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Amazon DVA-C02 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Developer – Associate (DVA-C02)
Exam Number:	DVA-C02
Exam Duration:	130 minutes
Real Exam Qty:	65
Exam Format:	Multiple response, Multiple choice
Available Languages:	French, Korean, Portuguese (Brazil), Traditional Chinese, Simplified Chinese, Japanese, Spanish (Latin America), German, English
Related Certifications:	AWS Certified Solutions Architect – Associate AWS Certified Cloud Practitioner AWS Certified DevOps Engineer – Professional
Certificate Validity Period:	3 years
Passing Score:	720 (scaled score)
Exam Price:	USD 150
Recommended Training:	AWS Certified Developer – Associate Exam Guide AWS Skill Builder – Developer Learning Plan
Exam Registration:	AWS Certification Official Registration Pearson VUE AWS Exams
Sample Questions:	Amazon DVA-C02 Sample Questions
Exam Way:	Online proctored exam via Pearson VUE or in-person at authorized testing centers
Pre Condition:	No mandatory prerequisites. Recommended: 1+ year of hands-on experience developing and maintaining AWS-based applications.
Official Syllabus URL:	https://aws.amazon.com/certification/certified-developer-associate/

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With the help of performance reports of AWS Certified Developer - Associate (DVA-C02) Desktop practice exam software, you can gauge and improve your growth. You can also alter the duration and Amazon DVA-C02 Questions numbers in your practice tests. Questions of this AWS Certified Developer - Associate (DVA-C02) mock test closely resemble the format of the actual test.

To prepare for the DVA-C02 Exam, candidates should have hands-on experience working with AWS services and should be familiar with AWS best practices. They should also have a good understanding of programming languages such as Java, Python, and Ruby, as well as database technologies such as MySQL and Oracle. Candidates can prepare for the exam by taking online courses, attending AWS training sessions, and practicing with sample questions and scenarios.

Amazon AWS Certified Developer - Associate Sample Questions (Q872-Q877):

NEW QUESTION # 872
A developer is building an application to process a stream of customer orders. The application sends processed orders to an Amazon Aurora MySQL database. The application needs to process the orders in batches.
The developer needs to configure a workflow that ensures each record is processed before the application sends each order to the database.
Which solution meets these requirements?

A. Use Amazon SQS to stream the orders. Use an AWS Lambda function to process the orders.
Configure an event source mapping for the Lambda function, and set the
MaximumBatchingWindowInSeconds setting to 0.
B. Use Amazon DynamoDB Streams to stream the orders. Use an Amazon ECS cluster on AWS Fargate to process the orders. Configure an event source mapping for the cluster, and set the BatchSize setting to 1.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to stream the orders. Use an Amazon EC2 instance to process the orders. Configure an event source mapping for the EC2 instance, and increase the payload size limit to 36 MB.
D. Use Amazon Kinesis Data Streams to stream the orders. Use an AWS Lambda function to process the orders. Configure an event source mapping for the Lambda function, and set the MaximumBatchingWindowInSeconds setting to 300.

Answer: D

NEW QUESTION # 873
A developer owns and supports an application that has database credentials stored in environment variables for AWS Lambda functions. The developer needs an alternate storage method for the credentials instead of using plaintext environment variables. Which solution will handle the credentials MOST securely?

A. Use AWS CloudFormation to deploy the application. Ensure that the NoEcho property is set to true for the parameters that contain the database credentials.
B. Store the database credentials as a secret in AWS Secrets Manager. Set the secret ' s Amazon Resource Name (ARN) as the value of an environment variable. Use the AWS Parameters and Secrets Lambda Extension to retrieve the credentials in the Lambda function.
C. Store the database credentials as a string-type parameter in AWS Systems Manager Parameter Store.
Set the parameter ' s Amazon Resource Name (ARN) as the value of an environment variable. Use the AWS Parameters and Secrets Lambda Extension to retrieve the credentials in the Lambda function.
D. Use base64 encoding for the database credentials. Include the database credentials in the Lambda function ' s source code as part of a build process. Update the Lambda function ' s code to decode the credentials before the credentials are used.

Answer: B

Explanation:
The most secure solution is A because AWS Secrets Manager is purpose-built for storing, retrieving, and managing secrets such as database credentials, API keys, and tokens. In AWS guidance, Secrets Manager is recommended when applications need secure secret storage with controlled access, encryption, and integration with runtime retrieval patterns. By storing the credentials as a secret and passing only the secret ARN through an environment variable, the Lambda function avoids exposing the actual username and password in plaintext configuration.
The AWS Parameters and Secrets Lambda Extension is specifically designed to let Lambda functions retrieve secrets securely at invocation time. This reduces the risk of accidental exposure in code, deployment artifacts, or function configuration. Secrets Manager also supports automatic rotation , which is a major security advantage for database credentials and is one of the main reasons AWS recommends it over hardcoded or manually managed values.
Option B is incorrect because base64 is only encoding, not encryption, and embedding credentials in source code is insecure. Option C is less appropriate because a string type parameter in Systems Manager Parameter Store is not the secure choice for sensitive credentials; secure storage there would require SecureString , but even then Secrets Manager is the stronger AWS-recommended service for database secrets. Option D only masks values in CloudFormation outputs and logs with NoEcho ; it does not provide secure runtime secret retrieval or secret lifecycle management.
Therefore, A is the best and most secure answer according to AWS security best practices for Lambda applications handling database credentials.

NEW QUESTION # 874
A company has many microservices that are comprised of AWS Lambda functions. Multiple teams within the company split ownership of the microservices. An application reads configuration values from environment variables that are contained in the Lambda functions.
During a security audit, the company discovers that some of the environment variables contain sensitive information.
The company's security policy requires each team to have full control over the rotation of AWS KMS keys that the team uses for its respective microservices.
Which solution will meet these requirements?

A. Create customer managed keys for all Lambda functions. Use the new customer managed keys to encrypt the environment variables. Add kms:CreateGrant permission and kms:Encrypt permission to the Lambda function execution roles.
B. Create AWS managed keys for all Lambda functions. Use the new AWS managed keys to encrypt the environment variables. Add kms:Decrypt permissions to the Lambda function execution roles.
C. Create AWS managed keys for all Lambda functions. Use the new AWS managed keys to encrypt the environment variables. Add kms:CreateGrant permission and kms:Encrypt permission to the Lambda function execution roles.
D. Create customer managed keys for all Lambda functions. Use the new customer managed keys to encrypt the environment variables. Add kms:Decrypt permission to the Lambda function execution roles.

Answer: D

Explanation:
Customer-managed KMS keys are required to meet the security policy requirement of team- specific control over KMS key rotation. Each team can manage the lifecycle of its own key. The kms:Decrypt permission allows the Lambda function execution roles to decrypt the environment variables during runtime.
This solution adheres to the principle of least privilege and satisfies the need for team-specific key control.

NEW QUESTION # 875
A developer is creating an AWS Lambda function that searches for items from an Amazon DynamoDB table that contains customer contact information- The DynamoDB table items have the customer's email_address as the partition key and additional properties such as customer_type, name, and job_tltle.
The Lambda function runs whenever a user types a new character into the customer_type text input The developer wants the search to return partial matches of all the email_address property of a particular customer_type The developer does not want to recreate the DynamoDB table.
What should the developer do to meet these requirements?

A. Add a local secondary Index (LSI) to the DynamoDB table With job_tltle as the partition key and emad_address as the sort key Perform a query operation on the LSI by using the begins_wrth key condition expression With the email_address property
B. Add a global secondary index (GSI) to the DynamoDB table With ernail_address as the partition key and customer_type as the sort key Perform a query operation on the GSI by using the begins_wtth key condition expression With the emal_address property.
C. Add a global secondary index (GSI) to the DynamoDB table with customer_type as the partition key and email_address as the sort key Perform a query operation on the GSI by using the begvns_wth key condition expression With the emad_address property
D. Add a local secondary index (LSI) to the DynamoDB table With customer_type as the partition key and email_address as the sort key Perform a query operation on the LSI by using the begins_wlth key condition expression With the email_address property

Answer: C

Explanation:
* Understand the Problem:The existing DynamoDB table hasemail_addressas the partition key.
Searching bycustomer_typerequires a different data accesspattern. We need an efficient way to query for partial matches onemail_addressbased oncustomer_type.
* Why Global Secondary Index (GSI):
* GSIs allow you to define a different partition key and sort key from the main table, enabling new query patterns.
* In this case, having customer_type as the GSI's partition key lets you group all emails with the same customer type together.
* Using email_address as the sort key allows ordering within each customer type, facilitating the partial matching.
* Querying the GSI:
* You'll perform a query operation on the GSI, not the original table.
* Use the begins_with key condition expression on the GSI's sort key (email_address) to find partial matches as the user types in the customer_type field.

NEW QUESTION # 876
A developer is building various microservices for an application that will run on Amazon EC2 instances. The developer needs to monitor the end-to-end view of the requests between the microservices and debug any issues in the various microservices.
What should the developer do to accomplish these tasks?

A. Use AWS Health to monitor the health of all the microservices.
B. Use AWS CloudTrail to aggregate the microservices' logs and metrics, and build the monitoring dashboard.
C. Use Amazon CloudWatch to aggregate the microservices' logs and metrics, and build the monitoring dashboard.
D. Use the AWS X-Ray SDK to add instrumentation in all the microservices, and monitor using the X-Ray service map.

Answer: D

NEW QUESTION # 877
......

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