Amazon DVA-C02 Exam Questions

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer Reference:

1. Understanding the Use Case:

The developer needs to export DynamoDB table data into Amazon S3 buckets using the AWS CLI, and some exports are failing. Proper credentials and permissions have already been configured.

2. Key Conditions to Check:

Region Consistency:

DynamoDB exports require that the target S3 bucket and the DynamoDB table reside in the same AWS Region. If they are not in the same Region, the export process will fail.

Point-in-Time Recovery (PITR):

PITR is not required for exporting data from DynamoDB to S3. Enabling PITR allows recovery of table states at specific points in time but does not directly influence export functionality.

DynamoDB Streams:

Streams allow real-time capture of data modifications but are unrelated to the bulk export feature.

DAX (DynamoDB Accelerator):

DAX is a caching service that speeds up read operations for DynamoDB but does not affect the export functionality.

3. Explanation of the Options:

Option A:

'Ensure that point-in-time recovery is enabled on the DynamoDB tables.'

While PITR is useful for disaster recovery and restoring table states, it is not required for exporting data to S3. This option does not address the export failure.

Option B:

'Ensure that the target S3 bucket is in the same AWS Region as the DynamoDB table.'

This is the correct answer. DynamoDB export functionality requires the target S3 bucket to reside in the same AWS Region as the DynamoDB table. If the S3 bucket is in a different Region, the export will fail.

Option C:

'Ensure that DynamoDB streaming is enabled for the tables.'

Streams are useful for capturing real-time changes in DynamoDB tables but are unrelated to the export functionality. This option does not resolve the issue.

Option D:

'Ensure that DynamoDB Accelerator (DAX) is enabled.'

DAX accelerates read operations but does not influence the export functionality. This option is irrelevant to the issue.

4. Resolution Steps:

To ensure successful exports:

Verify the Region of the DynamoDB tables:

Check the Region where each table is located.

Verify the Region of the target S3 buckets:

Confirm that the target S3 bucket for each export is in the same Region as the corresponding DynamoDB table.

If necessary, create new S3 buckets in the appropriate Regions.

Run the export command again with the correct setup:

aws dynamodb export-table-to-point-in-time \

--table-name <TableName> \

--s3-bucket <BucketName> \

--s3-prefix <Prefix> \

--export-time <ExportTime> \

--region <Region>

Exporting DynamoDB Data to Amazon S3

S3 Bucket Region Requirements for DynamoDB Exports

AWS CLI Reference for DynamoDB Export

Using a dead-letter queue (DLQ) with Amazon SQS is the most operationally efficient solution for handling unprocessable messages.

Amazon SQS Dead-Letter Queue:

A DLQ is used to capture messages that fail processing after a specified number of attempts.

Allows the application to continue processing other messages without being blocked.

Messages in the DLQ can be analyzed later for debugging and resolution.

Why DLQ is the Best Option:

Operational Efficiency: Automatically defers messages with errors, ensuring the queue is not blocked.

Analysis Ready: Messages in the DLQ can be inspected to identify recurring issues.

Scalable: Works seamlessly with Lambda and SQS at scale.

Why Not Other Options:

Option A: Logs the messages but does not resolve the queue blockage issue.

Option C: FIFO queues and 0-second retention do not provide error handling or analysis capabilities.

Option D: Alerts administrators but does not handle or store the unprocessable messages.

Steps to Implement:

Create a new SQS queue to serve as the DLQ.

Attach the DLQ to the primary queue and configure the Maximum Receives setting.

Using Amazon SQS Dead-Letter Queues

Best Practices for Using Amazon SQS with AWS Lambda

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer Reference:

1. Understanding the Use Case:

The goal is to store objects in an S3 bucket while optimizing storage costs. The key conditions are:

Objects are accessed infrequently after 1 week.

Objects must remain immediately accessible at all times.

2. AWS S3 Storage Classes Overview:

Amazon S3 offers various storage classes, each optimized for specific use cases:

S3 Standard: Best for frequently accessed data with low latency and high throughput needs.

S3 Standard-Infrequent Access (S3 Standard-IA): Optimized for infrequently accessed data but requires the same availability and immediate access as Standard storage. It provides lower storage costs but incurs retrieval charges.

S3 Glacier Flexible Retrieval (formerly S3 Glacier): Designed for archival data with retrieval latency ranging from minutes to hours. This does not meet the requirement for 'immediate access.'

S3 Glacier Deep Archive: Lowest-cost storage, suitable for rarely accessed data with retrieval times of hours.

3. Explanation of the Options:

Option A:

'Create an S3 Lifecycle rule to expire objects after 7 days.'

Expiring objects after 7 days deletes them permanently, which does not fulfill the requirement of retaining the objects for later infrequent access.

Option B:

'Create an S3 Lifecycle rule to transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 7 days.'

This is the correct solution. S3 Standard-IA is ideal for objects accessed infrequently but still need to be available immediately. Transitioning objects to this storage class reduces storage costs while maintaining availability and low latency.

Option C:

'Create an S3 Lifecycle rule to transition objects to S3 Glacier Flexible Retrieval after 7 days.'

S3 Glacier Flexible Retrieval is a low-cost archival solution. However, it does not provide immediate access as retrieval requires minutes to hours. This option does not meet the requirement.

Option D:

'Create an S3 Lifecycle rule to delete objects that have delete markers.'

This option is irrelevant to the given use case, as it addresses versioning cleanup, which is not enabled in the described S3 bucket.

4. Implementation Steps for Option B:

To transition objects to S3 Standard-IA after 7 days:

Navigate to the S3 Console:

Sign in to the AWS Management Console and open the S3 service.

Select the Target Bucket:

Choose the bucket where the objects are stored.

Set Up a Lifecycle Rule:

Go to the Management tab.

Under Lifecycle Rules, click Create lifecycle rule.

Define the Rule Name and Scope:

Provide a descriptive name for the rule.

Specify whether the rule applies to the entire bucket or a subset of objects (using a prefix or tag filter).

Configure Transitions:

Choose Add transition.

Specify that objects should transition to S3 Standard-IA after 7 days.

Review and Save the Rule:

Review the rule configuration and click Save.

5. Cost Optimization Benefits:

Transitioning to S3 Standard-IA results in cost savings as it offers:

Lower storage costs compared to S3 Standard.

Immediate access to objects when required.

However, remember that there is a retrieval cost associated with S3 Standard-IA, so it is best suited for data with low retrieval frequency.

Amazon S3 Lifecycle Configuration Guide

Amazon S3 Storage Classes

AWS S3 Pricing

AWS Documentation on S3 Standard-IA

API Gateway's canary deployments allow gradual traffic shifting to a new version of a function, minimizing disruption while testing.

Why Option A is Correct:

Gradual Rollout: Reduces risk by incrementally increasing traffic.

Rollback Support: Canary deployments make it easy to revert to the previous version.

Why Not Other Options:

Option B: Redeploying the stage disrupts all users.

Option C & D: Managing new stages and weighted routing introduces unnecessary complexity.

Canary Deployments in API Gateway

DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.

Why Option B is Correct:

Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.

Hot Partition: Excessive use of a single hash key can overwhelm its partition.

Why Not Other Options:

Option A: DynamoDB storage size does not affect throughput.

Option C: Provisioned scaling operations are unrelated to throughput errors.

Option D: Sort keys do not impact partition-level throughput.

DynamoDB Partition Key Design Best Practices