Cloud DNS provides _______ for domain name resolution.
- Managed DNS Service
- Email Hosting
- Cloud Storage
- CDN (Content Delivery Network)
Understanding that Cloud DNS is a managed DNS service helps users grasp its primary function and use cases in managing domain names and DNS records effectively.
Scenario: A data science team needs to deploy a machine learning model for sentiment analysis. Which component of AI Platform should they use for this purpose?
- AI Platform Notebooks
- AI Platform Training
- AI Platform Prediction
- AI Platform Pipelines
Understanding the purpose and capabilities of different components of AI Platform is crucial for selecting the right tool for deploying machine learning models in production environments. AI Platform Prediction, in this scenario, meets the requirement of deploying a sentiment analysis model effectively.
_______ is a Google Cloud service that allows users to create custom virtual machine types.
- Google Compute Engine
- Google Kubernetes Engine
- Google Cloud Functions
- Google Cloud Run
Google Compute Engine is the correct service for creating custom virtual machine types, giving users control over VM specifications to optimize performance and cost.
Scenario: A company wants to deploy a web application that serves users across multiple continents. Which type of Cloud Load Balancing should they choose, and why?
- Global HTTP(S) Load Balancing
- Regional TCP Load Balancing
- Internal Load Balancing
- Global SSL Proxy Load Balancing
For a web application with a global user base, Global HTTP(S) Load Balancing is the most appropriate choice. It ensures efficient routing, low latency, and high availability by directing users to the nearest backend servers.
Google Compute Engine provides _______ virtual machine instances.
- preemptible
- permanent
- transient
- non-preemptible
Google Compute Engine offers preemptible virtual machine instances that provide significant cost savings for certain types of workloads, which is an important feature to understand for cost management and resource planning in cloud computing.
Which of the following is not a feature of Google Cloud Dataproc?
- Real-time Data Processing
- Auto Scaling
- Managed Service
- NoSQL Database
Identifying features that are not part of Google Cloud Dataproc helps users understand its capabilities and limitations, enabling them to choose the right tools and services for their specific use cases.
Scenario: A company wants to grant read-only access to a developer for a specific Google Cloud Storage bucket. Which IAM feature should they leverage to accomplish this?
- IAM Custom Roles
- IAM Conditions
- IAM Service Accounts
- IAM Roles
Understanding the appropriate IAM feature for granting read-only access to specific resources is crucial for maintaining security and compliance in cloud environments. By leveraging IAM roles, organizations can control access to resources based on the principle of least privilege.
In Google Compute Engine autoscaling, what are cooldown periods used for?
- Cooldown periods are used to prevent the autoscaler from making additional scaling decisions immediately after a previous scaling operation.
- Cooldown periods are used to increase the frequency of scaling decisions to rapidly adapt to changing workload conditions.
- Cooldown periods are used to shut down instances that are no longer needed after scaling down, reducing costs and optimizing resource utilization.
- Cooldown periods are used to prioritize certain instances over others during scaling events, based on predefined criteria such as instance type or location.
Understanding the purpose of cooldown periods is essential for configuring effective autoscaling policies in Google Compute Engine. By implementing appropriate cooldown periods, organizations can ensure optimal resource utilization and cost efficiency while maintaining system stability.
Scenario: A company wants to reduce latency and improve the delivery speed of its web application globally. Which Google Cloud service should they implement?
- Cloud CDN
- Cloud Storage
- Cloud SQL
- Compute Engine
Cloud CDN is specifically designed to improve the performance of web applications by caching content closer to users, thus reducing latency and enhancing delivery speeds globally. It is the ideal solution for this scenario.
Scenario: A company wants to perform complex analytics on its massive datasets stored in Google Cloud. Which service should they choose for efficient and cost-effective analysis?
- BigQuery
- Cloud Dataflow
- Dataproc
- Cloud Pub/Sub
BigQuery is the recommended choice for performing complex analytics on massive datasets stored in Google Cloud due to its scalability, performance, and cost-effectiveness. Understanding the capabilities and use cases of different Google Cloud services is crucial for making informed decisions in data analytics projects.