A multinational corporation collects data from various sources, including IoT devices, web logs, and customer interactions. They need a solution that can store vast amounts of diverse data and make it available for advanced analytics. Which solution would best fit their needs?
- Amazon S3
- Hadoop Distributed File System (HDFS)
- MongoDB
- PostgreSQL
For a corporation with diverse data sources, HDFS is a distributed file system designed to store and analyze big data. It can handle a wide range of data types, making it suitable for advanced analytics.
Which pattern is essential in ensuring that microservices can independently scale based on their individual needs?
- Circuit Breaker
- Event Sourcing
- Load Balancing
- Service Discovery
Load Balancing is essential to distribute the traffic evenly among microservices, enabling independent scaling based on their needs.
Which functionality would you typically NOT find in a standard ERP system?
- Customer Relationship Management (CRM)
- Financial Accounting
- Human Resource Management
- Inventory Management
A standard ERP system typically does not include CRM functionalities. ERP focuses on core business operations like finance, HR, and inventory, while CRM specializes in managing customer relationships. Integrating both systems is common but not inherent in ERP.
Google Cloud Functions is a ________ computing platform.
- Containerized
- Microservices
- Serverless
- Virtualized
Google Cloud Functions is a serverless computing platform, meaning it allows you to run code without managing the underlying infrastructure.
Cloud platforms that specifically cater to the needs of IoT devices by offering tools and services for connecting, analyzing, and managing those devices are known as:
- IaaS (Infrastructure as a Service)
- IoTaaS (IoT as a Service)
- PaaS (Platform as a Service)
- SaaS (Software as a Service)
IoTaaS (IoT as a Service) platforms are designed to support IoT device connectivity, data analysis, and management, making them a specialized choice for IoT applications.
A startup wants to build a serverless application architecture and is comparing the offerings of major CSPs. They want a solution that integrates well with other services from the same provider. Which CSP's serverless platform might be suitable?
- Amazon Web Services (AWS) - AWS Lambda
- Google Cloud Platform (GCP) - Cloud Functions
- IBM Cloud - OpenWhisk
- Microsoft Azure - Azure Functions
AWS Lambda is known for its seamless integration with other AWS services, making it an excellent choice for a serverless architecture that leverages the AWS ecosystem.
The cloud computing model in which the infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers is called ________.
- Community Cloud
- Hybrid Cloud
- Private Cloud
- Public Cloud
A Private Cloud is established exclusively for a single organization, offering dedicated infrastructure to ensure security and control over cloud resources.
Which of the following best describes the relationship between IoT and the Cloud?
- IoT devices are entirely separate from the cloud
- IoT devices connect directly to the cloud
- IoT devices use the cloud for data storage and analysis
- IoT devices use the cloud for power supply
The relationship between IoT and the Cloud is that IoT devices use the cloud for data storage and analysis. IoT devices generate a vast amount of data, and the cloud provides the infrastructure for storing and processing this data efficiently.
SaaS applications typically offer extensive ________ capabilities to cater to a diverse set of customers.
- Backup
- Customization
- Networking
- Printing
SaaS applications often provide extensive customization capabilities to adapt the software to the specific needs of diverse customers. This flexibility is a key feature of SaaS.
Why are cloud-based data lakes preferred over traditional data warehouses for storing big data?
- Data Security and Reliability
- Query Performance and Structured Data
- Real-time Data Processing
- Scalability and Cost-Effectiveness
Cloud-based data lakes are preferred due to their scalability and cost-effectiveness. They can seamlessly scale to handle massive amounts of data while offering a pay-as-you-go pricing model. This is crucial for big data workloads where storage needs can grow significantly.