Answer:

Serverless architecture in the cloud entails building and deploying applications without the need to manage the underlying infrastructure.

Advantages of serverless architecture:

• Cost efficiency: Serverless architecture charges users solely for consumed compute resources, eliminating costs associated with idle instances and pre-allocated resources.
• Seamless scalability: Cloud providers automatically handle resource scaling based on demand, enabling effortless scalability of applications.
• Reduced operational burden: Maintenance and updates of servers are managed by the cloud provider, reducing operational complexities for development and IT teams.
• Rapid deployment: Serverless applications can be swiftly deployed without server setup, leading to quicker time-to-market.
• Focus on functionality: Developers can prioritize building application features, minimizing time spent on infrastructure management.

Answer:

Cloud engineers are drawn to Vision API due to its robust capabilities and easy integration. Key features of Vision API include:

• Label detection: Identifying objects, entities, and activities within images through descriptive labels and confidence scores.
• Optical Character Recognition (OCR): Extracting text from images in multiple languages for document processing.
• Logo detection: Recognizing popular brand logos for brand analysis and tagging.
• Landmark detection: Identifying natural and man-made landmarks to provide context for location-aware applications.
• Safe search detection: Analyzing images for inappropriate content to enable content moderation.
• Image attributes: Providing insights into dominant colors, image quality, and brightness levels for image analysis.

Answer:

AutoML Vision simplifies machine learning model creation for image analysis, benefiting cloud engineers by:

• Automating model building: Creating custom ML models without extensive machine learning expertise.
• Rapid and accurate predictions: Efficiently generating predictions for various image analysis tasks.
• Reduced development time: Shortening the time required for building and deploying image analysis models.

Answer:

Access control and authentication for cloud-based systems are managed using Identity and Access Management (IAM) solutions. Cloud engineers deploy IAM tools such as AWS IAM, Azure AD, or Google Cloud IAM to ensure secure data access and regulatory compliance.

Answer:

• GCP BigQuery: A serverless data warehouse for storing and analyzing massive datasets with SQL-like queries and seamless integration with Google Cloud services.
• Amazon EMR: Simplifies processing large data volumes using Apache Hadoop and Apache Spark, with automated setup, operation, and scalability.

Answer:

BigQuery streamlines data analysis by enabling SQL-like queries on massive datasets, reducing the need for complex ETL processes. It offers real-time data insights, rapid querying, and seamless integration with data visualization tools.

Answer:

Cloud SQL management involves instance provisioning, database configuration, scaling, backup and recovery setup, high availability configurations, and continuous monitoring.

Answer:

GKE (Google Kubernetes Engine) offers cloud engineers automated worker node management, simplified cluster setup, integrated security, scalability, and seamless integration with Google Cloud services.

Answer:

Configuration and management of Kubernetes involve defining and deploying containerized apps, establishing networking and storage, ensuring security, and scaling and monitoring the cluster.

Answer:

Cloud cost optimization entails monitoring resource utilization, eliminating waste, and selecting cost-effective services and pricing. Cost management involves tracking costs, setting caps, and using cost-effective models such as spot instances and autoscaling.

Answer:

Cloud network design involves architecture, traffic management, security, availability, scalability, and cost considerations. Services like Amazon VPC and Google Cloud Virtual Network aid in implementation.

Answer:

Data security in the cloud can be enhanced through techniques like encryption, data backups, recovery mechanisms, and setting secure access limits. It’s also crucial to regularly test disaster recovery strategies to ensure data retrieval in case of a loss. These established practices ensure robust data protection in cloud environments.

Answer:

Anthos serves as a hybrid and multi-cloud application platform, enabling uniform deployment and management of applications across diverse environments, encompassing on-premises setups, Google Cloud Platform (GCP), and other cloud providers. It additionally delivers benefits such as seamless configuration of on-premises settings, enhanced observability, automated processes, and robust security measures.

Answer:

The OSI model, or Open Systems Interconnection model, constitutes a structured framework comprising seven layers that represent the flow of data within a network. These layers, ranging from the Physical to the Application layer, interact with adjacent layers, forming a hierarchical network architecture.

The OSI model serves as a foundation for establishing network communication standards, protocols, and devices, promoting interoperability and streamlined collaboration between different network systems and devices. Understanding this model aids network administrators and designers in efficiently diagnosing and resolving network-related issues.

Answer:

Linux, an open-source operating system, has significantly contributed to the evolution of cloud computing. Its robustness, security, scalability, and customizable nature have made it a foundational element for cloud infrastructures. Notably, numerous prominent cloud computing systems, including Amazon Web Services, have been built upon the Linux platform, further accentuating its pivotal role in shaping the cloud landscape.

Answer:

Cloud migration entails tackling challenges such as intricate legacy applications, data, and a shortage of expertise. To address these hurdles, a systematic approach involving a comprehensive assessment of existing infrastructure, well-defined migration objectives, prudent cloud provider selection, and meticulous planning is essential. Employing appropriate tools, technology, and training for the team also contributes to overcoming these obstacles and ensuring a successful cloud migration.

Answer:

Evaluating an application’s preparedness for cloud migration involves analyzing factors like its architecture, dependencies, security measures, and scalability. This evaluation is facilitated through a combination of manual evaluation and automated tools. Application readiness implies that the application, along with its associated components, is poised for a smooth migration to the cloud devoid of significant challenges.

Answer:

Yes, executing system upgrades or downgrades with near-zero downtime is achievable through techniques such as live migration, load balancing, phased upgrades, redundant systems, and automated failover mechanisms. However, the complexity and size of the system may influence the extent of downtime required. Prior to implementing these processes in a live environment, thorough preparation and rigorous testing are imperative to ensure seamless execution.

Answer:

Code Run provides cloud engineers with a streamlined and integrated development environment, encompassing cloud-based code execution, collaborative capabilities, and efficient deployment features. This cloud-based development environment enables engineers to execute and test code securely and scalably without setting up local development setups.

Real-time collaboration facilitates seamless teamwork, while built-in deployment tools and cloud platform integrations simplify the deployment process. Customizability empowers engineers to tailor the environment to match their specific needs, enhancing productivity and code development.

Answer:

• Data Warehouse, Data Lake, Data Field, and Database each serve distinct purposes and exhibit differing architectures:
• Data Warehouse: Designed for corporate decision-making, it employs a structured schema for efficient data analysis.
• Data Lake: A scalable repository accommodating unstructured and raw data with flexible management options.
• Data Field: A horizontally scalable NoSQL database geared for real-time data processing and analytics.
• Database: An overarching term for data storage systems, encompassing relational, NoSQL, and in-memory databases.