Free AWS Certified Developer Associate DVA-C02 Practice Test

AWS Key Management Service (KMS) is designed for secure key management and allows for automatic key rotation. When choosing the SSE-KMS option, each S3 object is encrypted with a unique data key, and these data keys are themselves encrypted with a master key from KMS, which can be configured for key rotation. SSE-C is incorrect because it requires the client to manage its own encryption keys. AWS CloudHSM is a key storage and management service, but it does not provide the simplicity of automatic key rotation that KMS does. Using AWS Lambda for key rotation is unnecessary with KMS, as KMS already provides this functionality.

When logs are formatted as JSON, they provide the capability to include key-value pairs to delineate different pieces of information, such as session, item, and shopper details. This structure allows for effective indexing and querying specific attributes, making it highly suitable for troubleshooting purposes. Delimited text logs are less ideal because while they are parseable, they do not inherently provide the structured approach that JSON offers for ease of querying. Embedding CloudWatch Logs insights is a metric embedding feature and not pertinent to log structuring for effective data retrieval. Creating separate log streams for different criteria may help with log segregation but does not address the need for structured data within the logs.

Setting up an Amazon API Gateway with mock integrations is the most effective method to mimic third-party web service responses. It enables the developer to configure the expected responses, such as those indicating a successful operation, an error, or a timeout, without needing to rely on the real web service. This provides a controlled environment for thorough assessment of the serverless function's response to diverse scenarios. Other methods, such as hardcoding responses or utilizing additional serverless functions for emulation, do not offer the same level of flexibility or convenience as API Gateway's mock integrations for simulating a wide spectrum of behaviors.

The developer should leverage Amazon Cognito Identity Pools to meet the requirements. Identity Pools provide federated authentication, enabling users to authenticate with social identity providers, and then obtain temporary permissions to access cloud services directly. This is different from User Pools, which offer user directory functionality and authentication without direct federation into service-level access.

AWS Identity and Access Management (IAM) roles are used to grant permissions to AWS resources without the need to manage static credentials. When attaching an IAM role to an EC2 instance, the instance can perform actions on AWS services that the role's policies allow. This is the recommended method for granting EC2 instances access to other AWS services. IAM users are not attached directly to resources, access keys are not a feature for granting permissions but are used for programmatic access for IAM users, and Amazon Cognito is primarily used for managing user identities in apps, not for EC2 instances to access AWS resources.

The correct approach for resource allocation involves an assessment of resource usage patterns, which can be achieved by analyzing historical data from similar workloads and by performing load testing on the application. This helps to establish a baseline and adjust resources to match the demand, avoiding under-provisioning that can lead to poor performance, as well as over-provisioning that would incur unnecessary costs.

A query operation is the correct choice for efficiently retrieving an item using a unique hash key from an Amazon DynamoDB table because a query allows you to specify the hash key and, optionally, a range key to quickly locate the data. Scan operations, in contrast, read every item in a table and filter out results afterwards, which consumes more read capacity and increases latency. Secondary index searches are only necessary when querying on non-primary key attributes, and continuous backup is a durability feature, not related to data retrieval performance.

The correct service is designed to automate code reviews and provide recommendations for enhancing code quality, including identifying security vulnerabilities. The other options listed are services that serve different purposes: orchestration of resource creation, tracking resource configurations and changes, and logging API activity across an account respectively.

AWS Lambda Aliases enable you to route traffic to different versions of a Lambda function. Aliases are like pointers and can be changed to point to different function versions as needed. This is useful for integration testing as you can maintain separate testing environments without changing the function's ARN, thus ensuring the stability and consistency of your application during the testing phase. Using different function versions or Stage variables are incorrect choices because while they can point to different configuration settings or Lambda versions, they do not offer the same aliasing capability for routing across different environments without altering the ARN.

The correct approach is to integrate the cloud application with the on-premises directory service using a federation protocol such as SAML 2.0. IAM supports federation with SAML, which allows users to authenticate using their existing corporate credentials without storing those credentials in the cloud. While Cognito is also a service that supports federation, IAM with SAML is specifically designed to work seamlessly with corporate directories like Active Directory and is hence the better-suited choice for this particular use case. The other options mentioned do not directly address the requirement of federating with an existing on-premises directory service.

The correct service for these requirements is Amazon Kinesis, as it offers a platform specifically tailored to accommodate large-scale, real-time processing of streaming content. It is capable of handling vast amounts of data with low latency, allowing developers to collect, analyze, and process information as it arrives.

Amazon Simple Storage Service, while effective for storing and retrieval of data, is not optimized for instantaneous stream processing. Likewise, AWS Lambda, although capable of executing code in response to events, functions as a compute service rather than a data ingestion and processing service. Similarly, Amazon Relational Database Service provides management of relational databases but does not cater to the real-time processing of streaming data.

Using an alias for the serverless function that points to the new function version, and configuring traffic shifting to direct a set percentage of user requests to that version, will manage the gradual rollout of the feature. Doing so allows the team to gradually increase the traffic percentage as confidence in the feature grows, while monitoring for unforeseen issues. Directly modifying environment variables or updating the function code without traffic control would result in all traffic immediately using the new feature, which can be risky for a production environment. Modifying the execution role does not relate to traffic routing and would not achieve the desired outcome.

Choosing to transition the objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days leverages the lower storage cost for less frequently accessed data while providing millisecond access. After 90 days, transitioning the data to S3 Glacier provides a cost-effective storage solution suitable for infrequent access with retrieval times that can fit historical analysis requirements. Moving data directly from S3 Standard to S3 One Zone-Infrequent Access after 30 days is less optimal because it isn't leveraging the access frequency pattern correctly. Permanently deleting the data after 90 days would not fulfill the requirement of occasional access for historical analysis.

Loosely coupled components communicate with each other in a way that reduces the dependencies between them, which makes it easier to maintain and update one component without affecting the others. This is opposed to tightly coupled components, where components depend heavily on each other, making changes more difficult without affecting the entire system.

To trigger an AWS Lambda function on S3 object creation, an event notification should be configured on the specific S3 bucket. Each event notification can specify an event type (such as 's3:ObjectCreated:*') and the destination for the event, which would be the Lambda function's ARN (Amazon Resource Name). Thus, the correct answer is 'Configure an event notification on the S3 bucket to invoke the Lambda function when an object is created'. Bucket policies and CORS configurations on the S3 bucket do not relate to the invocation of Lambda functions based on object creation events. An S3 lifecycle policy is used for object management (such as expiration or transition) and not for event invocation of Lambda functions