41 Google Cloud Platform (GCP) Interview Questions and Answers

Google Cloud Platform is a suite of cloud computing services offered by Google. It provides infrastructure, platform, and serverless computing environments, enabling organizations to build, deploy, and scale applications efficiently. Key benefits include global infrastructure, advanced security, integrated AI/ML tools, and cost-effective pricing.

Google Cloud Storage is an object storage service that allows you to store and retrieve any amount of data at any time. Unlike traditional file storage, it is highly durable, scalable, and accessible from anywhere, making it ideal for backup, archival, and serving large amounts of unstructured data.

Google Compute Engine provides scalable and flexible virtual machines (VMs) that run on Google’s infrastructure. It is used to host applications, run batch processing, and support workloads that require custom configurations or high performance.

Regions are geographic locations where Google Cloud resources are hosted. Each region contains multiple zones, which are isolated locations within the region. This structure allows users to deploy applications closer to their users and design for high availability and fault tolerance.

Google App Engine is a fully managed platform for building and deploying applications. It automatically handles infrastructure, scaling, and patching, allowing developers to focus on writing code. It is ideal for web apps, APIs, and mobile backends that need to scale seamlessly.

Google Cloud Pub/Sub is a messaging service for exchanging messages between independent applications. It enables asynchronous communication by decoupling senders and receivers, making it suitable for event-driven architectures, data ingestion pipelines, and real-time analytics.

BigQuery is a fully managed, serverless data warehouse that enables fast SQL queries using Google’s infrastructure. Its unique features include real-time analytics, automatic scaling, and the ability to analyze petabytes of data quickly and cost-effectively.

IAM allows you to control who can access resources in your Google Cloud project. You assign roles to users, groups, or service accounts, specifying what actions they can perform. This ensures security and compliance by following the principle of least privilege.

Google Kubernetes Engine is a managed environment for deploying, managing, and scaling containerized applications using Kubernetes. It simplifies cluster management, provides automatic upgrades, and integrates with other GCP services, making it easier to run production workloads.

IaaS (Infrastructure as a Service) provides virtualized computing resources like Compute Engine. PaaS (Platform as a Service) offers managed platforms like App Engine for building applications without managing infrastructure. SaaS (Software as a Service) delivers ready-to-use applications like Google Workspace. GCP offers all three models to meet different business needs.

Google Cloud Functions is an event-driven, serverless compute service for running single-purpose functions in response to events. Cloud Run, on the other hand, allows you to run stateless containers that can be triggered via HTTP requests. Cloud Functions is ideal for lightweight, event-based workloads, while Cloud Run is better suited for deploying containerized applications with more control over the runtime environment.

Google Cloud VPC provides a private, isolated virtual network to host your resources. It allows you to define custom subnets, set up firewall rules, and configure routes for secure communication. Custom subnets let you specify IP ranges and regions, enabling fine-grained control over network segmentation and resource placement.

Service Accounts are special Google accounts used by applications or virtual machines to interact with GCP services securely. Unlike user accounts, which represent individuals, service accounts are intended for automated processes and can be assigned specific IAM roles to limit their permissions.

Google Cloud Deployment Manager is an infrastructure-as-code tool that allows you to define, deploy, and manage GCP resources using YAML or Python templates. It enables repeatable, version-controlled deployments and simplifies resource management through automation.

Cloud Spanner is a globally distributed, horizontally scalable relational database. It uses a combination of synchronous replication, TrueTime API, and multi-version concurrency control to provide strong consistency and high availability across regions.

Preemptible VMs are short-lived, cost-effective virtual machines that can be terminated by Google at any time if resources are needed elsewhere. They are ideal for fault-tolerant, batch-processing workloads, whereas regular VMs are suitable for long-running, critical applications.

To secure sensitive data in Cloud Storage, you can use features like server-side encryption, customer-managed encryption keys (CMEK), IAM policies for access control, and audit logging. Additionally, enabling Object Lifecycle Management and versioning can help protect against accidental deletions.

Google Cloud Operations Suite provides integrated monitoring, logging, and diagnostics for applications running on GCP. It collects metrics, logs, and traces, enabling you to set up alerts, visualize performance, and troubleshoot issues across your infrastructure.

Shared VPC allows multiple projects to connect to a common Virtual Private Cloud network, enabling centralized network management and resource sharing. It is useful for organizations with multiple teams or projects that require consistent security and connectivity policies.

Dataflow is a fully managed service for stream and batch data processing using Apache Beam, ideal for ETL, real-time analytics, and event processing. Dataproc is a managed Spark and Hadoop service, suitable for migrating existing big data workloads or running custom data processing pipelines.

To set up a multi-region application, you deploy resources such as Compute Engine instances, Cloud SQL databases, and load balancers across multiple regions. You use global load balancing, Cloud DNS, and replication features to ensure traffic is routed efficiently and data is available even if a region fails.

When designing IAM policies, consider the principle of least privilege, use predefined roles where possible, organize resources with folders and projects, and leverage service accounts for automation. Regularly audit permissions and use organization policies to enforce security standards.

Cloud Interconnect offers dedicated and partner connections between your on-premises network and Google’s network. It provides private, high-bandwidth, low-latency connectivity, which is more secure and reliable than standard internet connections.

GCP resources are organized in a hierarchy: Organization > Folders > Projects > Resources. Policies and IAM roles set at higher levels are inherited by lower levels, enabling centralized management and consistent enforcement of security and compliance requirements.

Implement disaster recovery by deploying resources across multiple regions, using managed backup solutions like Cloud SQL automated backups, leveraging object versioning in Cloud Storage, and setting up automated failover with global load balancing. Regularly test your recovery procedures to ensure readiness.

Custom machine types allow you to specify the exact number of vCPUs and memory for your Compute Engine instances, optimizing performance and cost for specific workloads. This flexibility helps avoid overprovisioning and reduces unnecessary expenses.

Best practices include using Virtual Private Cloud (VPC) for network segmentation, implementing firewall rules and private Google access, leveraging Shared VPC for centralized control, and using Cloud Armor for DDoS protection. Additionally, use Identity-Aware Proxy (IAP) for secure access, enable VPC Service Controls for data exfiltration prevention, and regularly audit network configurations.

You would deploy resources across multiple regions and zones, use global HTTP(S) Load Balancing, replicate data with Cloud Spanner or multi-region Cloud Storage, and leverage Cloud CDN for content delivery. Implement health checks, failover strategies, and automate deployments with Deployment Manager or Terraform for resilience.

Begin by assessing data sources, dependencies, and workloads. Use Database Migration Service or custom ETL pipelines for data transfer. Optimize schema design for BigQuery, partition and cluster tables for performance, and implement access controls. Test queries, validate data integrity, and monitor costs post-migration.

Use resource locations to control where data is stored and processed. Leverage organization policies to restrict resource creation to specific regions. Enable audit logging, use CMEK for encryption, and regularly review compliance reports. Engage with Google’s compliance offerings and certifications for industry standards.

Anthos enables consistent application deployment and management across on-premises, GCP, and other clouds. For example, a financial institution can use Anthos to modernize legacy apps, enforce security policies, and manage Kubernetes clusters centrally, ensuring portability and compliance across environments.

Use IAM roles at the dataset, table, or column level. Implement authorized views to restrict access to specific columns or rows. Leverage Data Loss Prevention (DLP) API for data classification and masking. Regularly audit access logs and use VPC Service Controls for additional data exfiltration protection.

Dataflow is a fully managed service for stream and batch data processing using Apache Beam. Its architecture decouples compute and storage, auto-scales resources, and supports windowing, triggers, and stateful processing. Use cases include real-time ETL, fraud detection, and IoT analytics pipelines.

Implement committed use discounts and sustained use discounts for Compute Engine. Use custom machine types and preemptible VMs for non-critical workloads. Enable billing alerts, use Recommender for rightsizing, and regularly review resource utilization. Automate shutdown of unused resources and leverage object lifecycle policies in Cloud Storage.

IAP provides context-aware access control by authenticating users and enforcing policies before granting access to web applications. It integrates with IAM and supports multi-factor authentication, ensuring only authorized users can access internal apps, even if they are exposed to the internet.

Source code is stored in Cloud Source Repositories or GitHub. Cloud Build triggers on code changes, builds and tests the application, and deploys artifacts to services like App Engine, Cloud Run, or GKE. Use Container Registry or Artifact Registry for image storage, and integrate with Cloud Deploy for advanced release strategies.

Use Cloud Operations Suite for monitoring metrics, logs, and traces. Analyze pod resource usage, check for node or pod autoscaling events, and review network latency. Use kubectl to inspect pod health, events, and logs. Enable Application Performance Management (APM) tools for deeper insights and set up alerts for anomalies.

VPC Service Controls create security perimeters around GCP resources, mitigating data exfiltration risks. They restrict access to APIs and services from outside defined perimeters, enforce context-aware access, and integrate with IAM and audit logging for comprehensive protection of sensitive data.

Identify RTO and RPO requirements, replicate data across regions using multi-region storage or database replication, automate failover with global load balancing, and use Infrastructure as Code for rapid redeployment. Regularly test DR procedures, document runbooks, and ensure backups are encrypted and monitored.

Confidential Computing uses secure enclaves (e.g., Confidential VMs) to encrypt data while it is being processed. This protects sensitive workloads from OS, hypervisor, and insider threats. Applications include processing regulated data, secure multi-party computation, and protecting intellectual property during analytics.

Cloud SQL is best for transactional workloads with moderate scale and relational requirements. Cloud Spanner offers global consistency and scalability for mission-critical, distributed relational workloads. Bigtable is ideal for high-throughput, low-latency NoSQL workloads such as time-series or IoT data. Choose based on consistency, scalability, and data model needs.