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Amazon AWS Certified Developer - Associate Sample Questions (Q67-Q72):

NEW QUESTION # 67
A company has a social media application that receives large amounts of traffic User posts and interactions are continuously updated in an Amazon RDS database The data changes frequently, and the data types can be complex The application must serve read requests with minimal latency The application's current architecture struggles to deliver these rapid data updates efficiently The company needs a solution to improve the application's performance.
Which solution will meet these requirements'?

A. Use Amazon DynamoDB Accelerator (DAX) in front of the RDS database to provide a caching layer for the high volume of rapidly changing data
B. Create an Amazon ElastiCache for Redis cluster. Update the application code to use a write-through caching strategy and read the data from Redis.
C. Set up Amazon S3 Transfer Acceleration on the RDS database to enhance the speed of data transfer from the databases to the application.
D. Add an Amazon CloudFront distribution in front of the RDS database to provide a caching layer for the high volume of rapidly changing data

Answer: B

Explanation:
Amazon ElastiCache for Redis: An in-memory data store known for extremely low latency, ideal for caching frequently accessed, complex data.
Write-Through Caching: Ensures that data is always consistent between the cache and the database. Writes go to both Redis and RDS.
Performance Gains: Redis handles reads with minimal latency, offloading the RDS database and improving the application's responsiveness.
Reference:
Amazon ElastiCache for Redis Documentation: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/ Caching Strategies: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Strategies.html

NEW QUESTION # 68
An application is processing clickstream data using Amazon Kinesis. The clickstream data feed into Kinesis experiences periodic spikes. The PutRecords API call occasionally fails and the logs show that the failed call returns the response shown below:

Which techniques will help mitigate this exception? (Choose two.)

A. Use a PutRecord API instead of PutRecords.
B. Use Amazon SNS instead of Kinesis.
C. Reduce the frequency and/or size of the requests.
D. Reduce the number of KCL consumers.
E. Implement retries with exponential backoff.

Answer: C,E

Explanation:
ProvisionedThroughputExceededException: The request rate for the stream is too high, or the requested data is too large for the available throughput. Reduce the frequency or size of your requests.
https://docs.aws.amazon.com/kinesis/latest/APIReference/API_PutRecords.html

NEW QUESTION # 69
A company is planning to use AWS CodeDeploy to deploy an application to Amazon Elastic Container Service (Amazon ECS) During the deployment of a new version of the application, the company initially must expose only 10% of live traffic to the new version of the deployed application. Then, after 15 minutes elapse, the company must route all the remaining live traffic to the new version of the deployed application.
Which CodeDeploy predefined configuration will meet these requirements?

A. CodeDeployDefault LambdaCanary10Percent5Minutes
B. CodeDeployDefault ECSCanary10Percent15Minutes
C. CodeDeployDefault LambdaCanary10Percent15Minutes
D. CodeDeployDefault ECSLinear10PercentEvery1 Minutes

Answer: B

Explanation:
Explanation
The predefined configuration "CodeDeployDefault.ECSCanary10Percent15Minutes" is designed for Amazon Elastic Container Service (Amazon ECS) deployments and meets the specified requirements. It will perform a canary deployment, which means it will initially route 10% of live traffic to the new version of the application, and then after 15 minutes elapse, it will automatically route all the remaining live traffic to the new version.
This gradual deployment approach allows the company to verify the health and performance of the new version with a small portion of traffic before fully deploying it to all users.

NEW QUESTION # 70
An Amazon Kinesis Data Firehose delivery stream is receiving customer data that contains personally identifiable information. A developer needs to remove pattern-based customer identifiers from the data and store the modified data in an Amazon S3 bucket.
What should the developer do to meet these requirements?

A. Create an AWS Step Functions workflow to remove the customer identifiers. As the last step in the workflow, store the transformed data in an Amazon S3 bucket. Set the workflow as the destination of the delivery stream.
B. Launch an Amazon EC2 instance. Set the EC2 instance as the destination of the delivery stream. Run an application on the EC2 instance to remove the customer identifiers. Store the transformed data in an Amazon S3 bucket.
C. Implement Kinesis Data Firehose data transformation as an AWS Lambda function. Configure the function to remove the customer identifiers. Set an Amazon S3 bucket as the destination of the delivery stream.
D. Create an Amazon OpenSearch Service instance. Set the OpenSearch Service instance as the destination of the delivery stream. Use search and replace to remove the customer identifiers. Export the data to an Amazon S3 bucket.

Answer: C

Explanation:
Explanation
Amazon Kinesis Data Firehose is a service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Amazon Kinesis Data Analytics. The developer can implement Kinesis Data Firehose data transformation as an AWS Lambda function. The function can remove pattern-based customer identifiers from the data and return the modified data to Kinesis Data Firehose. The developer can set an Amazon S3 bucket as the destination of the delivery stream.
References:
[What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose]
[Data Transformation - Amazon Kinesis Data Firehose]

NEW QUESTION # 71
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. Turn on read capacity auto scaling for the DynamoDB table. Increase the maximum read capacity units (RCUs).
D. 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.

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.

NEW QUESTION # 72
......

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New DVA-C02 Exam Format, Valid DVA-C02 Test Question

Valid DVA-C02 Test Question, Latest DVA-C02 Test Practice

Amazon AWS Certified Developer - Associate Sample Questions (Q67-Q72):

DVA-C02 Exam Torrent: AWS Certified Developer - Associate - DVA-C02 Prep Torrent & DVA-C02 Test Braindumps

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