Visit here for our full Google Associate Cloud Engineer exam dumps and practice test questions.
Question 101
A company needs to deploy a web application on Google Cloud that can automatically scale based on incoming traffic. The application is containerized and requires minimal operational overhead. Which Google Cloud service should be used?
A) Cloud Run for serverless container deployment
B) Compute Engine with manual scaling
C) Cloud Storage for static hosting only
D) Local on-premises servers
Answer: A
Explanation:
Cloud Run is a fully managed serverless platform that runs containerized applications with automatic scaling based on incoming requests. The service abstracts away infrastructure management, requiring developers to only provide container images while Cloud Run handles provisioning, scaling, load balancing, and HTTPS endpoints automatically. Cloud Run scales to zero when no requests are received, eliminating costs during idle periods, and instantly scales up when traffic increases without any manual intervention. The platform supports any language, library, or binary that can run in a container, providing flexibility for diverse application stacks. Cloud Run integrates seamlessly with Cloud Build for continuous deployment, allows configuration of CPU and memory allocation per container, and supports concurrency settings controlling how many requests each container instance handles simultaneously. Pricing is based on actual resource consumption with per-request granularity, making it extremely cost-effective for variable workloads. The service provides built-in observability through Cloud Logging and Cloud Monitoring, eliminating the need for separate monitoring infrastructure. Cloud Run is ideal for web applications, REST APIs, microservices, and event-driven workloads where automatic scaling and minimal operational overhead are priorities.
B is incorrect because Compute Engine provides virtual machines requiring manual configuration of autoscaling policies, load balancers, instance templates, and health checks. While managed instance groups can autoscale, they require more operational overhead including OS patching, security updates, and infrastructure management compared to serverless options.
C is incorrect because Cloud Storage provides object storage for static files but cannot execute containerized applications requiring server-side processing. Static website hosting works only for client-side applications without backend logic or dynamic content generation.
D is incorrect because local on-premises servers require significant operational overhead including hardware procurement, data center management, capacity planning, and manual scaling. On-premises deployment contradicts cloud adoption and provides none of the automatic scaling or serverless benefits available in Google Cloud.
Question 102
An organization stores sensitive data in Cloud Storage and needs to ensure that only authorized users can access specific objects. Which access control method provides the most granular permissions?
A) IAM policies with fine-grained permissions and conditions
B) Making all buckets publicly accessible
C) Disabling all access controls completely
D) Using only bucket-level permissions without object-level control
Answer: A
Explanation:
IAM policies provide comprehensive access control for Google Cloud resources including Cloud Storage buckets and objects. IAM allows administrators to define who has what access to which resources through role-based access control with fine-grained permissions and conditional access policies. For Cloud Storage, IAM policies can be applied at multiple levels including organization, project, bucket, and individual object levels, providing flexibility in access management. IAM supports predefined roles like Storage Object Viewer for read access and Storage Object Admin for full control, as well as custom roles where specific permissions are combined to meet unique requirements. IAM conditions add another layer of control by allowing access based on attributes like resource names, date and time, or request origin. For example, administrators can grant access to specific objects only during business hours or only from corporate IP addresses. Service accounts enable applications to access storage with specific permissions without user credentials. IAM policies support groups for simplified management where permissions are assigned to groups rather than individual users. Cloud Storage also supports Access Control Lists for backward compatibility, but IAM is the recommended approach for its superior flexibility and integration with other Google Cloud services. IAM audit logs track all access attempts providing visibility into who accessed what resources and when.
B is incorrect because making all buckets publicly accessible eliminates access controls entirely, allowing anyone on the internet to read or potentially modify data. Public access is appropriate only for websites or public datasets, never for sensitive data requiring protection.
C is incorrect because disabling all access controls removes security protections, exposing data to unauthorized access. Access controls are fundamental security requirements for protecting sensitive information and ensuring compliance with data protection regulations.
D is incorrect because bucket-level permissions apply uniformly to all objects within the bucket, preventing granular control where different objects need different access levels. Object-level permissions enable specific users to access specific objects while restricting access to others.
Question 103
A development team needs to deploy applications that require consistent environments across development, testing, and production. Which Google Cloud service provides container orchestration and management?
A) Google Kubernetes Engine (GKE)
B) Cloud SQL for database hosting only
C) Cloud Functions for single functions
D) Unmanaged virtual machines without orchestration
Answer: A
Explanation:
Google Kubernetes Engine is a fully managed Kubernetes service that provides container orchestration for deploying, managing, and scaling containerized applications. GKE automates operational tasks including cluster provisioning, node pool management, automatic upgrades, and auto-repair of unhealthy nodes. Kubernetes orchestrates containers by defining desired state through declarative configuration files, then continuously working to maintain that state by creating, destroying, or restarting containers as needed. GKE supports multiple node pools allowing different workloads to run on different machine types optimized for their requirements. The service integrates with Cloud Build for CI/CD pipelines, Container Registry for storing images, and Cloud Logging and Monitoring for observability. GKE provides horizontal pod autoscaling that adjusts the number of pod replicas based on CPU utilization or custom metrics, and cluster autoscaling that adds or removes nodes based on resource demands. Networking features include native VPC integration, network policies for pod-to-pod communication control, and load balancing for external and internal services. GKE supports both regional clusters for high availability across zones and zonal clusters for simpler deployments. Security features include workload identity for secure service account management, binary authorization for deployment policy enforcement, and shielded GKE nodes for enhanced security.
B is incorrect because Cloud SQL is a managed relational database service supporting MySQL, PostgreSQL, and SQL Server, not a container orchestration platform. Cloud SQL provides database hosting but does not manage application containers or provide orchestration capabilities.
C is incorrect because Cloud Functions is a serverless compute service for deploying individual functions that run in response to events, not a comprehensive container orchestration platform. Cloud Functions is appropriate for event-driven microservices but does not provide Kubernetes orchestration or complex application deployment.
D is incorrect because unmanaged virtual machines require manual application deployment, scaling configuration, and container runtime management without automated orchestration. VMs without orchestration platforms lack the automation, self-healing, and declarative configuration that container orchestration provides.
Question 104
An organization needs to implement a relational database with automatic backups, high availability, and minimal management overhead. Which Google Cloud service should be used?
A) Cloud SQL with automated backups and failover
B) Cloud Storage for unstructured data only
C) Self-managed database on Compute Engine
D) Local on-premises database servers
Answer: A
Explanation:
Cloud SQL is a fully managed relational database service supporting MySQL, PostgreSQL, and SQL Server engines with built-in high availability, automatic backups, and minimal operational overhead. Cloud SQL automates database administration tasks including patching, updates, backups, and failover, allowing teams to focus on application development rather than database management. Automated backups occur daily with configurable backup windows and retention periods up to 365 days, with point-in-time recovery enabling restoration to any specific time within the retention period. High availability configuration uses regional instances with synchronous replication to a standby instance in a different zone within the same region, providing automatic failover if the primary instance fails. Failover typically completes within 60-120 seconds with minimal data loss. Cloud SQL supports read replicas for scaling read-heavy workloads, allowing multiple replica instances that serve read queries while the primary handles writes. Connection options include public IP with authorized networks, private IP for VPC-native connectivity, and Cloud SQL Proxy for secure connections without managing SSL certificates. Cloud SQL integrates with Cloud IAM for authentication, supports encryption at rest and in transit, and provides query insights for performance optimization. Scaling options include vertical scaling by changing machine types and storage auto-increase that automatically adds capacity when usage thresholds are reached.
B is incorrect because Cloud Storage provides object storage for unstructured data like files, images, and videos, not relational database services. Cloud Storage cannot execute SQL queries or provide ACID transactions required for relational databases.
C is incorrect because self-managed databases on Compute Engine require manual configuration of backups, high availability, failover, patching, and monitoring. Self-managed databases increase operational complexity and require database administration expertise that managed services eliminate.
D is incorrect because local on-premises database servers require significant operational overhead including hardware maintenance, capacity planning, backup management, and disaster recovery planning. On-premises deployment contradicts cloud adoption and provides none of the automatic management features available in Cloud SQL.
Question 105
A company needs to analyze large volumes of streaming data in real-time from IoT devices. Which Google Cloud service processes streaming data with low latency?
A) Cloud Pub/Sub for message ingestion and Cloud Dataflow for processing
B) Cloud Storage for batch processing only
C) Compute Engine without streaming capabilities
D) Local file storage on workstations
Answer: A
Explanation:
Real-time streaming data analysis requires services that can ingest, process, and analyze data with minimal latency as it arrives. Cloud Pub/Sub is a fully managed messaging service that ingests streaming data from multiple sources including IoT devices, applications, and services. Pub/Sub provides at-least-once message delivery with automatic scaling to handle millions of messages per second. Publishers send messages to topics, and subscribers receive messages from subscriptions, decoupling data producers from consumers. Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam that transforms and analyzes data in real-time. Dataflow automatically provisions and scales resources based on processing demands, executing data pipelines defined using the Apache Beam SDK. For IoT scenarios, devices publish telemetry data to Pub/Sub topics, Dataflow subscriptions consume messages and perform transformations like filtering, aggregation, and enrichment, then output results to BigQuery for analysis, Cloud Storage for archival, or other services for downstream processing. Dataflow supports windowing for grouping streaming data into time-based batches, stateful processing for maintaining context across messages, and exactly-once processing semantics for critical applications. This architecture handles unpredictable data volumes, provides fault tolerance through automatic retry and dead-letter topics, and enables real-time insights through continuous processing.
B is incorrect because Cloud Storage is designed for object storage and batch processing scenarios where data is collected over time then processed in large batches. Cloud Storage does not provide real-time stream processing or low-latency data analysis required for IoT telemetry.
C is incorrect because Compute Engine provides virtual machines without built-in streaming data processing capabilities. While custom streaming applications can be built on Compute Engine, this requires significant development effort and lacks the managed scaling and fault tolerance that Pub/Sub and Dataflow provide.
D is incorrect because local file storage on workstations cannot handle high-volume streaming data from distributed IoT devices. Local storage lacks the scalability, availability, and real-time processing capabilities required for enterprise IoT data analysis.
Question 106
An organization needs to implement identity and access management for Google Cloud resources. Which authentication method provides the most secure access for automated applications and services?
A) Service accounts with JSON key files or Workload Identity Federation
B) Hardcoded passwords in application code
C) Shared user credentials across multiple applications
D) No authentication for service-to-service communication
Answer: A
Explanation:
Service accounts provide identity for applications and services running on Google Cloud, enabling secure authentication without user credentials. Service accounts are special Google accounts that belong to applications rather than individual users, allowing services to authenticate to Google Cloud APIs and access authorized resources. Each service account has an email address and cryptographic keys used for authentication. Applications can authenticate using service account keys downloaded as JSON files containing private keys, though this method requires secure key management. The preferred approach is using Workload Identity Federation that allows applications running outside Google Cloud to access Google Cloud resources without service account keys. Workload Identity Federation exchanges credentials from external identity providers like AWS, Azure, or on-premises Active Directory for short-lived Google Cloud access tokens, eliminating long-lived key management. For applications running on Compute Engine, GKE, Cloud Run, or Cloud Functions, instance metadata provides automatic service account credentials without managing keys. Service accounts can be granted specific IAM roles defining which resources they can access and what actions they can perform. Service account impersonation allows one service account to act as another for delegation scenarios. Audit logs track all actions performed by service accounts providing visibility into application behavior.
B is incorrect because hardcoding passwords in application code creates severe security vulnerabilities where credentials can be exposed through source code repositories, logs, or memory dumps. Hardcoded credentials cannot be rotated easily and often remain valid indefinitely, increasing risk if compromised.
C is incorrect because sharing user credentials across multiple applications prevents accountability, makes credential rotation difficult, and violates security best practices of least privilege. Each application should have unique credentials with only the permissions it requires.
D is incorrect because eliminating authentication for service-to-service communication removes security controls, allowing any service to access any resource without authorization. Authentication is fundamental for zero-trust security architectures where all access must be verified.
Question 107
A development team uses Cloud Build for continuous integration and needs to store Docker container images. Which Google Cloud service should be used for storing and managing container images?
A) Artifact Registry for unified artifact management
B) Cloud SQL for relational database storage
C) Cloud Functions for code execution
D) Local developer workstations
Answer: A
Explanation:
Artifact Registry is Google Cloud’s recommended service for storing, managing, and securing container images and other software artifacts including language packages. Artifact Registry provides fully managed Docker container registries with support for Docker Image Manifest V2 and OCI image formats. The service integrates natively with Cloud Build, allowing build pipelines to automatically push images to Artifact Registry after successful builds. Artifact Registry offers vulnerability scanning that automatically analyzes container images for known security vulnerabilities, providing visibility into potential risks before deployment. Access control uses Cloud IAM for fine-grained permissions, allowing specific users or service accounts to push, pull, or manage images. Artifact Registry supports multi-regional repositories for high availability and reduced latency by replicating images across geographic regions. Container images are encrypted at rest and in transit, with integration for customer-managed encryption keys through Cloud KMS. Artifact Registry maintains immutable image tags preventing accidental overwrites and supports cleanup policies that automatically delete old images based on age or count. The service provides audit logging tracking all image operations for compliance and security monitoring. Artifact Registry supports not only Docker images but also Maven, npm, Python, and other artifact types in a unified repository service, simplifying artifact management for polyglot development environments.
B is incorrect because Cloud SQL is a managed relational database service for storing structured data, not container images. Container images are binary artifacts requiring specialized registries with support for Docker APIs and image layer management.
C is incorrect because Cloud Functions is a serverless compute platform for running code in response to events, not a storage service for container images. Cloud Functions can deploy code but does not store or manage container registries.
D is incorrect because storing container images only on local developer workstations prevents team collaboration, lacks centralized access control, provides no vulnerability scanning, and does not integrate with CI/CD pipelines for automated deployments.
Question 108
An organization needs to run big data analytics on petabytes of data using SQL queries. Which Google Cloud service provides serverless data warehouse capabilities?
A) BigQuery for serverless data analytics
B) Cloud Storage for object storage only
C) Compute Engine with manual database setup
D) Spreadsheets on local computers
Answer: A
Explanation:
BigQuery is Google Cloud’s fully managed, serverless data warehouse designed for analyzing massive datasets using SQL queries. BigQuery separates compute and storage, allowing independent scaling of each component and charging for storage and query processing separately. The service automatically scales compute resources based on query complexity without requiring capacity planning or cluster management. BigQuery supports standard SQL syntax with extensions for working with nested and repeated data, user-defined functions, and geospatial analytics. Data loading options include batch loading from Cloud Storage, streaming inserts for real-time data ingestion, and federated queries that analyze data in external sources without loading it into BigQuery. Partitioned tables improve query performance and reduce costs by scanning only relevant partitions based on date or integer ranges. Clustered tables organize data within partitions by specific columns, further optimizing queries that filter on those columns. BigQuery ML enables training and deploying machine learning models using SQL without moving data to separate ML environments. The service integrates with BI tools like Looker, Data Studio, and Tableau for visualization, and with Cloud Dataflow and Cloud Composer for data pipeline orchestration. BigQuery provides encryption at rest and in transit, column-level security, and audit logging. Slot reservations allow predictable pricing for consistent workloads while on-demand pricing provides flexibility for variable usage.
B is incorrect because Cloud Storage provides object storage for files but lacks the SQL query engine and data warehouse capabilities necessary for big data analytics. Cloud Storage can store raw data that BigQuery analyzes but cannot execute queries itself.
C is incorrect because manually setting up databases on Compute Engine requires significant operational overhead including capacity planning, performance tuning, backup management, and scaling configuration. Self-managed data warehouses lack BigQuery’s serverless architecture and automatic optimization.
D is incorrect because spreadsheets on local computers cannot handle petabyte-scale datasets and lack the distributed computing capabilities necessary for big data analytics. Spreadsheets are limited to millions of rows while big data scenarios involve billions or trillions of records.
Question 109
A company needs to deploy applications across multiple regions for high availability and disaster recovery. Which strategy ensures application availability if one region fails?
A) Multi-region deployment with load balancing and data replication
B) Single region deployment without redundancy
C) Single availability zone with no backup
D) Local on-premises servers only
Answer: A
Explanation:
Multi-region deployment distributes application instances across geographically separated regions, ensuring continued operation if an entire region becomes unavailable due to natural disasters, network outages, or infrastructure failures. Google Cloud global load balancing automatically distributes traffic across regions based on proximity, capacity, and health checks. HTTP(S) load balancing uses Cloud CDN for caching static content close to users and anycast IP addresses that route users to the nearest healthy backend. Regional load balancers can be combined with global load balancers for comprehensive traffic management. Data replication strategies depend on application requirements and include synchronous replication for strong consistency requiring all replicas to acknowledge writes before completion, and asynchronous replication for eventual consistency with lower latency but potential for data lag. Cloud SQL supports cross-region read replicas for read scaling and disaster recovery, while Cloud Spanner provides multi-region configurations with synchronous replication for both reads and writes. Cloud Storage offers multi-region buckets that automatically replicate objects across multiple regions within geographic areas like US, EU, or ASIA. Application state should be stored in managed services rather than instance memory for seamless failover. Health checks continuously monitor application health, automatically removing failed instances from load balancer rotation. Multi-region deployments increase costs due to data replication and cross-region networking but provide critical availability guarantees for business-critical applications.
B is incorrect because single region deployment creates a single point of failure where regional outages cause complete application unavailability. Single region deployment is appropriate only for applications with relaxed availability requirements or regional compliance constraints.
C is incorrect because deploying in a single availability zone provides even less redundancy than single region deployment. Zone failures are more common than regional failures, making single zone deployment highly vulnerable to infrastructure issues.
D is incorrect because relying solely on local on-premises servers eliminates cloud benefits including global infrastructure, managed services, and geographic distribution. On-premises deployment is limited to physical locations and requires significant infrastructure investment for multi-region availability.
Question 110
An organization needs to control costs for Google Cloud resources by setting spending limits and receiving alerts. Which service helps manage and monitor cloud costs?
A) Cloud Billing with budgets and alerts
B) Cloud Storage for file management only
C) Compute Engine for compute resources
D) No cost management tools needed
Answer: A
Explanation:
Cloud Billing provides comprehensive cost management capabilities including billing accounts that consolidate charges for multiple projects, detailed cost breakdowns by project, service, and resource, and exportable billing data for analysis. Budgets allow setting spending thresholds at project, billing account, or service levels, with customizable alert rules that notify administrators via email or Pub/Sub messages when spending reaches configurable percentages of the budget. Budget alerts can trigger automated responses through Cloud Functions, like shutting down non-production resources when budgets are exceeded. Cost forecasting predicts future spending based on historical usage patterns helping with capacity planning. Committed use discounts provide reduced pricing for resources committed to run for one or three years, significantly lowering costs for steady-state workloads. Sustained use discounts automatically apply when resources run for significant portions of the month. Billing reports provide visualization of spending trends across time periods, services, and projects. Billing export to BigQuery enables advanced cost analysis using SQL queries, identifying optimization opportunities like underutilized resources or oversized instances. Labels organize resources for cost allocation to departments, applications, or environments, enabling chargeback models where costs are attributed to specific teams. Billing access control grants different users appropriate visibility and management permissions without providing broader project access. Recommendations engine suggests cost optimization opportunities based on usage patterns.
B is incorrect because Cloud Storage is a storage service for objects and files, not a cost management tool. While Cloud Storage has its own pricing and cost optimization features like lifecycle management, it does not provide billing management for overall cloud spending.
C is incorrect because Compute Engine is a compute service providing virtual machines, not a billing management service. Compute Engine generates costs but does not provide tools for monitoring and controlling spending across all Google Cloud services.
D is incorrect because cloud cost management tools are essential for preventing budget overruns, optimizing spending, and ensuring financial accountability. Without cost management, organizations risk unexpected expenses and lack visibility into resource utilization.
Question 111
A development team needs to debug production issues by examining application logs from multiple services. Which Google Cloud service aggregates and analyzes logs?
A) Cloud Logging for centralized log management
B) Cloud Storage for object storage only
C) Compute Engine without logging features
D) Local log files on individual servers
Answer: A
Explanation:
Cloud Logging is a fully managed service that ingests, stores, searches, analyzes, and monitors log data from Google Cloud services, applications, and on-premises systems. Cloud Logging automatically collects logs from Google Cloud services including Compute Engine, GKE, Cloud Run, and App Engine without configuration. Application logs can be written using logging libraries available for multiple programming languages or the Cloud Logging API. The service provides a unified interface for searching logs using filters based on time ranges, severity levels, resource types, and custom fields. Advanced filters support regular expressions and boolean logic for complex queries. Logs Explorer provides an interactive interface for real-time log viewing with histogram visualizations showing log volume over time. Log-based metrics extract numerical data from logs for monitoring and alerting, enabling custom metrics derived from application behavior. Logs Router directs log entries to destinations including Cloud Logging itself for storage and analysis, Cloud Storage for long-term archival, BigQuery for advanced analytics, or Pub/Sub for streaming to external systems. Exclusion filters prevent specific logs from being stored, reducing costs for high-volume low-value logs. Log retention is configurable per log bucket with default retention of 30 days and options up to 3650 days. Audit logs track administrative activities and data access for compliance and security monitoring.
B is incorrect because Cloud Storage provides object storage for files but lacks the search, filtering, and analysis capabilities necessary for effective log management. Logs stored in Cloud Storage require custom tools to parse and analyze.
C is incorrect because while Compute Engine virtual machines generate logs, the instances themselves do not provide centralized log aggregation or analysis. Without Cloud Logging integration, administrators must manually access individual instances to examine logs.
D is incorrect because storing logs only on individual servers prevents centralized analysis, makes correlation across services difficult, risks log loss if servers fail, and does not scale for modern distributed applications generating logs from hundreds of instances.
Question 112
An organization needs to implement network segmentation and control traffic flow between different tiers of their application. Which Google Cloud feature provides network isolation?
A) VPC (Virtual Private Cloud) with subnets and firewall rules
B) Public internet without security
C) Single flat network for all resources
D) No network configuration needed
Answer: A
Explanation:
VPC provides isolated virtual networks within Google Cloud with complete control over IP address ranges, subnets, routes, and firewall rules. Each VPC is a global resource spanning all regions, with subnets created in specific regions for deploying resources. Subnet IP ranges can be expanded without downtime to accommodate growth. VPC firewall rules control traffic flow between resources using allow and deny rules based on source and destination IP addresses, protocols, and ports. Firewall rules can be applied to specific instances using network tags or service accounts, enabling fine-grained security. Default deny ingress and allow egress policies provide security by default, requiring explicit rules for inbound traffic. Network segmentation is achieved by creating separate subnets for different application tiers like web, application, and database layers, then controlling traffic between tiers through firewall rules. VPC peering connects multiple VPCs enabling communication between projects or organizations while maintaining network isolation. Shared VPC allows multiple projects to use a common VPC, centralizing network administration while maintaining project-level IAM boundaries. Private Google Access enables instances without external IP addresses to access Google services. Cloud NAT provides outbound internet connectivity for private instances without exposing them to inbound connections. VPC Flow Logs capture network traffic for troubleshooting and security analysis. Hybrid connectivity options include Cloud VPN for encrypted IPsec tunnels and Cloud Interconnect for dedicated physical connections.
B is incorrect because using the public internet without security exposes resources to threats and provides no isolation or access control. All resources would be directly accessible from the internet without protection.
C is incorrect because a single flat network without segmentation prevents implementing security zones and least privilege access controls. All resources could communicate freely, violating security best practices and enabling lateral movement if any resource is compromised.
D is incorrect because network configuration is essential for security, isolation, and controlled communication between resources. Without proper network architecture, applications cannot implement security zones or defense in depth strategies.
Question 113
A company needs to transfer large volumes of data from on-premises storage to Cloud Storage. Which service provides offline data transfer using physical devices?
A) Transfer Appliance for offline data migration
B) Streaming small files over slow connections
C) Manual copying to individual USB drives
D) Emailing data in small batches
Answer: A
Explanation:
Transfer Appliance is a high-capacity storage device that Google ships to customer locations for offline data transfer when internet bandwidth is insufficient or cost-prohibitive for large data migrations. The appliance is a secure, tamper-evident device available in 100TB and 480TB capacities that customers can order through the Google Cloud Console. Once received, customers connect the appliance to their network and use the included software to copy data at local network speeds. The appliance encrypts data during transfer and storage using customer-managed keys, ensuring security throughout the process. After data copying completes, customers ship the appliance back to Google using provided shipping materials and tracking. Google receives the appliance at a secure facility, uploads data to specified Cloud Storage buckets, then securely wipes the appliance for reuse. Transfer Appliance is most cost-effective for datasets larger than 20TB where network transfer would take weeks or exceed bandwidth costs. The service eliminates network congestion from large transfers and provides predictable timelines based on shipping rather than network performance. Multiple appliances can be used simultaneously for datasets exceeding a single appliance’s capacity. Transfer Appliance complements Storage Transfer Service which transfers data over networks from AWS S3, Azure Blob Storage, or HTTP/HTTPS sources.
B is incorrect because streaming large datasets over slow connections results in extended transfer times potentially lasting weeks or months. Slow transfers risk interruption, consume bandwidth affecting other operations, and may incur substantial network costs.
C is incorrect because manually copying to individual USB drives is impractical for large datasets requiring hundreds or thousands of drives. Manual processes lack encryption, tracking, and security features necessary for enterprise data migration.
D is incorrect because email systems have attachment size limits typically under 25MB, making it impossible to transfer large datasets. Email is designed for communication, not bulk data transfer, and lacks security and integrity verification for data migration.
Question 114
An organization wants to implement machine learning models without extensive ML expertise. Which Google Cloud service provides pre-trained models and AutoML capabilities?
A) Vertex AI for unified ML platform
B) Cloud Storage for data storage only
C) Compute Engine without ML features
D) Manual coding without frameworks
Answer: A
Explanation:
Vertex AI is Google Cloud’s unified machine learning platform that brings together services for building, deploying, and managing ML models with tools for both expert data scientists and developers with limited ML experience. AutoML enables users to train custom models with minimal ML expertise by automating feature engineering, model selection, and hyperparameter tuning. Users provide labeled training data, select the model type like image classification, text classification, or tabular data, and AutoML trains high-quality models. Pre-trained APIs provide ready-to-use models for common tasks including Vision API for image analysis, Natural Language API for text analysis, Translation API for language translation, and Speech-to-Text and Text-to-Speech APIs. These pre-trained models work immediately without training data and are continuously improved by Google. Custom training allows experienced data scientists to train models using popular frameworks like TensorFlow, PyTorch, and scikit-learn with full control over model architecture and training process. Vertex AI provides managed notebooks based on JupyterLab for interactive development, feature store for managing and serving ML features, and model monitoring for detecting training-serving skew and data drift. Vertex AI Pipelines orchestrates ML workflows including data preprocessing, training, evaluation, and deployment. Vertex AI Prediction serves models for online and batch prediction with automatic scaling. MLOps capabilities include experiment tracking, model versioning, and A/B testing for production deployments.
B is incorrect because Cloud Storage provides object storage for data and model artifacts but does not provide ML training, inference, or AutoML capabilities. Cloud Storage is used alongside ML services for storing datasets and models.
C is incorrect because Compute Engine provides virtual machines without built-in ML frameworks, training pipelines, or pre-trained models. While custom ML infrastructure can be built on Compute Engine, this requires significant expertise and operational overhead.
D is incorrect because manual coding without frameworks requires deep ML expertise including knowledge of algorithms, model architectures, training techniques, and deployment strategies. Manual implementation is time-consuming and difficult for organizations without specialized ML talent.
Question 115
A development team needs to implement continuous deployment that automatically releases code changes to production after passing automated tests. Which Google Cloud service orchestrates CI/CD workflows?
A) Cloud Build with triggers and deployment pipelines
B) Cloud Storage for static files only
C) Manual deployment by operations team
D) No automation for deployments
Answer: A
Explanation:
Cloud Build is a fully managed continuous integration and continuous delivery platform that executes builds in Google Cloud infrastructure. Cloud Build imports source code from repositories including Cloud Source Repositories, GitHub, and Bitbucket, executes build steps defined in configuration files, and produces artifacts like container images or deployment packages. Build triggers automatically start builds in response to code commits, pull requests, or tag creation, enabling fully automated CI/CD pipelines. Build configuration uses YAML files defining sequential steps that execute in containers, allowing unlimited flexibility for build processes. Each step can use pre-built builder images or custom Docker images for specialized tools. Cloud Build integrates with Artifact Registry for storing build outputs and vulnerability scanning. Deployment steps in build configurations can deploy applications to Cloud Run, GKE, App Engine, Cloud Functions, or Compute Engine. Cloud Build supports parallel execution of steps for faster builds and caching of dependencies to reduce build times. Approval gates enable manual approval before production deployments for compliance requirements. Cloud Build logs provide detailed output for troubleshooting failed builds. The service integrates with Cloud Monitoring for build metrics and alerting. Build notifications send status updates through Pub/Sub, email, or Slack. Cloud Build pricing is based on build minutes with free tier available. For complex workflows, Cloud Deploy provides progressive delivery with deployment strategies like canary and blue-green.
B is incorrect because Cloud Storage stores objects and files but does not execute builds, run tests, or deploy applications. Cloud Storage can store build artifacts but is not a CI/CD orchestration tool.
C is incorrect because manual deployment by operations teams introduces delays, increases risk of human error, and does not scale for modern development practices requiring frequent releases. Manual processes cannot match the speed and reliability of automated CI/CD.
D is incorrect because continuous deployment automation is essential for modern software delivery, enabling rapid iteration, reducing deployment risk through consistency, and allowing teams to release features quickly in response to business needs.
Question 116
An organization needs to implement encryption for sensitive data stored in Cloud Storage. Which encryption option provides customer control over encryption keys?
A) Customer-managed encryption keys (CMEK) with Cloud KMS
B) No encryption at all
C) Plain text storage without protection
D) Encryption disabled completely
Answer: A
Explanation:
Cloud Storage encrypts all data at rest by default using Google-managed encryption keys, but customer-managed encryption keys provide additional control for organizations with specific compliance requirements or security policies. CMEK uses Cloud Key Management Service where customers create and manage encryption keys while Google handles encryption operations using those keys. Cloud KMS stores keys in hardware security modules meeting FIPS 140-2 Level 3 security standards. Customers can create key rings organizing keys by region and purpose, and key versions enabling key rotation. When configuring CMEK for Cloud Storage buckets, customers specify which Cloud KMS key to use for encrypting new objects. Existing objects can be rewritten with CMEK encryption. CMEK provides audit logging showing when keys are used and by which service accounts, enabling compliance with regulations requiring key usage tracking. Customers can disable or destroy keys immediately preventing decryption of data encrypted with those keys, providing crypto-shredding capability for secure data deletion. Cloud KMS supports automatic key rotation creating new key versions while maintaining old versions for decrypting existing data. CMEK does not impact application code because encryption is transparent to applications accessing Cloud Storage. Besides Cloud Storage, CMEK works with Compute Engine persistent disks, BigQuery, Cloud SQL, and other Google Cloud services requiring consistent encryption key management.
B is incorrect because while Google encrypts all Cloud Storage data at rest automatically, the question specifically asks about customer control over encryption keys. Default encryption uses Google-managed keys without customer key management capabilities.
C is incorrect because plain text storage without encryption exposes sensitive data to unauthorized access if storage is compromised.
D is incorrect because encryption cannot be disabled in Cloud Storage. Google automatically encrypts all data at rest, and the only choice is whether to use Google-managed keys or customer-managed keys. Disabling encryption is not an option and would violate security best practices.
Question 117
A company needs to provide temporary access to external contractors to specific Google Cloud resources without creating full user accounts. Which authentication method should be used?
A) Workload Identity Federation or temporary IAM grants with conditions
B) Permanent administrator accounts for all contractors
C) Shared passwords across all external users
D) No access controls for contractors
Answer: A
Explanation:
Workload Identity Federation allows external identities from AWS, Azure, or any identity provider supporting OIDC or SAML to access Google Cloud resources without creating Google Cloud user accounts or service account keys. Federation establishes trust relationships where external credentials are exchanged for short-lived Google Cloud access tokens with specific permissions. Contractors can authenticate using their existing corporate credentials while accessing only authorized Google Cloud resources. Federation configuration includes creating workload identity pools, configuring providers with external identity details, and mapping external attributes to Google Cloud IAM policies. Attribute-based access control uses claims from external tokens to dynamically grant permissions, enabling just-in-time access without pre-provisioning accounts. Temporary IAM grants use IAM conditions that expire after specified time periods or dates, automatically revoking access without manual intervention. Conditions can also restrict access based on IP addresses, device trust levels, or request attributes. For short-term contractor access, administrators grant IAM roles with expiration conditions ensuring access automatically terminates when the contract ends. Service accounts with limited permissions can be created specifically for contractor access, with account lifecycle tied to contract duration. All contractor activity is logged in Cloud Audit Logs providing visibility into resource access and changes made during the engagement period.
B is incorrect because granting permanent administrator accounts to contractors violates least privilege principles and creates security risks when contracts end. Administrator permissions provide excessive access beyond what contractors typically require, and permanent accounts require manual cleanup preventing automatic access termination.
C is incorrect because shared passwords eliminate accountability by preventing identification of which contractor performed specific actions. Shared credentials violate security best practices and compliance requirements for individual user authentication and audit trails.
D is incorrect because contractors accessing resources without access controls creates severe security vulnerabilities where unauthorized actions cannot be prevented or attributed to specific individuals. All access must be authenticated, authorized, and logged for security and compliance.
Question 118
An organization needs to migrate existing virtual machines from on-premises VMware environments to Google Cloud. Which service facilitates VM migration with minimal downtime?
A) Migrate for Compute Engine (formerly Velostrata)
B) Manual VM rebuilding from scratch
C) Cloud Storage for object storage only
D) Cloud Functions for serverless code
Answer: A
Explanation:
Migrate for Compute Engine provides large-scale migration of virtual machines from on-premises, AWS, Azure, or other cloud environments to Google Cloud with minimal downtime and disruption. The service uses intelligent streaming technology that rapidly migrates VMs by streaming workload data from source environments while VMs continue running, then synchronizes changes before final cutover. Migration process begins with installing Migrate for Compute Engine manager in the source environment, discovering existing VMs, selecting VMs for migration, and configuring target Google Cloud settings including machine types, regions, and networks. During migration, VMs run in their source environment while data streams to Google Cloud in the background. Test clones allow validating migrated VMs in Google Cloud without affecting source VMs. When ready for cutover, final synchronization occurs and VMs start running in Google Cloud, typically requiring only minutes of downtime. Wave migrations enable migrating hundreds or thousands of VMs in coordinated groups based on application dependencies. Migrate for Compute Engine automatically adapts VMs for Google Cloud including installing necessary drivers and agents. The service supports various source platforms including VMware vSphere, AWS EC2, Azure VMs, and physical servers. Post-migration optimization recommendations help right-size instances based on actual utilization. Migrate for Compute Engine integrates with migration planning tools for dependency mapping and runbook generation.
B is incorrect because manually rebuilding VMs from scratch requires extensive time and effort for recreating configurations, installing applications, and migrating data. Manual rebuilding causes extended downtime and risks configuration errors or missing dependencies.
C is incorrect because Cloud Storage stores objects and files but does not provide VM migration capabilities. Cloud Storage cannot run operating systems or execute migration orchestration for live virtual machines.
D is incorrect because Cloud Functions executes individual functions in response to events, not complete virtual machine environments. Cloud Functions is not designed for migrating or running existing VM-based applications that require persistent state and traditional OS environments.
Question 119
A development team needs to implement load balancing for a global web application with users worldwide. Which Google Cloud load balancer provides global distribution and automatic failover?
A) Global HTTP(S) Load Balancing with Cloud CDN
B) Single region load balancer without global reach
C) No load balancing for single instance
D) Manual traffic distribution
Answer: A
Explanation:
Global HTTP(S) Load Balancing is a fully distributed, software-defined managed service operating at Layer 7 providing automatic intelligent routing of user requests to the nearest healthy backend. The load balancer uses a single anycast IP address that routes users to the closest Google Cloud point of presence, then selects optimal backends based on proximity, capacity, and health. Global load balancing distributes traffic across backends in multiple regions, automatically routing traffic away from unhealthy or overloaded backends to healthy instances in other regions. Cloud CDN integration caches static and dynamic content at edge locations worldwide, reducing latency and origin server load. HTTP/2 and QUIC protocol support improves performance for modern applications. SSL/TLS offloading terminates encrypted connections at the load balancer, reducing backend compute requirements. URL mapping routes requests to different backend services based on URL paths, enabling microservices architectures where different services handle different API endpoints. Session affinity directs requests from the same client to the same backend using cookie-based or IP-based affinity. Advanced traffic management includes weighted load balancing for gradual traffic shifting during deployments, and traffic splitting for A/B testing. Backend health checks continuously monitor instance health, automatically removing unhealthy instances from rotation. Capacity-based balancing prevents overload by considering current connections and utilization when selecting backends.
B is incorrect because single region load balancers serve users only from one geographic region, increasing latency for distant users and providing no automatic failover if the region fails. Single region load balancing is appropriate only for applications serving localized user bases.
C is incorrect because running applications on single instances without load balancing creates single points of failure where instance failures cause complete application unavailability. Single instances cannot scale to handle traffic spikes or distribute load across multiple servers.
D is incorrect because manual traffic distribution requires human intervention for routing changes, cannot respond dynamically to failures or capacity changes, and does not scale for global applications serving millions of requests. Automated load balancing is essential for reliable highly available applications.
Question 120
An organization needs to implement disaster recovery with automated backups and point-in-time recovery for their database. Which Cloud SQL feature provides this capability?
A) Automated backups with point-in-time recovery (PITR)
B) No backups or recovery planning
C) Manual backups to local storage only
D) Relying on single database instance without redundancy
Answer: A
Explanation:
Cloud SQL automated backups create daily backups of database instances with configurable backup windows to minimize impact during low-traffic periods. Backups are stored separately from the database instance in durable storage spanning multiple locations within the region. Backup retention can be configured from 1 to 365 days allowing compliance with various regulatory requirements. Point-in-time recovery enables restoring databases to any specific moment between the oldest retained backup and the most recent transaction log, providing granular recovery options when data corruption or accidental deletion occurs. PITR works by applying transaction logs to the most recent automated backup before the desired recovery point. Transaction logs are automatically maintained by Cloud SQL without additional configuration. Recovery process creates a new Cloud SQL instance from the backup and transaction logs, leaving the original instance unchanged until recovery is verified. Backup and PITR configuration is critical for disaster recovery planning because it enables meeting Recovery Time Objectives and Recovery Point Objectives. On-demand backups can be created manually before major changes like schema migrations or application updates. Backup operations are transparent to applications with minimal performance impact. Backups can be exported to Cloud Storage for long-term retention beyond Cloud SQL’s retention limits. High availability configuration with synchronous replication provides even faster recovery by maintaining a standby replica that can be promoted immediately if the primary instance fails.
B is incorrect because operating databases without backups creates catastrophic risk where hardware failures, data corruption, or human errors result in permanent data loss. Backups are fundamental requirements for any production database system.
C is incorrect because manual backups require remembering to perform backups regularly, risk being forgotten during busy periods, and local storage lacks the durability and geographic redundancy that cloud-based backups provide. Manual backups also cannot provide point-in-time recovery granularity.
D is incorrect because single database instances without backups or redundancy create severe availability and durability risks. Database failures or data corruption would result in complete data loss and extended downtime while attempting recovery. All production databases require backup and recovery capabilities.
