Cloud Identity provides centralized _______ management for users and groups.
- Identity
- Access
- Resource
- Authorization
Understanding the role of Cloud Identity in managing user identities and groups is essential for organizations to maintain security and compliance in their cloud environments.
Which of the following is not a feature of Cloud Identity?
- Data Processing
- Single Sign-On (SSO)
- Multi-Factor Authentication (MFA)
- Access Control
Recognizing the features of Cloud Identity helps users understand its capabilities and how it can be leveraged to enhance security and manage access to GCP resources effectively.
What are some of the key benefits of using Google Cloud Dataproc over managing on-premises Hadoop or Spark clusters?
- Scalability, managed infrastructure, and integration with other Google Cloud services.
- Lower total cost of ownership and higher performance due to optimized hardware configurations.
- Enhanced security features and compliance certifications for sensitive workloads.
- Advanced analytics capabilities, including machine learning and real-time streaming analytics.
Understanding the advantages of Google Cloud Dataproc over managing on-premises clusters is essential for organizations considering a move to the cloud for big data processing, enabling them to make informed decisions about infrastructure management and resource allocation.
Virtual machines offer _______ computing resources over the internet.
- scalable
- static
- limited
- redundant
Virtual machines in cloud computing are designed to provide scalable resources that can adapt to varying workload demands, offering flexibility and efficiency. Understanding this concept is crucial for effectively leveraging cloud infrastructure.
BigQuery supports _______ as a query language for data analysis.
- SQL
- Python
- NoSQL
- Java
Understanding that SQL is the primary query language for data analysis in BigQuery is crucial for intermediate users familiarizing themselves with the platform. Knowing how to write efficient SQL queries enables users to extract insights from their data effectively.
Google Kubernetes Engine automates the deployment, scaling, and _______ of containerized applications.
- Management
- Orchestration
- Security
- Networking
Understanding what Google Kubernetes Engine automates helps users grasp its capabilities and benefits in managing containerized workloads efficiently.
Cloud Bigtable uses _______ for automatic scaling based on workload demands.
- Colossus
- Google Kubernetes Engine
- Google Cloud Spanner
- Apache HBase
Understanding the underlying technologies that enable Cloud Bigtable's automatic scaling is essential for optimizing performance and cost efficiency in managing large-scale data workloads. Knowing that Cloud Bigtable is built on Apache HBase helps in understanding its scalability features.
In Cloud Datastore, what is the purpose of an ancestor query?
- Hierarchical Data Retrieval
- Full-text Search
- Real-time Data Updates
- Data Aggregation
Understanding the purpose of ancestor queries in Cloud Datastore helps developers design efficient data models and query patterns for hierarchical data structures. Leveraging ancestor queries appropriately can improve application performance and simplify data access in cloud environments.
What does VPC stand for in the context of Google Cloud?
- Virtual Private Cloud
- Virtual Public Cloud
- Virtual Provisioned Cloud
- Virtual Personal Cloud
Understanding what VPC stands for is essential for beginners to grasp the concept of virtual networking in Google Cloud and its significance in building secure and isolated environments for cloud-based applications and services.
In Google Compute Engine autoscaling, what are cooldown periods used for?
- Cooldown periods are used to prevent rapid scaling activity by imposing a waiting period between scaling events, ensuring stability and preventing unnecessary resource allocation.
- Cooldown periods determine the maximum time a virtual machine can remain idle before it is automatically shut down to conserve resources.
- Scaling decisions made by the autoscaler are influenced by historical usage patterns and predicted future demand, allowing it to proactively adjust resources in anticipation of workload changes.
- Cooldown periods are used to synchronize scaling activities across multiple virtual machines, ensuring consistent behavior and avoiding conflicts between concurrent scaling events.
Understanding the purpose and function of cooldown periods in Google Compute Engine autoscaling is crucial for optimizing resource utilization and maintaining system stability in dynamic environments with fluctuating workloads.