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Amazon EC2 instance types

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Amazon EC2 (Elastic Compute Cloud) instance types are categorized to suit various application needs, providing flexibility and scalability for diverse workloads. The primary categories include:1. **General Purpose**: Balancing compute, memory, and networking resources, suitable for a wide range of applications. Examples are the T4g and M6g instances, ideal for web servers, development environments, and small databases2. **Compute Optimized**: Designed for compute-intensive tasks requiring high-performance processors. The C6g instances are typical, used for high-performance web servers, scientific modeling, and batch processing3. **Memory Optimized**: Featuring large memory capacities, these instances are ideal for data-intensive applications like databases, in-memory caches, and real-time big data analytics. R5 and X1 instances fall into this category4. **Storage Optimized**: Engineered for workloads that demand high, sequential read and write access to large data sets on local storage. Instances like the I3 are used for NoSQL databases, data warehousing, and high-frequency online transaction processing (OLTP)5. **Accelerated Computing**: Equipped with specialized hardware such as GPUs or FPGAs for tasks requiring parallel processing power. P4 and F1 instances are examples, suitable for machine learning, gaming, and video encodingEach instance type offers various sizes, allowing users to scale resources according to demand. Additionally, instances can be launched in different purchasing models: On-Demand for flexibility, Reserved for cost savings with commitment, and Spot for utilizing unused capacity at lower pricesUnderstanding EC2 instance types enables users to select the most appropriate configuration based on application requirements, performance needs, and cost considerations. This selection is fundamental for optimizing infrastructure, ensuring efficient resource utilization, and achieving scalability within AWS environments, which are key concepts for the AWS Certified Cloud Practitioner certification.

EC2 Instance Types

Understanding Amazon EC2 instance types is crucial for the AWS Certified Cloud Practitioner exam. EC2 instances are virtual servers in the cloud that can run applications and workloads. Different instance types are optimized for various use cases, such as compute, memory, storage, and accelerated computing.

Key points about EC2 instance types:
- Instance types differ in CPU, memory, storage, and networking capacity
- Choosing the right instance type can optimize performance and cost
- Instance types are grouped into families based on their capabilities (e.g., general purpose, compute optimized, memory optimized, storage optimized, and accelerated computing)
- Instance types can be changed after launching an instance (known as instance type modification)

How to answer questions on EC2 instance types:
1. Read the question carefully and identify the key requirements (e.g., high CPU, large memory, fast storage)
2. Determine the appropriate instance family based on the requirements
3. Select the specific instance type within the family that meets the requirements and is cost-effective
4. Consider any additional features mentioned in the question, such as enhanced networking or GPU support

Exam Tips:
- Familiarize yourself with the main instance families and their use cases
- Remember that instance types can be changed after launch, allowing for flexibility
- Pay attention to questions that mention specific workload requirements, as they may guide you towards a particular instance type
- If a question doesn't provide enough information to select a specific instance type, focus on choosing the appropriate instance family

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CCP - Technologies and concepts Example Questions

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Question 1

A company is running a data-intensive application that requires high disk throughput and low latency. Which Amazon EC2 instance type should they choose for optimal performance?

Correct Answer: I3 instances, which are optimized for high random I/O performance and low latency

For a data-intensive application that requires high disk throughput and low latency, the optimal choice would be I3 instances. I3 instances are specifically designed and optimized for workloads that require high random I/O performance and low latency. They are equipped with NVMe SSD storage, which provides extremely fast and low-latency access to data. This makes them well-suited for applications that heavily rely on disk I/O operations. C5 instances are compute-optimized instances that offer high CPU performance but may not be the best fit for disk-intensive workloads. R5 instances are memory-optimized instances suitable for memory-intensive applications but may not provide the optimal disk performance required in this scenario. T3 instances provide burstable performance and are suitable for workloads with moderate CPU usage, but they are not specifically optimized for high disk throughput and low latency.

Question 2

A graphic design company requires high-performance GPU instances for their 3D rendering application. Which Amazon EC2 instance type should they choose?

Correct Answer: G4

G4 instances are specifically designed for graphics-intensive applications like 3D rendering and provide the latest generation NVIDIA GPUs. The other instance types do not offer the required GPU performance for this type of workload.

Question 3

A company needs to run a GPU-intensive application for machine learning tasks. Which Amazon EC2 instance type should they choose?

Correct Answer: P3 instances, which are optimized for machine learning and high performance computing using NVIDIA Tesla V100 GPUs.

P3 instances are the best choice for running GPU-intensive applications for machine learning tasks. These instances are optimized specifically for machine learning and high-performance computing workloads, and they come equipped with NVIDIA Tesla V100 GPUs, which are powerful and well-suited for these types of tasks. The other instance types mentioned, such as C5 (compute-optimized), R5 (memory-optimized), and M5 (general-purpose), do not have the specialized GPU hardware that is required for efficient machine learning computations. While they may be suitable for other types of workloads, they are not the optimal choice for GPU-intensive machine learning applications.

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