In Cloud CDN, what does "edge caching" refer to?
- Storing content closer to users in geographically distributed locations
- Caching content only at the origin server
- Caching user data for analytics
- Storing content temporarily during a data transfer
Edge caching is a critical feature of CDNs, where content is cached in servers located at the edge of the network, closer to users. This reduces the distance data needs to travel, leading to faster content delivery and improved user experience.
Google Cloud's Cloud DNS service is fully _______ and highly available.
- Managed
- Open Source
- On-premises
- Scalable
Understanding that Cloud DNS is fully managed by Google Cloud emphasizes the hands-off nature of the service for users and highlights its reliability and availability.
Google Cloud ensures the _______ of Cloud Billing APIs to prevent downtime.
- Availability
- Scalability
- Redundancy
- Efficiency
Availability is key to ensuring that Cloud Billing APIs are operational and accessible without interruptions, thus preventing downtime.
Which of the following is not a typical use case for Pub/Sub?
- Data Warehousing
- Event Sourcing
- Stream Processing
- Task Scheduling
Identifying typical and atypical use cases for Pub/Sub helps beginners understand its strengths and limitations, enabling them to make informed decisions when designing architectures and selecting technologies.
What is the significance of the term "cluster" in the context of Google Cloud Dataproc?
- A group of virtual machines configured to work together to process data in parallel.
- A collection of data storage nodes used for storing data persistently.
- A scheduling mechanism for orchestrating data processing workflows.
- An instance of a virtual machine running a specific data processing application.
Understanding the concept of clusters in Google Cloud Dataproc is essential for users to effectively manage and utilize the service for big data processing tasks. It involves provisioning, configuring, and scaling clusters to meet workload demands efficiently.
Which of the following is not a feature of Google Cloud Shell?
- Built-in code editor
- Multiplayer Gaming
- Pre-installed tools and libraries
- Persistent disk storage
While Google Cloud Shell offers various features for cloud development and management, multiplayer gaming is not one of them.
With Cloud DNS, users can integrate DNS _______ with other Google Cloud services.
- Management
- Resolution
- Authentication
- Billing
With Cloud DNS, users can integrate DNS management capabilities with other Google Cloud services, allowing for seamless configuration, automation, and management of DNS settings to support various applications and services hosted on Google Cloud Platform. Understanding how DNS integration works with other Google Cloud services is essential for optimizing infrastructure and workflow efficiencies in the cloud.
What is one key advantage of using AI Platform for machine learning projects?
- Managed Infrastructure
- Lower Cost
- Limitations on Model Size
- Manual Scaling
Recognizing the advantages of using AI Platform for machine learning projects helps professionals make informed decisions about tooling and infrastructure options. Managed infrastructure, scalability, and flexibility are key benefits that contribute to the efficiency and effectiveness of ML workflows on Google Cloud Platform.
In Cloud Bigtable, what is the significance of column families?
- Column Organization
- Data Encryption
- Replication
- Indexing
Understanding the role and significance of column families in Cloud Bigtable is essential for designing efficient data models and optimizing performance for analytical workloads. Column families provide a way to logically group related data together, enabling efficient storage and retrieval operations.
How does AI Platform support the integration of custom machine learning algorithms?
- Custom Container Support
- Pre-built Models
- AI Platform SDK
- AI Platform Marketplace
Integrating custom machine learning algorithms into AI Platform expands the range of problems that can be addressed using the platform and enables organizations to leverage their domain expertise to build tailored solutions. Understanding the available options for integrating custom algorithms is essential for designing and implementing machine learning workflows effectively.