AWS (Amazon Web Services) Certification Practice Exam

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Dive into the AWS Certification Exam with our comprehensive quiz. Challenge yourself with multiple-choice questions designed to mimic the exam format. Each question includes detailed explanations to boost your understanding and help you ace your AWS Certification!

Each practice test/flash card set has 50 randomly selected questions from a bank of over 500. You'll get a new set of questions each time!

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What distinguishes a cloud-native database like Amazon DynamoDB?

It relies heavily on traditional server architecture
It requires pre-provisioning of database tables
It automatically scales for varying workloads
It is always structured

The correct answer is: It automatically scales for varying workloads

A cloud-native database like Amazon DynamoDB is distinguished by its ability to automatically scale for varying workloads. This feature is essential for applications that experience fluctuating traffic patterns and need to accommodate spikes in demand without manual intervention. DynamoDB's architecture allows it to dynamically adjust its throughput capacity in response to real-time usage, ensuring consistent performance even under heavy loads. This automatic scaling is achieved through its distributed nature, which effortlessly handles increases in data and request volume. As a result, users can focus on application development rather than managing database capacity, and they only pay for the resources they actually use, making it a cost-effective solution. In contrast, a reliance on traditional server architecture would imply a more static and fixed database setup, which doesn't align with the cloud-native approach. Pre-provisioning of database tables suggests a level of planning and resource allocation that contradicts the on-demand nature of cloud-native databases. Finally, the notion that a cloud-native database is always structured doesn’t hold true as many cloud-native databases, including DynamoDB, support both structured and semi-structured data, allowing for flexible data models.