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The Amazon DVA-C02 exam consists of 65 multiple-choice and multiple-response questions, which must be completed within 130 minutes. To pass the exam, candidates must score a minimum of 720 out of a possible 1000 points. DVA-C02 Exam Fee is $150 USD, and it can be taken online or at a testing center.
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To be eligible for the Amazon DVA-C02 Certification Exam, candidates should have at least one year of hands-on experience in developing and maintaining applications on AWS. They should also have a strong understanding of AWS services and architecture, as well as proficiency in at least one programming language such as Java, Python, or Ruby. DVA-C02 Exam consists of 65 multiple-choice and multiple-answer questions, and candidates have 130 minutes to complete it.
NEW QUESTION # 135
A developer is preparing to begin development of a new version of an application. The previous version of the application is deployed in a production environment. The developer needs to deploy fixes and updates to the current version during the development of the new version of the application. The code for the new version of the application is stored in AWS CodeCommit.
Which solution will meet these requirements?
Answer: A
Explanation:
A feature branch is a branch that is created from the main branch to work on a specific feature or task1. Feature branches allow developers to isolate their work from the main branch and avoid conflicts with other changes1. Feature branches can be merged back to the main branch when the feature or task is completed and tested1.
In this scenario, the developer needs to maintain two parallel streams of work: one for fixing and updating the current version of the application that is deployed in production, and another for developing the new version of the application. The developer can use feature branches to achieve this goal.
The developer can create a feature branch from the main branch for production bug fixes. This branch will contain the code that is currently deployed in production, and any fixes or updates that need to be applied to it. The developer can push this branch to the CodeCommit repository and use it to deploy changes to the production environment.
The developer can also create a second feature branch from the main branch for development of the new version of the application. This branch will contain the code that is under development for the new version, and any changes or enhancements that are part of it. The developer can push this branch to the CodeCommit repository and use it to test and deploy the new version of the application in a separate environment.
By using feature branches, the developer can keep the main branch stable and clean, and avoid mixing code from different versions of the application. The developer can also easily switch between branches and merge them when needed.
NEW QUESTION # 136
An application is using Amazon Cognito user pools and identity pools for secure access. A developer wants to integrate the user-specific file upload and download features in the application with Amazon S3. The developer must ensure that the files are saved and retrieved in a secure manner and that users can access only their own files. The file sizes range from 3 KB to 300 MB.
Which option will meet these requirements with the HIGHEST level of security?
Answer: C
Explanation:
https://docs.aws.amazon.com/cognito/latest/developerguide/amazon-cognito-integrating-user-pools-with-identity-pools.html
NEW QUESTION # 137
A developer is writing unit tests tor a new application that will be deployed on AWS. The developer wants to validate all pull requests with unit tests and merge the code with the main branch only when all tests pass The developer stores the code in AWS CodeCommit and sets up AWS CodeBuild to run the unit tests. The developer creates an AWS Lambda function to start the CodeBuild task. The developer needs to identify the CodeCommit events in an Amazon EventBridge event that can invoke the Lambda function when a pull request is created or updated.
Which CodeCommit event will meet these requirements?
Answer: C
Explanation:
https://aws.amazon.com/blogs/devops/automated-code-review-on-pull-requests-using-aws-codecommit-and- aws-codebuild/
NEW QUESTION # 138
A developer is modifying a large-scale IoT application that stores device telemetry data in an Amazon DynamoDB table. The telemetry data is valuable only for a limited time, but the application stores the data indefinitely. Data storage is slowing the application down. The developer needs a solution to improve the performance of the application.
Which solution will meet this requirement in the MOST operationally efficient way?
Answer: D
Explanation:
The most operationally efficient way to remove time-bound data from a DynamoDB table is to use Time to Live (TTL). TTL is a DynamoDB feature that lets you define an attribute (typically a Unix epoch timestamp) that represents an item's expiration time. After the timestamp has passed, DynamoDB automatically marks the item as expired and later deletes it in the background. This directly satisfies the requirement because the telemetry is only valuable for a limited time, yet it is currently stored indefinitely.
Enabling TTL (option C) improves performance and operational efficiency by preventing the table from growing without bounds. As the item count grows, access patterns that rely on scans, large partitions, or indexes can degrade, and storage-related overhead increases. TTL helps keep the dataset "fresh" by automatically removing stale telemetry, reducing the amount of data the application must work around and decreasing overall storage footprint.
Option A is operationally heavier: scanning and deleting items with a scheduled Lambda introduces ongoing maintenance, costs, and risk of throttling (table scans can be expensive and disruptive). It also requires custom logic, error handling, and retries. Option B addresses cost optimization for archived data but does not fix the DynamoDB table size or the performance degradation caused by keeping old data in the table; archiving is useful, but you still need an efficient deletion mechanism from DynamoDB. Option D (on-demand capacity mode) changes how capacity is managed, not how much data is stored; it does not remove stale items and therefore does not address the core problem.
Therefore, the best and most operationally efficient solution is C: add a TTL attribute to each telemetry item and enable TTL on the DynamoDB table so DynamoDB automatically expires and deletes old telemetry.
NEW QUESTION # 139
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_title.
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 the email_address property for a particular customer_type value. The developer does not want to recreate the DynamoDB table.
What should the developer do to meet these requirements?
Answer: A
Explanation:
The requirement is to search for partial matches of email_address but scoped to a specific customer_type. In DynamoDB, efficient partial matching is performed with a Query on a sort key using the begins_with() condition. However, Query requires that the partition key be specified exactly, so we need an index where customer_type can be used as the partition key, and email_address can be used as the sort key for prefix matching.
A Global Secondary Index (GSI) can be added to an existing table without recreating it, and it can use a different partition key and sort key from the base table. Option A correctly proposes a GSI with:
* Partition key: customer_type
* Sort key: email_address
Then the Lambda can run a Query on the GSI like: customer_type = :ct AND begins_with(email_address, :
prefix) which returns emails that start with the typed prefix for that customer type.
Option B is wrong because it keeps email_address as the partition key. That would require an exact email value (not a prefix) to Query, and you cannot do begins_with on a partition key; begins_with is for sort keys.
Options C and D are invalid because Local Secondary Indexes (LSIs) must share the same partition key as the base table (here, email_address). You cannot create an LSI with customer_type or job_title as the partition key when the table partition key is email_address.
Therefore, adding a GSI (customer_type PK, email_address SK) and querying it with begins_with is the correct solution.
NEW QUESTION # 140
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