Amazon Web Services Updated CLF-C02 Exam Questions and Answers by emilio

Amazon SageMakeris a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. It offers integrated Jupyter notebooks for data exploration, pre-built algorithms, and automatic model tuning to optimize hyperparameters.

Why other options are not suitable:

A. Amazon Personalize: A managed machine learning service that provides personalized recommendations, but does not offer the full suite of ML tools for building, training, and deploying models.

B. Amazon Comprehend: A natural language processing (NLP) service, not a general-purpose ML model building and deployment service.

C. Amazon Forecast: A managed service specifically for creating accurate forecasts using machine learning, not for general-purpose ML model development.

According to theAWS Shared Responsibility Model, customers are responsible for managing their data, classifying their assets, and using AWS's identity and access management tools to apply the appropriate permissions. Implementingmulti-factor authentication (MFA) for IAM user accountsis a customer responsibility to enhance the security of their accounts and protect against unauthorized access.

A. Apply security patches for Amazon S3 infrastructure devices: Incorrect, as AWS is responsible for managing and patching the underlying infrastructure, including storage devices.

B. Provide physical security for AWS data centers: Incorrect, as AWS is responsible for the physical security of its data centers.

C. Install operating system updates on Lambda@Edge: Incorrect, as AWS manages the underlying infrastructure for Lambda@Edge, including operating system updates.

AWS Cloud References:

AWS Shared Responsibility Model

The ability to scale resources up and down based on application load is tied to the advantage of "Stop guessing capacity." This feature allows users to scale resources automatically to match the demand, thereby avoiding over-provisioning or under-provisioning of resources. "Go global in minutes," "Benefit from massive economies of scale," and "Trade fixed expense for variable expense" are other advantages of cloud computing, but they do not specifically address scaling resources up and down based on load.

One of the key advantages of cloud computing is the ability totrade fixed expenses (like data centers and physical servers) for variable expenses. With AWS, companies pay only for the compute power, storage, and other resources they use, rather than investing heavily in on-premises infrastructure upfront. This reduces capital expenditure and helps lower overall costs.

Why other options are not suitable:

A. Go global in minutes: Refers to the ability to quickly deploy applications around the world, but does not directly reduce upfront costs.

B. Increase speed and agility: Refers to the ability to quickly scale and deploy resources, but is not directly related to cost reduction.

C. Benefit from massive economies of scale: Refers to AWS's ability to lower costs by leveraging its scale, but the primary advantage for reducing upfront costs is moving from fixed to variable expenses.