What I learn from Google Cloud Study Jam: GenAI with Cloud Core
Tags: googlecloud, google
Published: 2024-01-13 11:50:44

<u>Google Cloud Solution Architect:</u>

- A professional responsible for designing and implementing solutions on the Google Cloud platform.
- Works closely with customers to understand their business requirements and technical needs.
- Designs architectures and recommends Google Cloud products and services to meet customer goals.
- Develops proof of concepts, prototypes, and technical proposals.
- Collaborates with cross-functional teams to ensure successful implementation and deployment of solutions.
- Provides technical guidance, support, and troubleshooting throughout the project lifecycle.
- Stays updated on Google Cloud technologies and industry trends to offer the best solutions to customers.

<br/>
**Computing and storing**

<br/>
<u>What is the Google computer engine?</u>

- Google Compute Engine is an Infrastructure as a Service (IaaS) offering by Google Cloud.
- It provides virtual machine instances in the cloud for running workloads.
- Users can choose from a variety of machine types and customize the virtual machines based on their needs.
- Compute Engine offers scalability, flexibility, and high-performance computing capabilities.
- It integrates with other Google Cloud services, allowing seamless data and resource management.
- Compute Engine supports both Linux and Windows operating systems.
- Users have control over networking, storage, and security configurations.
- Pricing is based on a pay-as-you-go model, with options for sustained use discounts and custom machine types.

<br/>
<u>What is Google Kubernetes engine?</u>

- Google Kubernetes Engine is a managed container orchestration platform provided by Google Cloud.
- It enables the deployment, scaling, and management of containerized applications using Kubernetes.
- GKE automates much of the infrastructure management, allowing developers to focus on application development.
- It offers features like automatic scaling, load balancing, and self-healing capabilities for containers.
- GKE integrates with other Google Cloud services, providing seamless access to storage, networking, and monitoring tools.
- It supports both stateless and stateful applications, providing persistent storage options.
- GKE provides a robust security framework, including identity and access management, encryption, and network policies.
- Pricing is based on a pay-as-you-go model, with options for node pools and cluster autoscaling.

<br/>
<u>What is the Google app engine?</u>

- Google App Engine is a fully managed Platform as a Service (PaaS) offering by Google Cloud.
- It allows developers to build and deploy web applications and APIs without managing the underlying infrastructure.
- App Engine provides a NoSQL Datastore for storing and retrieving data.
- It offers a Memcache service for caching frequently accessed data, improving application performance.
- App Engine includes built-in load balancing to distribute traffic across multiple instances.
- It supports health checks to monitor the status of instances and automatically replace unhealthy ones.
- App Engine provides logging capabilities for collecting and analyzing application logs.
- It offers a User Authentication API for managing user authentication and authorization.
- App Engine supports preconfigured containers for running custom runtime environments.
- It integrates with a Content Delivery Network (CDN) for efficient content delivery worldwide.
- App Engine includes security scanning to detect and mitigate vulnerabilities in applications.
- It supports versioning, allowing developers to manage different versions of their applications.

<br/>
<u>What is Google Cloud's function?</u>

- Google Cloud Functions is a serverless compute service provided by Google Cloud.
- It allows developers to write and deploy small, event-driven functions in various languages.
- Functions are triggered by events from various sources like Cloud Storage, Pub/Sub, HTTP requests, and more.
- Cloud Functions automatically scales based on the incoming workload and charges only for the execution time.
- It supports multiple programming languages, including JavaScript, Python, Go, and more.
- Cloud Functions integrates seamlessly with other Google Cloud services and APIs.
- It provides built-in logging and monitoring capabilities for observability.
- Cloud Functions simplifies the deployment and management of individual functions, abstracting away infrastructure concerns.
- It enables rapid development and deployment of microservices and event-driven applications.
- Cloud Functions is suitable for use cases such as data processing, real-time analytics, and application logic execution.

<br/>
<u>What is Google Cloud run?</u>

- Google Cloud Run is a fully managed serverless container platform provided by Google Cloud.
- It allows developers to run stateless HTTP containers in a serverless environment.
- Cloud Run abstracts away infrastructure management, auto-scales based on incoming requests, and charges only for actual usage.
- It supports containerized applications built with Docker and can be written in any programming language.
- Cloud Run provides a seamless deployment experience with easy integration into existing CI/CD workflows.
- It offers rapid scaling capabilities to handle high traffic and spikes in demand.
- Cloud Run supports both HTTP/1.1 and HTTP/2 protocols for efficient communication.
- It integrates with other Google Cloud services for seamless data storage, logging, and monitoring.
- Cloud Run is suitable for microservices, APIs, web applications, and event-driven workloads.
- It provides a balance between the scalability and flexibility of containers and the simplicity of serverless computing.

<br/>
<u>What is Google Cloud DataFlow?</u>

- Google Cloud Dataflow is a fully managed service for processing and analyzing large-scale data sets.
- It is based on Apache Beam, an open-source unified programming model for batch and streaming data processing.
- Dataflow allows you to build data pipelines for ETL (Extract, Transform, Load), batch processing, and stream processing.
- It offers automatic scaling, handling the complexity of resource management and optimization.
- Dataflow supports both batch and streaming data processing, offering near-real-time data analysis.
- It integrates with other Google Cloud services, such as BigQuery, Pub/Sub, and Cloud Storage.
- Dataflow provides a visual monitoring interface and detailed logs for pipeline monitoring and troubleshooting.
- It offers flexibility in programming languages, supporting Java, Python, and other Beam SDK-supported languages.
- Dataflow enables data parallelism, distributing processing across multiple workers for faster execution.
- It provides a unified model for both batch and streaming processing, simplifying development and maintenance of data pipelines.

<br/>
<u>What is Google Dataproc?</u>

- Google Dataproc is a fully managed cloud service for running Apache Hadoop, Spark, and other big data frameworks.
- It provides a scalable and cost-effective solution for processing and analyzing large datasets.
- Dataproc automates the provisioning, management, and scaling of clusters, allowing users to focus on data analysis.
- It integrates with other Google Cloud services, such as BigQuery, Cloud Storage, and Pub/Sub.
- Dataproc clusters can be easily customized with specific software versions, libraries, and configurations.
- It supports both batch and streaming data processing, enabling real-time data analysis.
- Dataproc provides flexibility in choosing the cluster size to meet performance and cost requirements.
- It offers monitoring, logging, and debugging capabilities for cluster management and troubleshooting.
- Dataproc supports popular big data tools and frameworks, including Hadoop, Spark, Hive, and Pig.
- It allows easy migration of on-premises big data workloads to the cloud, reducing operational overhead.

<br/>
<u>What is Google Firestore?</u>

- Google Firestore is a fully managed NoSQL document database provided by Google Cloud.
- It offers flexible and scalable data storage for web, mobile, and server applications.
- Firestore organizes data into collections and documents, allowing hierarchical data modeling.
- It provides real-time data synchronization and automatic scaling to handle high traffic and concurrent access.
- Firestore offers strong consistency, ensuring data integrity across distributed environments.
- It supports querying and indexing for efficient data retrieval and searching.
- Firestore integrates with other Google Cloud services, such as Cloud Functions and Cloud Storage.
- It provides client libraries for various programming languages and mobile platforms.
- Firestore offers granular access control and security features to protect data.
- It allows seamless integration with Firebase, enabling powerful backend services for mobile and web applications.

<br/>
<u>What is Google Looker?</u>

- Google Looker is a business intelligence and data analytics platform.
- It enables organizations to explore, analyze, and visualize data from various sources.
- Looker provides a unified view of data through a web-based interface.
- It offers a wide range of data modeling and querying capabilities.
- Looker supports collaborative data exploration and sharing of insights.
- It integrates with popular databases, data warehouses, and cloud platforms.
- Looker provides advanced analytics features, including predictive modeling and machine learning.
- It offers customizable dashboards and reports for data visualization.
- Looker allows users to create and share data-driven reports and presentations.
- It empowers organizations with data-driven decision-making and insights.

<br/>
<u>What are Automatic scaling and upgrades?</u>

- Automatic scaling refers to the ability of a system to dynamically adjust its resources based on demand.
- It allows the system to automatically allocate or deallocate resources to meet changing workload requirements.
- Automatic scaling helps optimize resource utilization, ensuring efficient and cost-effective operations.
- Scaling can be based on various metrics such as CPU usage, memory utilization, or incoming request rate.
- Automatic upgrades refer to the process of automatically applying software updates or patches to a system.
- It eliminates the need for manual intervention and ensures that the system remains up-to-date with the latest features and security fixes.
- Automatic upgrades can be scheduled during maintenance windows or performed seamlessly with zero downtime.
- They help improve system reliability, performance, and security by keeping the software stack current.
- Automatic scaling and upgrades are common features in cloud computing platforms and services to provide scalability, flexibility, and optimal performance.

<br/>
**Data**

<br/>
<u>What is the difference between a stream and batch process?</u>

Stream Processing:

- Stream processing deals with continuous and real-time data.
- Data is processed as it arrives, typically in small, incremental units.
- It enables immediate response and analysis of data as it flows.
- Stream processing is suitable for scenarios that require low latency and real-time insights.
- Examples include real-time analytics, fraud detection, and IoT data processing.

Batch Processing:

- Batch processing deals with large volumes of data collected over a period of time.
- Data is processed in batches or chunks, typically in scheduled intervals.
- It involves processing a significant amount of data in a single operation.
- Batch processing is suitable for scenarios where results can be obtained retrospectively.
- Examples include data warehousing, generating reports, and data analysis.

Key Differences:

- Stream processing is real-time and continuous, while batch processing is retrospective and periodic.
- Stream processing deals with data as it arrives, while batch processing operates on collected data.
- Stream processing enables immediate insights, while batch processing provides insights after the data is processed.
- Stream processing is suitable for low-latency, real-time applications, while batch processing is suitable for large-scale data analysis and reporting.

<br/>
<u>What is Google Cloud pub/sub?</u>

- Google Cloud Pub/Sub is a fully managed messaging service provided by Google Cloud.
- It enables asynchronous and reliable communication between independent applications.
- Pub/Sub follows the publish-subscribe model, where publishers send messages to topics, and subscribers receive messages from subscriptions.
- It provides scalable and durable message storage, ensuring message delivery even during system failures.
- Pub/Sub supports at-least-once delivery semantics, ensuring message reliability.
- It integrates with other Google Cloud services, enabling seamless data flow and event-driven architectures.
- Pub/Sub allows for the decoupling of sender and receiver systems, improving scalability and flexibility.
- It supports push and pull message delivery mechanisms to accommodate different application requirements.
- Pub/Sub can handle high throughput and high-volume message processing.
- It provides access controls and authentication mechanisms to secure message communication.

<br/>
<u>What is a Google BigQuery? (Analysis workload, SQL)</u>

- Google BigQuery is a fully managed, serverless data warehouse and analytics platform provided by Google Cloud.
- It is designed for processing and analyzing large-scale datasets in a fast and scalable manner.
- BigQuery supports analysis workloads, allowing users to run complex queries on large datasets.
- It offers a familiar SQL interface for querying and manipulating data.
- BigQuery provides high-performance query execution, leveraging distributed computing power.
- It automatically scales resources based on query demands, ensuring fast query response times.
- BigQuery integrates with other Google Cloud services and third-party tools for data ingestion and export.
- It supports data encryption at rest and in transit, ensuring data security.
- BigQuery allows for real-time analysis using streaming data ingestion.
- It provides features for data exploration, data visualization, and machine learning integration.

<br/>
<u>What is Google's cloud bigTable? (Analysis workload, NoSQL)</u>

- Google Cloud Bigtable is a highly scalable, NoSQL database service provided by Google Cloud.
- It is designed to handle large-scale, high-throughput workloads with low latency.
- Bigtable is a distributed, columnar database that can store and retrieve vast amounts of structured data.
- It is suitable for applications requiring real-time analytics, time-series data, and high-volume data processing.
- Bigtable provides linear scalability, allowing users to add or remove nodes to meet changing workload demands.
- It offers automatic sharding and load balancing, distributing data and queries across multiple nodes.
- Bigtable supports high-speed read and write operations, making it suitable for applications with low-latency requirements.
- It integrates with other Google Cloud services, such as BigQuery and Dataflow, for data processing and analysis.
- Bigtable provides data durability and fault-tolerance through replication and data backup mechanisms.
- It is used by various industries for use cases like IoT data storage, time-series data analysis, and ad serving platforms.

<br/>
<u>What is Google's cloud spanner? (SQL, global scalability)</u>

- Google Cloud Spanner is a globally distributed, horizontally scalable, and strongly consistent relational database service provided by Google Cloud.
- It combines the benefits of relational databases with the scalability and global reach of a NoSQL database.
- Spanner supports SQL queries and provides ACID transactions across globally distributed data centers.
- It offers automatic scaling and rebalancing of resources to handle changing workloads and data volumes.
- Spanner provides high availability and fault tolerance, with built-in replication and data redundancy.
- It offers strong consistency guarantees, ensuring that all replicas of the data are consistent at all times.
- Spanner is suitable for applications requiring global scalability, high throughput, and low-latency data access.
- It integrates with other Google Cloud services and supports seamless data replication and synchronization.
- Spanner provides schema changes without downtime, enabling schema evolution in a live system.
- It is used for various use cases, including multi-region data replication, financial applications, and global inventory management.

<br/>
<u>What is Google's Firebase? (noSQL)</u>

- Google Firebase is a comprehensive development platform provided by Google for building web and mobile applications.
- It offers a range of services and tools to support app development, backend infrastructure, and user engagement.
- Firebase provides a NoSQL database called Cloud Firestore for storing and syncing data in real-time.
- Firestore is designed for scalability, offline data access, and real-time synchronization across devices.
- Firebase offers authentication services, allowing developers to easily implement user registration, login, and access control.
- It provides cloud functions for serverless computing, enabling developers to run custom code in response to events.
- Firebase includes hosting services for deploying and serving web apps with HTTPS support.
- It offers a variety of SDKs and libraries for different platforms, making it easy to integrate Firebase services into applications.
- Firebase provides analytics, crash reporting, and performance monitoring tools to gain insights into app usage and performance.
- It supports push notifications, in-app messaging, and remote configuration to enhance user engagement and app personalization.

<br/>
**App Engine**

<br/>
<u>NoSQL database:</u> 
Cloud Datastore, Cloud Firestore, Cloud BigTable

<br/>
<u>Relational database: </u>
Cloud SQL or Cloud AlloyDB, Cloud Spanner

<br/>
<u>File/object storage: </u>
Cloud Storage, Cloud Filestore, Google Drive

<br/>
<u>Caching: </u>
Cloud Memorystore (Redis or Memcached)

<br/>
<u>Task execution: </u>
Cloud Tasks, Cloud Pub/Sub, Cloud Scheduler, Cloud Workflows

<br/>
<u>User authentication: </u>
Cloud Identity Platform, Firebase Auth, Google Identity Services

<br/>
**Endpoint**

<br/>
<u>What is a cloud endpoint?</u>

- Cloud Endpoint is a service provided by Google Cloud for building, deploying, and managing APIs.
- It allows developers to create, secure, and monitor APIs for their applications.
- Cloud Endpoint provides features like request validation, authentication, and authorization.
- It supports various API protocols, including REST and gRPC.
- Cloud Endpoint integrates with other Google Cloud services, such as Cloud Run and Cloud Functions.
- It offers features like API versioning, traffic splitting, and analytics for API management.
- Cloud Endpoint provides client libraries and tools for generating API documentation and SDKs.
- It offers scalability and reliability, handling API traffic across multiple regions.
- Cloud Endpoint helps developers focus on building APIs by handling infrastructure and security concerns.
- It supports deployment on Google Cloud or on-premises using Google Kubernetes Engine (GKE).

<br/>
<u>What is Apigee API management?</u>

- Apigee is an API management platform provided by Google Cloud.
- It enables organizations to design, deploy, and manage APIs at scale.
- Apigee offers features for API lifecycle management, including design, development, testing, and deployment.
- It provides tools for API security, authentication, and access control.
- Apigee allows organizations to create developer portals for API documentation, onboarding, and support.
- It offers analytics and monitoring capabilities to track API usage, performance, and trends.
- Apigee supports traffic management and rate limiting to ensure API availability and performance.
- It integrates with other Google Cloud services and third-party tools for seamless API integration.
- Apigee provides tools for API versioning, caching, and transformation.
- It helps organizations with API governance, compliance, and policy enforcement.

<br/>
**Logging**

<br/>
<u>What is Infrastructure as Code (IaC)?</u>

- Infrastructure as Code refers to the practice of managing and provisioning infrastructure resources using machine-readable configuration files or scripts.
- It enables the automation and reproducibility of infrastructure deployment and management.
- IaC treats infrastructure as software, allowing it to be version-controlled, tested, and managed using familiar software development practices.
- It reduces manual configuration and human error by codifying infrastructure provisioning and configuration steps.
- IaC tools, such as Terraform and AWS CloudFormation, allow infrastructure to be defined declaratively, specifying the desired state of resources.
- Infrastructure changes can be tracked, reviewed, and audited through version control systems.
- IaC promotes consistency and scalability by providing a repeatable and scalable approach to infrastructure deployment and management.
- It supports cloud platforms, virtualization technologies, and configuration management tools.
- IaC enables the creation and management of infrastructure resources across different environments, such as development, testing, and production.
- It facilitates collaboration among teams by providing a shared and collaborative infrastructure definition.

<br/>
<u>Why need Logging and Monitoring with Cloud Monitoring?</u>

- Logging and monitoring are essential for understanding the health, performance, and behavior of applications and infrastructure.
- Cloud Monitoring provides real-time insights into the performance and availability of cloud resources.
- Logging allows capturing and storing log data from various sources, such as applications, virtual machines, and containers.
- Monitoring enables proactive detection of issues, performance bottlenecks, and anomalies in real-time.
- Logging helps in troubleshooting, debugging, and auditing by providing a record of events and actions.
- Monitoring provides alerts and notifications to notify about critical events or deviations from predefined thresholds.
- It helps in capacity planning and resource optimization by analyzing usage patterns and trends.
- Logging and monitoring enable compliance with regulatory requirements and security auditing.
- Cloud Monitoring integrates with other Google Cloud services and third-party tools for comprehensive visibility.
- It supports customizable dashboards, visualizations, and reporting for data analysis and decision-making.

<br/>
<u>What is Google Cloud logging?</u>

- Google Cloud Logging is a service provided by Google Cloud for collecting, storing, and analyzing log data from various sources.
- It supports different types of logs, including Cloud Audit logs, agent logs, network logs, and service logs.
- Cloud Audit logs capture API calls and administrative actions for auditing and compliance purposes.
- Agent logs are generated by monitoring agents installed on virtual machines or containers, providing insights into system-level activities.
- Network logs capture network traffic information for monitoring and troubleshooting network-related issues.
- Service logs are specific to Google Cloud services and provide detailed information about service activities and events.
- Cloud Logging allows for centralized log management and analysis across multiple projects and resources.
- It provides filtering, searching, and correlation capabilities to extract meaningful insights from log data.
- Cloud Logging integrates with other Google Cloud services, such as Cloud Monitoring and BigQuery, for advanced analysis and visualization.
- It supports log export to external systems and tools for further processing and archiving.
- Cloud Logging helps in troubleshooting, performance monitoring, security analysis, and compliance auditing.

<br/>
<u>What is Google error reporting?</u>

- Google Error Reporting is a service provided by Google Cloud for collecting, analyzing, and managing application error data.
- It automatically captures and aggregates error information from applications running on Google Cloud.
- Error Reporting supports multiple programming languages and frameworks, including Java, Python, Node.js, and more.
- It provides real-time notification and alerting for critical errors and exceptions.
- Error data includes stack traces, error messages, affected user information, and other relevant metadata.
- It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive error analysis.
- Error Reporting allows for grouping and deduplication of similar errors to focus on root causes and prioritize fixes.
- It offers customizable dashboards and reports to visualize error trends, patterns, and impact.
- Error Reporting helps in identifying and resolving application issues, improving application quality and user experience.
- It supports error data export to external systems and tools for further analysis and integration.

<br/>
<u>What is Google Cloud Trace?</u>

- Google Cloud Trace is a service provided by Google Cloud for capturing, analyzing, and visualizing application latency data.
- It helps in understanding and optimizing the performance of applications running on Google Cloud.
- Cloud Trace captures timing data for requests and traces the flow of requests across different services and components.
- It provides insights into latency bottlenecks, performance issues, and dependencies between different parts of an application.
- Cloud Trace integrates with popular programming languages and frameworks, allowing developers to instrument their code for tracing.
- It offers detailed information about request processing time, network latency, and time spent in different parts of the application.
- Cloud Trace provides visualizations, including flame graphs and waterfall diagrams, to visualize the latency distribution and identify bottlenecks.
- It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive performance analysis.
- Cloud Trace helps in troubleshooting and optimizing application performance, leading to improved user experience and efficiency.
- It supports exporting trace data to external systems and tools for further analysis and integration.

<br/>
<u>What is a Google Cloud profile?</u>

- Google Cloud Profiler is a service provided by Google Cloud for profiling and analyzing application performance.
- It helps developers identify performance bottlenecks, hotspots, and inefficiencies in their applications.
- Cloud Profiler collects and analyzes CPU usage, memory allocations, and function call traces.
- It provides detailed performance data at the function level, allowing developers to pinpoint performance issues.
- Cloud Profiler integrates with popular programming languages and frameworks, including Java, Go, Python, and Node.js.
- It requires minimal code instrumentation to capture performance data.
- Cloud Profiler offers visualizations and reports to understand application behavior and performance patterns.
- It integrates with other Google Cloud services, such as Cloud Monitoring and Cloud Logging, for comprehensive performance analysis.
- Cloud Profiler helps in optimizing application performance, reducing resource usage, and improving scalability.
- It supports exporting profiling data to external systems and tools for further analysis and integration.

<br/>
**Security**

<br/>
<u>What are rate quotes and allocation quotes?</u>

- Rate Quote is an estimate provided by Google Cloud for the cost of using specific Google Cloud services and resources.
- It helps customers understand the anticipated charges based on the usage and pricing details of the selected services.
- Rate Quotes provide transparency and assist in budgeting and cost planning for using Google Cloud services.
- Allocation Quote is an allocation of resources provided by Google Cloud to customers for specific services or projects.
- It defines the limits and capacity allocated to a customer, such as compute resources, storage, or API quotas.
- Allocation Quotes ensure that customers have sufficient resources available to meet their needs without exceeding the allocated capacity.
- Both Rate Quotes and Allocation Quotes are essential for customers to understand the cost implications and resource availability associated with using Google Cloud services.

<br/>
<u>What is the editor and owner role of Google Cloud?</u>

Editor Role:

- The Editor role is a predefined Google Cloud IAM (Identity and Access Management) role.
- Users assigned the Editor role have broad access to manage resources within a project.
- Editors can create, modify, and delete resources, including instances, networks, and storage buckets.
- They can also grant access to other users and assign roles to control resource permissions.

Owner Role:

- The Owner role is a predefined Google Cloud IAM role with the highest level of access.
- Owners have full control and authority over a project and all its resources.
- They can perform all actions available to Editors, as well as manage billing and project settings.
- Owners can add or remove other project members, assign roles, and manage IAM policies.

Note: It is important to follow the principle of least privilege and assign roles based on the specific needs and responsibilities of individuals or teams.

<br/>
<u>What is encryption at rest?</u>

- Encryption at rest refers to the practice of encrypting data when it is stored or "at rest" in storage systems or databases.
- It ensures that data remains protected even if the storage media or devices are compromised.
- Encryption at rest uses cryptographic algorithms to convert plaintext data into ciphertext, rendering it unreadable without the encryption key.
- It adds an extra layer of security to sensitive data, preventing unauthorized access or data breaches.
- Encrypted data remains encrypted until it is accessed or retrieved by authorized users or applications.
- Encryption at rest is commonly used in cloud storage services, databases, and file systems.
- It helps organizations meet security and compliance requirements for data protection and privacy.
- Encryption keys used for encryption at rest should be securely managed and protected to maintain the confidentiality and integrity of the data.

<br/>
<u>What is Google's front end?</u>

- Google Front End (GFE) is a global infrastructure component of Google Cloud that handles incoming network traffic.
- GFE acts as a load balancer and SSL/TLS termination point for secure connections.
- It receives client requests and distributes them to backend services for processing.
- GFE supports Transport Layer Security (TLS) encryption for secure communication between clients and services.
- It performs SSL/TLS termination by decrypting incoming encrypted traffic and forwarding it to backend services as unencrypted traffic.
- GFE handles the complexities of SSL/TLS negotiation and certificate management.
- It offloads the computational overhead of SSL/TLS encryption from backend services, improving their performance.
- GFE supports advanced features like HTTP/2 and QUIC protocols for efficient and faster communication.
- Google Front End plays a crucial role in providing secure and scalable network access to Google Cloud services.

<br/>
<u>What is intrusion detection?</u>

- Intrusion detection is a security mechanism used to identify and respond to unauthorized activities or malicious behavior within a computer system or network.
- It involves monitoring and analyzing network traffic, system logs, and other data sources to detect signs of intrusion or suspicious activity.
- Intrusion detection systems (IDS) can be host-based or network-based, depending on the scope of monitoring.
- Host-based IDS monitor activities on individual systems, while network-based IDS monitor network traffic for anomalies.
- Intrusion detection systems use various techniques such as signature-based detection, anomaly detection, and behavior-based detection.
- Signature-based detection involves comparing observed patterns with known attack signatures or patterns.
- Anomaly detection identifies deviations from normal behavior based on predefined thresholds or statistical models.
- Behavior-based detection analyzes user and system behavior to identify abnormal or malicious activities.
- Intrusion detection systems generate alerts or notifications when suspicious activities are detected, allowing for timely response and mitigation.
- Intrusion detection is an important component of a comprehensive security strategy and helps protect systems and networks from unauthorized access and potential threats.

<br/>
<u>What is insider risk?</u>

- Insider risk refers to the potential threat posed by individuals within an organization who have authorized access to sensitive data, systems, or resources.
- It involves the risk of intentional or unintentional misuse, theft, or compromise of confidential information by employees, contractors, or trusted insiders.
- Insider risks can arise from malicious actions, such as data theft, sabotage, or unauthorized access, as well as unintentional errors or negligence.
- Insider risks can result in data breaches, financial loss, reputational damage, and regulatory non-compliance.
- Common insider risk scenarios include unauthorized data access, unauthorized sharing of sensitive information, insider trading, and intellectual property theft.
- Insider risk mitigation strategies include implementing access controls and least privilege principles, monitoring and auditing user activities, and conducting employee awareness and training programs.
- Insider risk management involves a combination of technical controls, policies, procedures, and employee education to detect, prevent, and respond to insider threats.
- Organizations may deploy insider threat detection solutions, data loss prevention (DLP) tools, and behavior analytics to identify suspicious activities and patterns.
- Collaboration between HR, IT, and security teams is crucial in addressing insider risk through effective policies, incident response plans, and employee monitoring practices.
- Proactive identification and mitigation of insider risks are essential to protect sensitive data, maintain trust, and safeguard organizational assets.

<br/>
<u>What is the security responsibility shared between customers and Google Cloud?</u>

<br/>
What is a different type of encryption key?

<br/>
What is Google KMS?

<br/>
What is cloud identity?

<br/>
What are predefined roles and custom roles?

<br/>
What is a service account?

<br/>
What is a virtual private cloud?

<br/>
What is a public IP address and a private IP address?

<br/>
**Networking**

<br/>
<u>What is Load balancing?</u>

<br/>
<u>What is IPsec VPC protocol?</u>

<br/>
<u>What is direct peering?</u>

<br/>
<u>What is a dedicated interconnect?</u>

<br/>
<u>What is partner interconnect?</u>