Which Google Cloud service provides high-performance block storage for virtual machine instances?
- Google Cloud Storage
- Google Cloud SQL
- Google Cloud Storage Nearline
- Google Cloud Persistent Disk
Understanding the storage options available in Google Cloud is essential for designing scalable and reliable architectures. Knowing that Persistent Disk provides high-performance block storage helps in choosing the right storage solution for virtual machine instances.
Scenario: An application deployed on Google Cloud Platform is exhibiting unexpected behavior. How can the development team utilize Stackdriver Trace to diagnose and resolve the issue quickly?
- Trace requests across services to identify failure points
- Review CPU utilization metrics
- Analyze disk I/O operations
- Check DNS resolution times
By tracing requests across services, the development team can quickly identify failure points and diagnose issues within their application, leading to faster resolution of unexpected behavior.
Which of the following is not a typical use case for Cloud DNS?
- Hosting a Website
- Internal DNS for VPC
- Load Balancing
- Email Routing
Recognizing the typical and atypical use cases for Cloud DNS helps beginners understand the specific roles and limitations of DNS within cloud infrastructure, ensuring appropriate application of this service.
What is the significance of latency distribution charts in Stackdriver Trace?
- Identifies outliers and bottlenecks
- Measures network throughput
- Tracks disk usage
- Monitors memory consumption
Latency distribution charts help developers understand the variability in request latencies, enabling them to identify and address performance issues effectively, ensuring optimal application performance.
In Cloud Datastore, what is an entity group used for?
- Transactional Consistency
- Data Encryption
- Indexing
- Data Replication
Understanding the role of entity groups in Cloud Datastore is essential for designing scalable and transactional applications that require consistency guarantees. Knowing how entity groups impact transactional behavior helps developers implement robust data management strategies.
What is Stackdriver Trace used for in Google Cloud Platform?
- Monitoring and profiling application performance
- Database Management
- Infrastructure Provisioning
- File Storage
Stackdriver Trace enables developers to understand how their applications are performing in production environments, helping them identify and resolve performance issues effectively.
Scenario: A company is experiencing fluctuating traffic on its website. Which feature of Google Cloud can they leverage to automatically adjust the number of virtual machine instances based on demand?
- Google Cloud Autoscaler
- Google Cloud Load Balancer
- Google Kubernetes Engine (GKE)
- Google Cloud CDN (Content Delivery Network)
Google Cloud Autoscaler is a key feature for optimizing resource usage and ensuring smooth performance during traffic spikes or lulls. Understanding its capabilities and how it integrates with virtual machine instances is crucial for managing dynamic workloads effectively.
_______ is a Google Cloud service that provides centralized security policy management for Virtual Private Clouds (VPCs).
- VPC Security
- VPC Firewall
- VPC Load Balancer
- VPC Router
Understanding the role and capabilities of Google Cloud services like VPC Service Controls is essential for implementing robust security measures and ensuring compliance with regulatory requirements in cloud environments.
What are Cloud Billing APIs primarily used for?
- Accessing and managing billing information
- Configuring virtual networks
- Managing cloud resources
- Monitoring application performance
Cloud Billing APIs are designed to help users programmatically access and manage billing information, providing insights into cloud expenditures and facilitating better financial management.
Dataflow allows users to create data processing _______ to transform and analyze data.
- Pipelines
- Clusters
- Containers
- Workloads
Understanding the concept of data processing pipelines is crucial for users to effectively design and implement data transformation and analysis tasks using Dataflow.