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아마존 DVA-C02 자격증 취득은 개발자가 AWS 서비스와 기술에 능숙함을 입증하는 것으로, 해당 기술을 클라우드 컴퓨팅 요구사항으로 사용하는 모든 조직에게 가치 있는 자산이 됩니다. 이 자격증은 또한 클라우드 컴퓨팅 분야에서의 경력 성장과 진행을 위한 새로운 기회를 열어줍니다. 클라우드 기반 솔루션에 대한 수요가 더욱 증가함에 따라, AWS 인증 개발자 - 어소시에이트 자격증을 취득하는 것은 개발자들이 산업에서 경쟁력을 유지하고 관련성을 유지하는 뛰어난 방법입니다.

최신 AWS Certified Associate DVA-C02 무료샘플문제 (Q264-Q269):

질문 # 264
A developer created reusable code that several AWS Lambda functions need to use. The developer bundled the code into a zip archive. The developer needs to deploy the code to AWS and update the Lambda functions to use the code.
Which solution will meet this requirement in the MOST operationally efficient way?

A. Create a Lambda layer that contains the zip archive. Attach the Lambda layer to the Lambda functions.
B. Create a Lambda container image that includes the shared code. Use the container image as a Lambda base image for all the functions.
C. Upload the zip archive to Amazon S3. Configure an import path on the Lambda functions to point to the zip archive.
D. Create a new Lambda function that contains and runs the shared code. Update the existing Lambda functions to invoke the new Lambda function synchronously.

정답：A

질문 # 265
A company introduced a new feature that should be accessible to only a specific group of premium customers.
A developer needs the ability to turn the feature on and off in response to performance and feedback. The developer needs a solution to validate and deploy these configurations quickly without causing any disruptions.
What should the developer do to meet these requirements?

A. Use AWS Config to manage the feature configuration and validation. Set up AWS Config rules to turn the feature on and off based on predefined conditions.
B. Use AWS Systems Manager Parameter Store to store and validate the configuration settings for the feature. Enable lifecycle rules to turn the feature on and off.
C. Use AWS AppConfig to manage the feature configuration and to validate and deploy changes. Use feature flags to turn the feature on and off.
D. Use AWS Secrets Manager to securely manage and validate the feature configurations. Enable lifecycle rules to turn the feature on and off.

정답：C

설명：
This is a feature flag / dynamic configuration requirement: enable a new feature for a subset of users (premium customers), toggle it on/off quickly based on feedback/performance, and deploy configuration changes safely with validation and without disruption. AWS AppConfig (part of AWS Systems Manager) is designed for exactly this use case.
AWS AppConfig helps create, manage, and deploy application configurations, including feature flags, in a controlled way. It supports configuration validators (such as JSON schema or Lambda validators) to catch errors before deployment. It also supports controlled rollouts with deployment strategies to reduce blast radius (for example, gradual deployments). This allows rapid changes without redeploying code and minimizes disruption.
Option A aligns perfectly: use AppConfig feature flags to target premium users and toggle the feature quickly.
Option B is incorrect because Secrets Manager is for sensitive secrets (passwords, API keys), not feature flag management and progressive configuration deployments.
Option C is incorrect because AWS Config evaluates resource compliance and records configuration changes of AWS resources; it does not serve runtime feature flag delivery.
Option D (Parameter Store) can store configuration values, but it does not provide the same feature-flag- focused capabilities, validation/deployment strategies, and safe rollout controls that AppConfig provides.
Also, "lifecycle rules" is not how Parameter Store toggles features.

질문 # 266
A developer needs to modify an application architecture to meet new functional requirements.
Application data is stored in Amazon DynamoDB and processed for analysis in a nightly batch.
The system analysts do not want to wait until the next day to view the processed data and have asked to have it available in near-real time.
Which application architecture pattern would enable the data to be processed as it is received?

A. Fan-out driven
B. Event driven
C. Client-server driven
D. Schedule driven

정답：B

질문 # 267
A developer has implemented an AWS Lambda function that inserts new customers into an Amazon RDS database. The function is expected to run hundreds of times each hour. The function and RDS database are in the same VPC. The function is configured to use 512 MB of RAM and is based on the following pseudocode:
def lambda_handler(event, context):
db = database.connect()
db.statement("INSERT INTO Customers (CustomerName) VALUES (%s)", event.name) db.execute() db.close() After successfully testing the function multiple times, the developer notices that the execution time is longer than expected.
What should the developer do to improve performance?

A. Increase the size of the RDS database to facilitate an increased number of database connections each hour.
B. Move the database connection and close statement out of the handler. Place the connection in the global space.
C. Increase the reserved concurrency of the Lambda function.
D. Replace Amazon RDS with Amazon DynamoDB to implement control over the number of writes per second.

정답：B

설명：
A common performance issue for Lambda functions that access relational databases is the overhead of establishing a new database connection on every invocation. In the provided pseudocode, each call to lambda_handler opens a connection, executes one statement, and closes the connection. When the function runs hundreds of times per hour, repeatedly creating and tearing down connections adds latency (TCP handshake, TLS negotiation if used, authentication, database session setup), and can also increase load on the database connection manager.
AWS guidance for Lambda execution environments explains that the runtime environment may be reused across multiple invocations of the same function (a "warm" execution environment). Code that is initialized outside the handler runs once per execution environment lifecycle and can be reused on subsequent invocations in that environment. By moving database.connect() into the global scope (module-level initialization), the function can reuse an existing connection when the execution environment is warm, reducing per-invocation latency and improving overall throughput.
This change directly targets the slow part of the flow (connection setup) without changing the database engine, resizing the database, or adding unrelated concurrency settings. Increasing reserved concurrency (A) controls scaling limits but does not reduce the per-invocation connection overhead; it can even amplify connection storms. Resizing the database (B) might help capacity, but it does not address the repeated connection establishment cost and is not the first optimization. Replacing RDS with DynamoDB (D) is a major redesign and unnecessary for the stated issue.
So the best improvement, with minimal code change and aligned with Lambda best practices, is option C:
create the connection outside the handler and reuse it when possible, while still handling retries and reconnect logic if the connection becomes stale.

질문 # 268
Users are reporting errors in an application. The application consists of several micro services that are deployed on Amazon Elastic Container Serves (Amazon ECS) with AWS Fargate.
When combination of steps should a developer take to fix the errors? (Select TWO)

A. Deploy AWS X-Ray as a sidecar container to the micro services. Update the task role policy to allow access to me X -Ray API.
B. Instrument the ECS task to send the stout and spider- output to Amazon CloudWatch Logs. Update the task role policy to allow the cloudwatch Putlogs action.
C. Instrument the application by using the AWS X-Ray SDK. Update the application to use the Put- XrayTrace API call to communicate with the X-Ray API.
D. Instrument the application by using the AWS X-Ray SDK. Update the application to communicate with the X-Ray daemon.
E. Deploy AWS X-Ray as a daemon set to the Fargate cluster. Update the service role policy to allow access to the X-Ray API.

정답：A,B

설명：
The combination of steps that the developer should take to fix the errors is to deploy AWS X-Ray as a sidecar container to the microservices and instrument the ECS task to send the stdout and stderr output to Amazon CloudWatch Logs. This way, the developer can use AWS X-Ray to analyze and debug the performance of the microservices and identify any issues or bottlenecks. The developer can also use CloudWatch Logs to monitor and troubleshoot the logs from the ECS task and detect any errors or exceptions. The other options either involve using AWS X-Ray as a daemon set, which is not supported by Fargate, or using the PutTraceSegments API call, which is not necessary when using a sidecar container.
Reference: Using AWS X-Ray with Amazon ECS

질문 # 269
......

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