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Amazon DVA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Deployment	24%	- Prepare application artifacts for deployment 1. Use infrastructure as code (AWS CloudFormation, AWS SAM) 2. Package code and dependencies - Deploy applications on AWS 1. Deploy to AWS Lambda, Amazon EC2, Elastic Beanstalk 2. Perform safe updates and rollbacks - Implement CI/CD pipelines 1. Automate deployment and testing workflows 2. Use AWS CodePipeline, CodeBuild, CodeDeploy
Topic 2: Security	26%	- Secure application data 1. Implement encryption at rest and in transit 2. Manage secrets and credentials securely - Apply security best practices 1. Follow AWS Well-Architected Framework security principles 2. Validate application security configurations - Implement authentication and authorization 1. Manage IAM roles, policies, and permissions 2. Use Amazon Cognito for identity management
Topic 3: Troubleshooting and Optimization	18%	- Optimize application performance 1. Optimize resource usage and costs 2. Improve latency and throughput - Debug and resolve errors 1. Identify and fix code defects 2. Resolve integration and configuration problems - Troubleshoot application issues 1. Analyze logs, metrics, and traces 2. Use Amazon CloudWatch, AWS X-Ray for monitoring
Topic 4: Development with AWS Services	32%	- Write code for applications hosted on AWS 1. Implement architectural patterns 2. Use AWS SDKs and APIs 3. Develop serverless applications - Interact with AWS data services 1. Implement caching and data storage strategies 2. Work with Amazon DynamoDB, Amazon S3 - Integrate AWS services into applications 1. Implement messaging and event-driven workflows 2. Use AWS Lambda, API Gateway, Amazon EventBridge

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Amazon AWS Certified Developer - Associate Sample Questions (Q704-Q709):

NEW QUESTION # 704
A bookstore has an ecommerce website that stores order information in an Amazon DynamoDB table named BookOrders. The DynamoDB table contains approximately one million records.
The table uses OrderID as a partition key. There are no other indexes.
A developer wants to build a new reporting feature to retrieve all records from the table for a specified customer, based on a CustomerID property.

A. Create a DynamoDB global secondary index (GSI) on the table. Use CustomerID as the partition key.
Use the specified CustomerID value to run a query on the table.
B. Create a DynamoDB local secondary index (LSI) on the table. Use CustomerID as the partition key.Use the specified CustomerID value to run a query on the table.
C. Create a DynamoDB global secondary index (GSI) on the table. Use CustomerID as the sort key. Use a filter expression to perform a scan operation on the table to match on the specified CustomerID value.
D. Create a DynamoDB local secondary index (LSI) on the table. Use CustomerID as the sort key. Run a PartiQL query on the table with a SELECT statement where CustomerID equals the specified CustomerID value.

Answer: A

Explanation:
Comprehensive and Detailed Step-by-Step Explanation:The requirement is to query records by CustomerID, which is not the current partition key (OrderID). To achieve this efficiently:
* Option A: Create a GSI with CustomerID as the Partition Key:
* A Global Secondary Index (GSI) allows developers to create a different partition key and optional sort key for querying the data.
* By creating a GSI with CustomerID as the partition key, the developer can query the table efficiently using CustomerID as the primary lookup key.
* This avoids scanning the entire table and matches the requirement.
* Why Other Options Are Incorrect:
* Option B: Using CustomerID as a sort key for the GSI and performing a scan operation is inefficient. Queries are optimized, but scans are not.
* Option C and D: Local Secondary Indexes (LSI) are only valid when the partition key remains the same as the base table. Since OrderID is the base table's partition key, using CustomerID as the partition key or sort key in an LSI is not valid.
References:
* Amazon DynamoDB Documentation: GSIs

NEW QUESTION # 705
A developer created an AWS Lambda function to process data in an application. The function pulls large objects from an Amazon S3 bucket, processes the data, and loads the processed data into a second S3 bucket. Application users have reported slow response times. The developer checks the logs and finds that Lambda function invocations run much slower than expected. The function itself is simple and has a small deployment package. The function initializes quickly. The developer needs to improve the performance of the application. Which solution will meet this requirement with the LEAST operational overhead?

A. Configure the function to use ephemeral storage. Upload the objects and process data in the /tmp directory.
B. Create an Amazon EventBridge rule to schedule invocations of the function every minute.
C. Store the data in an Amazon EFS file system. Mount the file system to a local directory in the function.
D. Create a Lambda layer to package the function dependencies. Add the layer to the function.

Answer: A

Explanation:
Configuring the Lambda function to use ephemeral storage and processing data in the /tmp directory improves performance by leveraging local storage during execution.
* Why Option C is Correct:
* Ephemeral Storage: Lambda provides temporary storage (up to 10 GB) in the /tmp directory for each invocation, which is faster than pulling data directly from S3 multiple times.
* Performance Boost: Data can be downloaded to /tmp, processed locally, and uploaded to the destination S3 bucket, minimizing S3 network calls.
* Low Overhead: This approach requires only minimal changes to the function's configuration.
* Why Not Other Options:
* Option A: Using Amazon EFS adds complexity and is unnecessary for this use case.
* Option B: Scheduling the function does not address the root cause of slow performance.
* Option D: Lambda layers improve deployment efficiency, not runtime performance for this scenario.
References:
* Using Ephemeral Storage in AWS Lambda
* Best Practices for S3 and Lambda Performance

NEW QUESTION # 706
A developer is creating an AWS CloudFormation template to deploy Amazon EC2 instances across multiple AWS accounts. The developer must choose the EC2 instances from a list of approved instance types.
How can the developer incorporate the list of approved instance types in the CloudFormation template?

A. In the Resources section of the CloudFormation template, create resources for each EC2 instance type in the list.
B. Create a separate CloudFormation template for each EC2 instance type in the list.
C. In the CloudFormation template, create a separate parameter for each EC2 instance type in the list.
D. In the CloudFormation template, create a parameter with the list of EC2 instance types as AllowedValues.

Answer: D

Explanation:
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/parameters-section- structure.html Parameters:
InstanceTypeParameter:
Type: String
Default: t2.micro
AllowedValues:
- t2.micro
- m1.small
- m1.large
Description: Enter t2.micro, m1.small, or m1.large. Default is t2.micro.

NEW QUESTION # 707
A company runs an application on AWS The application stores data in an Amazon DynamoDB table Some queries are taking a long time to run These slow queries involve an attribute that is not the table's partition key or sort key The amount of data that the application stores in the DynamoDB table is expected to increase significantly. A developer must increase the performance of the queries.
Which solution will meet these requirements'?

A. Perform a parallel scan operation by issuing individual scan requests in the parameters specify the segment for the scan requests and the total number of segments for the parallel scan.
B. Create a global secondary index (GSI). Set query attribute to be the partition key of the index
C. Increase the page size for each request by setting the Limit parameter to be higher than the default value Configure the application to retry any request that exceeds the provisioned throughput.
D. Turn on read capacity auto scaling for the DynamoDB table. Increase the maximum read capacity units (RCUs).

Answer: B

Explanation:
* Global Secondary Index (GSI): GSIs enable alternative query patterns on a DynamoDB table by using different partition and sort keys.
* Addressing Query Bottleneck: By making the slow-query attribute the GSI's partition key, you optimize queries on that attribute.
* Scalability: GSIs automatically scale to handle increasing data volumes.
References:
* Amazon DynamoDB Global Secondary Indexes: https://docs.aws.amazon.com/amazondynamodb
/latest/developerguide/GSI.html

NEW QUESTION # 708
A developer built an application that calls an external API to obtain data, processes the data, and saves the result to Amazon S3. The developer built a container image with all of the necessary dependencies to run the application as a container.
The application runs locally and requires minimal CPU and RAM resources. The developer has created an Amazon ECS cluster. The developer needs to run the application hourly in Amazon Elastic Container Service (Amazon ECS).
Which solution will meet these requirements with the LEAST amount of infrastructure management overhead?

A. Create an Amazon ECS cluster and add the managed node groups feature to run the application.
B. Add an Amazon EC2 instance that runs the application.
C. Define a task definition with an AWS Fargate launch type.
D. Add a capacity provider to manage instances.

Answer: A

NEW QUESTION # 709
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