The App Engine Flexible environment allows users to customize _______ and runtime libraries.
- Infrastructure
- Application
- Networking
- Security
Understanding the capabilities of the App Engine Flexible environment, including its support for customizing runtime environments and libraries, empowers developers to tailor their applications to their specific needs effectively.
_______ allows you to control access to resources based on conditions, such as IP address or time of day.
- Context-aware access
- Role-based access control
- Access control lists
- Identity and Access Management
Context-aware access adds an additional layer of security by considering contextual factors when granting access to resources. This enhances security posture by allowing organizations to enforce stricter access controls based on dynamic conditions.
GKE provides built-in _______ for monitoring and logging containerized applications.
- Monitoring and Logging
- CI/CD Pipelines
- Security Policies
- Load Balancing
Understanding the built-in monitoring and logging capabilities of Google Kubernetes Engine is essential for effectively monitoring and troubleshooting containerized applications deployed on GKE clusters. Leveraging these tools helps in maintaining application reliability, performance, and security in production environments.
Stackdriver Monitoring allows users to create _______ for monitoring specific conditions and triggering alerts.
- Metrics
- Dashboards
- Instances
- Reports
Metrics are fundamental to Stackdriver Monitoring, allowing users to define the parameters they want to monitor and set up alerts accordingly.
A Virtual Private Cloud (VPC) provides _______ networking functionality within the Google Cloud Platform.
- Isolated
- Shared
- Public
- Dynamic
Understanding the networking functionality of Virtual Private Clouds (VPCs) is crucial for designing and managing secure and isolated environments within the Google Cloud Platform.
What is TensorFlow in the context of Google Cloud Platform (GCP)?
- A machine learning framework
- A cloud storage service
- A database management system
- An email service provider
Understanding what TensorFlow is and its role in the Google Cloud Platform ecosystem is crucial for beginners interested in leveraging machine learning capabilities in their projects.
What is the difference between IAM policies and IAM roles?
- IAM policies define the permissions that apply to specific resources or actions, while IAM roles are collections of permissions that can be assigned to users or service accounts.
- IAM policies determine which users have access to GCP resources, while IAM roles define the permissions granted to each user.
- IAM policies and IAM roles serve distinct purposes within GCP's access control model. Policies define permissions, while roles bundle those permissions for assignment to users or service accounts.
- IAM roles determine which resources users can access, while IAM policies specify what actions they can perform.
Understanding the distinction between IAM policies and IAM roles is crucial for designing effective access control strategies in GCP. Policies specify permissions, while roles bundle those permissions for easier assignment, enabling administrators to manage access at scale.
Scenario: An organization wants to automate the deployment and management of Dataproc clusters using Infrastructure as Code principles. Which tool should they use?
- Deployment Manager
- Terraform
- CloudFormation
- Cloud Build
Infrastructure as Code (IaC) principles emphasize automating the provisioning and management of infrastructure using code-based definitions. Google Cloud Deployment Manager is a suitable tool for automating the deployment and management of Dataproc clusters in this scenario.
Google Compute Engine's autoscaler can be configured to use custom _______ for scaling decisions.
- Metrics
- Templates
- Policies
- Algorithms
Using custom metrics in autoscaling provides more precise control over scaling actions, ensuring that the infrastructure scales appropriately based on relevant performance indicators.
Scenario: A project requires complex queries and strong consistency guarantees for transactional data. Which Google Cloud database service is the most suitable choice for this scenario, considering performance and data model requirements?
- Cloud Spanner
- BigQuery
- Cloud SQL
- Firestore
Cloud Spanner's combination of horizontal scalability, strong consistency, and SQL support makes it the most suitable choice for projects requiring complex queries and strong consistency guarantees for transactional data.