An organization is deploying a new public-facing REST API packaged as a stateless container image. The API experiences sudden, unpredictable bursts of traffic alongside extended periods of complete inactivity outside business hours. The engineering leadership has outlined several strict criteria: The compute environment must scale down to zero instances during idle periods to eliminate idle resource costs. Autoscaling must be rapid and fully automated without requiring teams to define or fine-tune complex scaling metrics. Operational overhead for managing underlying infrastructure, servers, and runtime updates must be eliminated. Which compute service should the organization select?

Deploy the containerized API onto Cloud Run: What Cloud Run is Cloud Run is Google Cloud's fully managed serverless compute platform that enables developers to run stateless containerized applications directly on top of Google's scalable infrastructure. It abstracts all underlying server management, automatically provisioning and de-provisioning container instances on demand. How it meets the requirements Scale down to zero: Cloud Run is designed to scale dynamically based on incoming HTTP requests. When there is no active traffic, the service automatically scales down to zero running instances, ensuring you pay nothing for idle compute. Rapid and automated scaling: Unlike virtual machine or cluster-based platforms, serverless autoscaling in Cloud Run handles sudden traffic spikes instantaneously without manual intervention or pre-warming. Zero infrastructure overhead: Operating system updates, container orchestration, host security patches, and capacity provisioning are entirely managed by Google Cloud, removing all administrative burden from the engineering team. Key advantages and capabilities Pure pay-per-use billing: Charges are calculated strictly per request and for the exact duration (down to 100 milliseconds) each request takes to process. Native concurrency: A single container instance can handle multiple concurrent requests simultaneously, optimizing performance and cost. Broad language and container support: Any stateless container that listens for HTTP requests on a designated port can be deployed seamlessly. Direct networking integration: Supports native integrations with Application Load Balancers via serverless network endpoint groups (NEGs) and private VPC access. Why this is optimal Compared to Infrastructure as a Service (IaaS) or container orchestration (GKE), Cloud Run eliminates the operational overhead of cluster and VM capacity planning while fulfilling the requirement of zero cost during idle periods.

Cloud Run Scale to Zero for Spiky APIs

IntermediateProfessional Cloud Architect

Custom Machine Types

Compute resource provisioning

IntermediateProfessional Cloud Architect

Regional MIG EVEN Distribution Autoscaling

Compute resource provisioning

IntermediateProfessional Cloud Architect

An enterprise is deploying a mission-critical web application on Google Kubernetes Engine (GKE) and Compute Engine virtual machines behind a global external Application Load Balancer. The organization requires a multi-layered network security design that satisfies the following requirements: Mitigate distributed denial-of-service (DDoS) attacks and block OWASP Top 10 web application vulnerabilities (such as SQL injection and Cross-Site Scripting) at Google Cloud's network edge before requests enter the VPC network. Perform deep packet inspection on traffic traversing the Virtual Private Cloud (VPC) network to detect and alert on malware, network intrusions, and lateral movement. Which combination of Google Cloud security services and configurations should you deploy?

Security protection (e.g. intrusion protection, access control, and firewalls)

IntermediateProfessional Cloud Architect

An enterprise organization is modernizing a multi-tier web application deployed across Google Cloud compute services. The application currently loads third-party API keys, TLS certificates, and database passwords from local plaintext configuration files bundled inside the container images and source code repository. The security architect has established the following requirements: Sensitive configuration values and credentials must be completely decoupled from application source code and deployment images. The solution must centralize credential storage, support secret versioning for rotation, and provide access auditing. Applications must retrieve the plaintext credentials securely at runtime using the principle of least privilege. Which solution should the architect implement?

Data security (key management, encryption, secret management)

IntermediateProfessional Cloud Architect

An enterprise retail organization is preparing historical transaction data to train a tabular demand forecasting model in Vertex AI to predict daily sales per product across multiple store locations. The data engineering team is using Apache Beam on Dataflow to build a scalable data preprocessing pipeline that extracts, cleans, and structures raw records before importing them into Vertex AI. Which data formatting and schema design must the Dataflow pipeline implement to produce a valid dataset for the forecasting model objective?

Preparing for Vertex AI data integration

AdvancedProfessional Cloud Data Engineer

An enterprise is designing a centralized orchestration solution for complex batch ETL/ELT pipelines spanning multiple Google Cloud services and on-premises data sources. The architecture must satisfy the following operational, security, and resilience requirements: Workflows must be authored and maintained natively in Python as Directed Acyclic Graphs (DAGs) with automated dependency resolution. Database connection strings, API keys, and sensitive service credentials must be stored securely and retrieved dynamically without hardcoding them in pipeline files. The orchestration infrastructure must be protected against single-zone failures through multi-zone scheduler redundancy and automated database failover. Service access must adhere to the principle of least privilege using a custom identity rather than overly broad default accounts. Which architecture and configuration should the organization deploy?

Job automation and orchestration (e.g., Cloud Composer and Workflows)

Professional Cloud Architect

Cloud Run Scale to Zero for Bursty APIs

Custom Machine Types

Regional MIG EVEN Distribution Autoscaling

Professional Cloud Architect

Cloud Run Scale to Zero for Bursty APIs

What Cloud Run is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Custom Machine Types

Regional MIG EVEN Distribution Autoscaling

What Cloud Run is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Professional Cloud Architect

Cloud Run Scale to Zero for Bursty APIs

Related questions

Custom Machine Types

Regional MIG EVEN Distribution Autoscaling

Professional Cloud Architect

Cloud Run Scale to Zero for Bursty APIs

What Cloud Run is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Related questions

Custom Machine Types

Regional MIG EVEN Distribution Autoscaling

What Cloud Run is

How it meets the requirements

Key advantages and capabilities

Why this is optimal