AWS Certified Developer Associate Practice Test (DVA-C02)

A canary deployment strategy involves rolling out changes to a small subset of users to test the impact before deploying it to the entire infrastructure. This minimizes the risk as it allows for monitoring and quick rollback if necessary. Blue/green deployment involves switching traffic between two identical environments that only differ in the version of the application they are running. Rolling updates incrementally replace the previous version with a new version across all hosts. Resume-based deployment is not a recognized AWS deployment strategy.

The team should update their production alias to point to both the old and new versions of the Lambda function and then use version weights within the alias configuration to specify the percentage of traffic that each version receives. This setup allows them to control the traffic flow and incrementally increase the weight towards the new version as confidence in stability increases. Assigning 100% to one version, updating the function code directly, or deploying without aliases, does not provide the gradual traffic shifting capability required for a canary release strategy. Therefore, careful allocation of weights to the alias is the correct approach.

Encrypting personally identifiable information (PII) is a fundamental best practice and a technical measure mentioned by many data protection regulations like GDPR. However, it does not single-handedly ensure compliance. Global regulations have a wide range of other requirements, such as establishing a lawful basis for processing data, adhering to data retention and minimization principles, respecting data subject rights (e.g., the right to access or be forgotten), and complying with data sovereignty rules that may dictate the physical location where data is stored. Therefore, compliance requires a holistic approach that includes legal, organizational, and multiple technical controls, not just encryption.

The correct answer is AWS X-Ray. AWS X-Ray helps developers analyze and debug distributed applications. It allows developers to add annotations, which are indexed key-value pairs, to trace data. This enables powerful filtering and searching capabilities, such as finding all traces associated with a specific user ID, which is exactly what the scenario requires.

• Amazon CloudWatch Logs is used for collecting and monitoring logs, not for adding indexed annotations to distributed traces.
• AWS Lambda is a compute service that runs code. While you can instrument Lambda functions to send data to X-Ray, Lambda itself is not the tracing service.
• Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS; it does not handle application tracing.

AWS Secrets Manager is the correct choice because it is specifically designed for securely storing secrets and provides native, automated rotation capabilities for services like Amazon RDS. While AWS Systems Manager Parameter Store can store secrets, it does not offer built-in automatic rotation functionality; this would require a custom solution. AWS Key Management Service (KMS) is used to manage the encryption keys that protect secrets, but it does not manage or rotate the secrets themselves. AWS IAM Identity Center is used for managing user access to AWS accounts and applications, not for programmatic secrets management.

A resource-based policy, specifically an S3 bucket policy, is the correct method to directly grant permissions on the S3 bucket to entities in another AWS account without sharing any user credentials. This policy allows the external account to assume the necessary permissions. While a service control policy (SCP) applies to all IAM entities in an AWS Organizations account and does not grant permissions to outside accounts, and IAM user policies are attached directly to users within your account, not to resources.

Allocating more memory to the serverless function can lead to an improvement in the compute capacity available to the function. Since the compute capacity scales with the memory size, this can decrease execution time for complex tasks and large data payloads. Thus, timeouts are less likely to occur. Extending the function's execution time may alleviate symptoms but does not address performance inefficiency and may increase costs without solving the real issue. Removing code lines is a practice of code optimization, but without a strategy, it can be ineffective at solving the problem. Moreover, it is not a configuration setting that can be adjusted for performance tuning. Diagnostic tracing with a tool can highlight performance issues, but it does not directly improve execution time.

The correct service for tracking requests in a distributed system, providing an end-to-end view of requests as they travel through the application, is intended to enable developers to analyze and debug systems. It helps in creating a map of services used by an application with detailed latency information for each service component. The other options listed are not primarily used for application tracing. CloudTrail is for auditing API use, Inspector for security assessments, and CloudFormation for infrastructure management.

Using a secrets management service to dynamically inject configurations at runtime ensures that the microservice remains environment-agnostic and that sensitive information is not stored within the container image. This approach promotes security and flexibility in configuration management, essential for maintaining best practices in cloud application deployments. Hard-coding settings violates security best practices and reduces flexibility. Instance metadata is meant for obtaining information about the host, not for storing sensitive configuration data. Defining sensitive information as environment variables within the image build process exposes them in the image layers, which is not secure.

The developer should use Amazon API Gateway's built-in request validators for checking the conformity of incoming data structures. If a submission fails validation, API Gateway can be configured to transform and return a structured message by modifying the integration response. This directly fulfills the need to send a client error status and a specific message format without the need to write additional code in AWS Lambda. Other options either introduce unnecessary complexity or do not provide the means to send structured messages directly from the gateway.

Using AWS managed encryption with Amazon DynamoDB provides transparent data encryption at rest without affecting the performance of the application. It uses AWS Key Management Service (AWS KMS) to manage the encryption keys, which eliminates the overhead of managing server-side encryption directly. While client-side encryption could also protect data at rest, it would add complexity to the application and could impact performance. Additionally, SSL/TLS ensures encryption in transit but does not encrypt data at rest, and IAM roles are used for access control and do not address encryption needs.

Serialization is the process of converting an object into a data format that can be easily stored or transmitted and later deserialized to recreate the original object. This concept is key in application development for storing data in a format that the data store can understand and interact with. Deserialization is the reverse process, taking data structured in a specific format and building it back into an object. Encoding and Decoding refer to the process of converting data from one form to another, particularly when discussing character sets (such as UTF-8) or binary data. They can be part of the serialization process, but in themselves are not equivalent to serializing or deserializing objects.

Using AWS Auto Scaling in conjunction with Elastic Load Balancing (ELB) allows the application to handle the increased load efficiently. Auto Scaling adjusts the amount of compute resources automatically based on the demand, while ELB distributes ingress application traffic across multiple targets, such as Amazon EC2 instances, containers, and IP addresses. This combination is the most effective and cost-efficient way of managing unpredictable traffic spikes because it ensures that the application can scale out (add resources) or scale in (remove resources) as needed. Provisioning a larger EC2 instance size would not be as cost-effective because it would involve paying for potentially unutilized resources during off-peak times. Increasing the database read replica count can improve database read scalability but would not address the application's overall ability to handle traffic spikes. Using AWS Lambda alone without implementing scaling mechanisms does provide scalability in certain scenarios but does not guarantee optimal resource management for the described web application scenario.

AWS managed policies are pre-built, centrally maintained policies that AWS updates as new services and permissions become available. Attaching an AWS managed policy such as ReadOnlyAccess gives developers the required access without the security team having to author or maintain custom JSON documents. Customer-managed and inline policies would require ongoing upkeep, and a resource-based bucket policy would not scale to all roles or other services.

Amazon ElastiCache offers a fully managed, in-memory caching service that supports both Redis and Memcached engines. Choosing ElastiCache allows the developer to deploy a cache cluster or serverless cache in minutes, offloading undifferentiated tasks such as patching and scaling while preserving API compatibility with existing open-source clients. Amazon MemoryDB for Redis is Redis-only, DynamoDB Accelerator works only with DynamoDB tables, and AWS AppConfig does not provide caching. Therefore, ElastiCache is the only option that meets all stated requirements.

Attaching an instance profile that contains an IAM role with the necessary S3 permissions to your EC2 instances is the recommended solution for this scenario. This allows the application to assume the IAM role and obtain temporary credentials, which can be used to access the S3 bucket. The use of an instance profile ensures that the EC2 instance can securely make API calls to AWS services on behalf of the user that assumed the role. Unlike static credentials, these permissions are automatically rotated and managed by AWS. Creating individual IAM users is not scalable for multiple instances, and hard-coding credentials is a security risk and violates the AWS recommended practice of not embedding secrets in code. Using a resource-based policy on the S3 bucket is not possible, as it cannot grant the necessary EC2 instance permissions to assume a role.

AWS CodeCommit provides a secure, highly scalable, managed source control service that hosts private Git repositories. It is the best choice because it integrates natively with other AWS services like IAM and CodePipeline, which aligns with the team's existing technology stack and minimizes operational overhead. GitHub and Bitbucket are third-party services that require additional configuration for integration. AWS CodePipeline is an orchestration service for CI/CD and does not host source code repositories itself.

Assigning a role to the virtual server that specifies the permissions for the object storage service is the most secure and manageably sound approach, allowing the application to access resources without the need to embed or manually enter credentials. This method leverages the capability of the virtual server to use temporary credentials that are automatically rotated, preventing the risks associated with hardcoding or exposing long-term credentials.

A blue/green deployment creates a replacement task set in a separate target group, lets you validate the new version, and then redirects production traffic in a single cutover. Because traffic is switched only after the green environment is confirmed healthy, the API remains fully available and you can revert by pointing the load balancer back to the blue environment. Rolling (in-place) and linear or canary updates modify the running tasks; although they minimize risk, they still change the live environment and can reduce capacity or surface issues during the rollout. An all-at-once update replaces every task at once, making downtime likely.

Amazon Kinesis Data Streams is the appropriate service for real-time data collection and processing of streaming data at scale. It can handle large streams of data from potentially thousands of sources, with low latencies for processing and the ability to scale according to the throughput needed.

Amazon DynamoDB, while capable of handling high-throughput and providing low-latency access to data, is not designed to be a data streaming service. Instead, it is optimized for use as a NoSQL database with strong consistency and performance at scale for application data storage.

Amazon S3 is an object storage service well-suited for storing and retrieving large amounts of data, but it is not optimized for real-time data streaming scenarios.

Amazon RDS is a relational database service that also does not provide a solution for real-time streaming data, as it is intended for managing relational databases and not for processing data streams.