriteMany access mode using Filestore
C) Local SSD
D) Cloud Storage mounted as a volume
Answer: B
Explanation:
Kubernetes storage requirements vary based on application architecture, with some applications needing exclusive access to storage while others require shared access across multiple pods. Google Kubernetes Engine supports various storage options through the Persistent Volume abstraction, each with different access modes and characteristics. Understanding these options is critical for architecting containerized applications correctly.
Persistent Volume with ReadWriteMany access mode using Filestore is the correct solution for storage that multiple pods can access simultaneously with read-write capability. Filestore provides fully managed NFS file storage that supports the ReadWriteMany access mode required for concurrent write access from multiple pods. When a Persistent Volume Claim requests ReadWriteMany access, GKE can provision Filestore volumes that mount into multiple pods simultaneously, allowing them to read and write the same data. This is essential for applications requiring shared file access such as content management systems, shared configuration storage, machine learning training with shared datasets, and collaborative editing applications. Filestore offers high performance, predictable latency, and supports standard file operations, making it suitable for applications expecting traditional file system semantics. The managed nature eliminates operational overhead of maintaining file servers.
A is incorrect because ReadWriteOnce access mode allows mounting to only a single node at a time, preventing multiple pods from accessing the volume simultaneously even if they’re on the same node. C is incorrect because Local SSDs are ephemeral storage attached to specific nodes, cannot be shared across pods on different nodes, and data is lost when the node is deleted. D is incorrect because Cloud Storage uses object storage semantics rather than file system semantics and mounting it directly as a volume doesn’t provide true concurrent read-write access with proper file locking and consistency guarantees needed for many applications.
Question 12
You need to deploy a containerized application that automatically scales based on CPU utilization. Which GCP service provides the simplest fully managed solution?
A) Compute Engine with containers
B) Google Kubernetes Engine
C) Cloud Run
D) App Engine Flexible
Answer: C
Explanation:
Deploying containerized applications in Google Cloud Platform offers multiple options with varying levels of management and control. The choice depends on complexity requirements, scaling needs, and desired operational overhead. Understanding when to use fully managed serverless platforms versus more configurable orchestration platforms is important for efficient cloud operations.
Cloud Run provides the simplest fully managed solution for deploying containerized applications with automatic scaling because it offers serverless container execution without Kubernetes cluster management. Cloud Run automatically scales from zero to thousands of instances based on incoming requests, charges only for actual request processing time and allocated resources during execution, provides automatic HTTPS endpoints, handles load balancing and traffic splitting, and requires minimal configuration. Developers simply deploy container images and Cloud Run manages all infrastructure. For CPU-based autoscaling, Cloud Run automatically allocates CPU during request processing and scales instances based on concurrency limits and request volume. This serverless approach eliminates cluster management, node provisioning, and capacity planning. Cloud Run supports any container that listens on a port and can be built from any language or framework. It’s ideal for stateless services, APIs, websites, and event-driven processing where you want maximum simplicity with automatic scaling.
A is incorrect because Compute Engine with containers requires managing virtual machine instances including scaling logic, load balancing configuration, and operational overhead, providing more control but less simplicity than serverless options. B is incorrect because Google Kubernetes Engine requires managing Kubernetes clusters including node pools, control plane interaction, and more complex configuration despite being managed Kubernetes, offering more flexibility but less simplicity than Cloud Run. D is incorrect because App Engine Flexible, while managed, requires more configuration than Cloud Run and is designed for traditional application deployment patterns rather than pure serverless container execution.
Question 13
Your organization needs to audit all API calls made within your GCP project. Which service should you use?
A) Cloud Monitoring
B) Cloud Logging
C) Cloud Trace
D) Cloud Profiler
Answer: B
Explanation:
Security and compliance in Google Cloud Platform require comprehensive audit trails showing who performed what actions and when. GCP provides various observability services for monitoring, logging, tracing, and profiling applications and infrastructure. Understanding which service provides what type of visibility is essential for meeting security and compliance requirements.
Cloud Logging is the correct service for auditing all API calls within a GCP project because it automatically records Admin Activity audit logs and Data Access audit logs for GCP services. Admin Activity logs capture administrative actions like creating instances, modifying IAM permissions, and changing configurations without any configuration required. Data Access logs record API calls that read or modify user data and can be enabled per service as needed. These audit logs include detailed information about who made the call through principal email, what action was performed through method name, when it occurred through timestamp, where it originated through source IP and location, and what resources were affected. Cloud Logging provides audit logs with tamper-evident storage, long-term retention capabilities, integration with Security Command Center, export to BigQuery for analysis, and search capabilities for investigation. These logs are essential for compliance with standards like SOC 2, ISO 27001, and regulatory requirements.
A is incorrect because Cloud Monitoring focuses on metrics, alerting, and performance monitoring rather than detailed audit trails of API calls, though it can alert on metric thresholds. C is incorrect because Cloud Trace analyzes application latency and distributed tracing for performance optimization, not security auditing of API calls. D is incorrect because Cloud Profiler analyzes application CPU and memory usage for performance optimization, not audit logging of administrative and data access activities.
Question 14
You need to create a custom VPC network with specific IP ranges for different subnets. Which VPC network type should you use?
A) Auto mode VPC network
B) Legacy network
C) Custom mode VPC network
D) Default VPC network
Answer: C
Explanation:
Virtual Private Cloud networks in Google Cloud Platform provide isolated network environments for resources. GCP offers different VPC network creation modes that determine how subnets are created and managed. Selecting the appropriate network mode depends on whether you need automatic subnet creation or precise control over network topology and IP addressing.
Custom mode VPC network is the correct choice for creating networks with specific IP ranges for different subnets because it provides complete control over subnet creation and IP range assignment. In custom mode, you manually create each subnet specifying its IP range, region, and other properties. This allows implementing specific IP addressing schemes required by organizational standards, avoiding IP range conflicts with on-premises networks or other cloud environments, creating subnets only in regions where resources will be deployed, and optimizing IP address allocation based on actual needs. Custom mode networks are essential for enterprise deployments requiring IP address planning, hybrid cloud connectivity with consistent addressing, compliance with networking standards, and precise control over network segmentation. Each subnet is regional, spanning all zones within a region, and you can expand subnet IP ranges after creation if needed without recreating the subnet.
A is incorrect because auto mode VPC networks automatically create one subnet in each GCP region with predetermined IP ranges from the 10.128.0.0/9 CIDR block, not allowing specification of custom IP ranges for specific subnets. B is incorrect because legacy networks are deprecated and use a single global IP range without regional subnets, not recommended for new deployments. D is incorrect because the default VPC network is an auto mode network automatically created in new projects with predefined subnets in all regions, not providing the control needed for custom IP range requirements.
Question 15
Your application needs to access secrets like database passwords and API keys securely. Which GCP service should you use to store and manage these secrets?
A) Cloud Storage with encryption
B) Secret Manager
C) Cloud KMS
D) Environment variables
Answer: B
Explanation:
Managing sensitive information like passwords, API keys, and certificates requires dedicated secret management solutions that provide secure storage, access control, auditing, and rotation capabilities. Applications should never store secrets in code, configuration files, or environment variables visible in instance metadata. Google Cloud Platform provides specialized services for different aspects of security and encryption.
Secret Manager is the correct service for storing and managing secrets like database passwords and API keys because it provides centralized, secure secret storage with version control, access auditing, and integration with GCP services. Secret Manager encrypts secrets at rest using Google-managed or customer-managed encryption keys, provides fine-grained IAM access control to individual secrets, maintains version history allowing rollback to previous secret values, integrates with Cloud Build, Cloud Functions, and other services for seamless secret injection, and logs all secret access for auditing. Applications retrieve secrets at runtime using Secret Manager API rather than storing them in code or configuration. This approach provides secure secret lifecycle management including creation, rotation, and deletion, prevents secret sprawl across configuration files, enables central audit trails of secret access, and supports compliance requirements. Secret Manager also provides automatic replication across regions for high availability.
A is incorrect because while Cloud Storage with encryption protects data at rest, it’s designed for file and object storage rather than structured secret management with versioning and access control optimized for credentials. C is incorrect because Cloud KMS manages encryption keys used to encrypt data but isn’t designed for storing application secrets like passwords and API keys; it’s complementary to Secret Manager which can use KMS keys for encryption. D is incorrect because storing secrets in environment variables exposes them in process listings, logs, and instance metadata, creating security vulnerabilities, and provides no secret rotation, versioning, or access auditing capabilities.
Question 16
You need to route traffic to the nearest healthy backend service across multiple regions for a global application. Which load balancing option should you use?
A) Internal TCP/UDP Load Balancing
B) Network Load Balancing
C) HTTP(S) Load Balancing
D) Regional Internal Load Balancing
Answer: C
Explanation:
Load balancing in Google Cloud Platform comes in various types designed for different use cases, protocols, and deployment patterns. Global applications requiring low latency and high availability need load balancing solutions that can route traffic intelligently based on geographic proximity and backend health across multiple regions.
HTTP(S) Load Balancing is the correct choice for routing traffic to the nearest healthy backend service across multiple regions because it provides global load balancing with automatic failover and intelligent routing. HTTP(S) Load Balancing operates at Layer 7 application layer, uses a single global anycast IP address that routes users to the nearest point of presence, automatically directs traffic to healthy backends in the closest region with available capacity, provides cross-region failover when backends become unhealthy, and supports advanced traffic management including URL-based routing and traffic splitting. The global nature means users in Asia route to Asian backends while European users route to European backends automatically based on network proximity. Health checks continuously monitor backend health and remove unhealthy backends from rotation. This load balancing also provides SSL/TLS termination, Cloud CDN integration, Cloud Armor DDoS protection, and IPv6 support. It’s ideal for global web applications, APIs, and services requiring worldwide low-latency access.
A is incorrect because Internal TCP/UDP Load Balancing is designed for internal traffic within a VPC network and doesn’t provide global routing or public internet accessibility. B is incorrect because Network Load Balancing is regional, not global, and operates at Layer 4 without application-layer awareness, making it unsuitable for global multi-region routing. D is incorrect because Regional Internal Load Balancing is limited to a single region and designed for internal traffic, not global external traffic routing across multiple regions.
Question 17
Your team needs to implement CI/CD pipelines for deploying applications to GCP. Which service provides native integration with GCP resources for building and deploying?
A) Jenkins on Compute Engine
B) Cloud Build
C) GitLab CI
D) CircleCI
Answer: B
Explanation:
Continuous Integration and Continuous Deployment pipelines automate building, testing, and deploying applications, improving development velocity and reliability. While many CI/CD tools can work with Google Cloud Platform, native GCP services provide tighter integration, simpler configuration, and better security through built-in service account authentication.
Cloud Build is the correct service for implementing CI/CD pipelines with native GCP integration because it provides fully managed continuous integration and deployment infrastructure specifically designed for Google Cloud Platform. Cloud Build executes builds using configurable steps defined in YAML configuration files, supports multiple source repositories including Cloud Source Repositories, GitHub, and Bitbucket, natively integrates with other GCP services through automatic service account authentication, and provides built-in container image building and pushing to Container Registry or Artifact Registry. Cloud Build triggers can automatically execute builds on code commits, pull requests, or tags, enabling fully automated CI/CD workflows. The service handles parallel build execution, caching for faster builds, and integration with Binary Authorization for deployment security. Cloud Build is commonly used for building containers, deploying to GKE, Cloud Run, App Engine, or Compute Engine, running tests, and executing deployment scripts, all with native understanding of GCP resources and IAM permissions.
A is incorrect because while Jenkins can be installed on Compute Engine and configured for GCP deployment, it requires managing the Jenkins infrastructure, configuring authentication manually, and lacks the native integration and serverless nature of Cloud Build. C is incorrect because GitLab CI is a third-party service requiring authentication configuration and lacking the native GCP service integration provided by Cloud Build. D is incorrect because CircleCI, like GitLab CI, is a third-party service without the same level of native GCP integration and requiring separate authentication management.
Question 18
You need to deploy multiple applications sharing the same Compute Engine instances with strong isolation between them. Which technology should you use?
A) Separate Compute Engine instances per application
B) Containers using Docker
C) Different user accounts on the same instance
D) Separate VPC networks
Answer: B
Explanation:
Efficiently utilizing compute resources while maintaining application isolation requires appropriate abstraction layers. Traditional approaches like running multiple applications directly on operating systems can lead to dependency conflicts and security concerns. Containerization provides lightweight isolation that enables efficient resource sharing while maintaining application independence.
Containers using Docker provide the best solution for deploying multiple applications on shared Compute Engine instances with strong isolation. Containers package applications with their dependencies in isolated runtime environments that share the host operating system kernel while maintaining separate file systems, process namespaces, network namespaces, and resource allocations. This provides several advantages including strong isolation between applications preventing dependency conflicts, efficient resource utilization by sharing the operating system, consistent behavior across development and production environments, easy deployment and scaling of individual applications, and standardized packaging format. Multiple containers running on the same Compute Engine instance operate independently with separate networking, file systems, and process spaces. Container orchestration platforms like Kubernetes further enhance multi-container deployments with automated scheduling, health monitoring, and scaling. Containers are industry standard for microservices architecture and modern application deployment providing the right balance of isolation and efficiency.
A is incorrect because using separate Compute Engine instances per application is inefficient and expensive, wasting resources when applications don’t fully utilize instance capacity, and increasing operational overhead. C is incorrect because different user accounts provide weak isolation that doesn’t prevent dependency conflicts, doesn’t isolate network ports, and doesn’t provide the process-level isolation and resource control that containers offer. D is incorrect because separate VPC networks control network connectivity between instances but don’t provide isolation for applications running on the same instance, and would still require separate instances per application to achieve isolation.
Question 19
Your application needs to perform complex SQL queries on large datasets updated hourly. Which GCP storage service is most appropriate?
A) Cloud Spanner
B) BigQuery
C) Cloud SQL
D) Cloud Bigtable
Answer: B
Explanation:
Different workloads have different storage requirements regarding query capabilities, data volume, update frequency, and performance characteristics. Transactional databases and analytical data warehouses serve different purposes with distinct architectural optimizations. Understanding these differences helps select the appropriate storage service for specific use cases.
BigQuery is the most appropriate service for performing complex SQL queries on large datasets updated hourly because it’s specifically designed as a serverless data warehouse optimized for analytics and ad-hoc queries on massive datasets. BigQuery provides columnar storage optimized for analytical queries, can process petabytes of data efficiently, supports standard SQL with analytical functions, and automatically scales query execution resources. It excels at aggregations, joins, and complex analytical queries across billions of rows, executing queries in seconds or minutes that would take hours in traditional databases. BigQuery supports batch data loading with hourly updates through scheduled queries, data transfer service, or streaming inserts. The serverless architecture eliminates infrastructure management, and you pay only for storage and query processing. BigQuery is commonly used for business intelligence, reporting, log analysis, and data science workloads requiring complex analytical queries on large historical datasets.
A is incorrect because Cloud Spanner is optimized for transactional workloads requiring strong consistency and global distribution, not complex analytical queries on large datasets where BigQuery’s columnar storage provides better performance. C is incorrect because Cloud SQL is designed for traditional relational database workloads with transaction processing, and while it supports SQL queries, it doesn’t scale to the data volumes or provide the query performance that BigQuery delivers for analytical workloads. D is incorrect because Cloud Bigtable is a NoSQL wide-column database designed for high-throughput write and read operations on large datasets but doesn’t support SQL queries or complex analytical operations that BigQuery provides.
Question 20
You need to grant a service account access to BigQuery datasets across multiple projects. What is the most efficient approach?
A) Grant project-level IAM roles in each project
B) Create a custom IAM role with specific permissions
C) Grant dataset-level permissions in each dataset
D) Use Google Groups to manage service account permissions
Answer: C
Explanation:
IAM permissions in Google Cloud Platform can be granted at various levels including organization, folder, project, and resource levels. The appropriate level depends on the scope of access needed and the principle of least privilege. For service accounts needing access to specific resources like BigQuery datasets across multiple projects, resource-level permissions often provide the right balance of granularity and manageability.
Granting dataset-level permissions in each dataset is the most efficient approach for service account access to specific BigQuery datasets across multiple projects. BigQuery supports dataset-level access control allowing precise permission grants without providing broader project access. Dataset-level permissions enable granting only necessary access such as bigquery.dataViewer for read access or bigquery.dataEditor for write access to specific datasets while preventing access to other project resources. This follows the principle of least privilege by limiting access to only required datasets rather than entire projects. Dataset-level permissions can be managed independently in each project without coordinating project-level IAM changes that might affect other resources. This approach is particularly valuable when the service account needs access to datasets owned by different teams or organizations where granting project-level access would be inappropriate. BigQuery’s IAM integration allows direct service account grants to datasets making this straightforward to implement.
A is incorrect because granting project-level IAM roles provides excessive permissions including access to all BigQuery datasets and potentially other project resources beyond what’s required, violating the principle of least privilege. B is incorrect because while custom IAM roles allow defining specific permissions, they still need to be granted at some scope, and dataset-level grants with predefined roles are simpler and more maintainable than custom roles for this use case. D is incorrect because Google Groups are designed for organizing users, not service accounts, and adding service accounts to groups doesn’t provide a more efficient permission management approach than direct dataset-level grants for service-to-service access.
