Pass the Amazon Web Services AWS Certified Associate DVA-C02 Questions and answers with ExamsMirror

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.

This solution will meet the requirements by using AWS Serverless Application Model (AWS SAM) templates and gradual deployments to automatically test the Lambda functions. AWS SAM templates are configuration files that define serverless applications and resources such as Lambda functions. Gradual deployments are a feature of AWS SAM that enable deploying new versions of Lambda functions incrementally, shifting traffic gradually, and performing validation tests during deployment. The developer can enable gradual deployments through AWS SAM templates by adding a DeploymentPreference property to each Lambda function resource in the template. The developer can set the deployment preference type to Canary10Percent30Minutes, which means that 10 percent of traffic will be shifted to the new version of the Lambda function for 30 minutes before shifting 100 percent of traffic. The developer can also use hooks to test the deployment, which are custom Lambda functions that run before or after traffic shifting and perform validation tests or rollback actions.

https://docs.aws.amazon.com/lambda/latest/dg/nodejs-context.html https://docs.aws.amazon.com/lambda/latest/dg/nodejs-logging.html

There is no explicit information for the runtime, the code is written in Node.js.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. The developer can use the AWS request ID field in the context object to obtain a unique identifier for each function invocation. The developer can configure the application to write logs to standard output, which will be captured by Amazon CloudWatch Logs. This solution will meet the requirement of logging key events with a unique identifier.

Requirement Summary:

DynamoDB table Orders

Partition key: OrderID

No sort key

100,000+ records

Need toefficiently retrieveall records where:

OrderSource = "MobileApp"

Mustoptimize for user experience(i.e., performance)

Evaluate Options:

Option A: Scan with FilterExpression

❌Inefficientfor large tables

AScanreadsevery itemin the table and then applies a filter condition.

This isslow, expensive, andnot scalableas the dataset grows.

Option B: Create a Local Secondary Index (LSI) with OrderSource

❌Invalid: LSIs require thesame partition key(OrderID) as the base table.

Since OrderSource is not part of the primary key, and we want to querybased on it, LSI won’t help.

Option C: Create a GSI with OrderSource as the sort key

❌Misuse of sort key: Querying bysort key alone is not allowedin DynamoDB.

Youmust specify a partition keyin a Query.

This design is not valid for the use case.

Option D: Create a GSI with OrderSource as the partition key

✅✅BEST CHOICE: With a GSI on OrderSource, you canquery efficientlyfor all items where OrderSource = "MobileApp".

DynamoDB GSIs allow analternative access patternfor queries not supported by the base table schema.

GSI Design:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GSI.html

Querying a GSI:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Query.html

Scan vs Query:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-query-scan.html

https://aws.amazon.com/blogs/devops/automated-code-review-on-pull-requests-using-aws-codecommit-and-aws-codebuild/

The combination of steps that will resolve the latency issue is to create an Amazon CloudFront distribution to cache the static content and store the application’s static content in Amazon S3. This way, the company can use CloudFront to deliver the static content from edge locations that are closer to the website users, reducing latency and improving performance. The company can also use S3 to store the static content reliably and cost-effectively, and integrate it with CloudFront easily. The other options either do not address the latency issue, or are not necessary or feasible for the given scenario.

AWS Secrets Manager is a service that allows you to store and manage secrets, such as database credentials, API keys, and passwords, in a secure and centralized way. It also provides features such as automatic secret rotation, auditing, and monitoring1. By using AWS Secrets Manager, you can avoid hardcoding credentials in your code, which is a bad security practice and makes it difficult to update them. You can also replicate your secrets to another Region, which is useful for disaster recovery purposes2. To accessyour secrets from your application, you can use the ARN of the secret, which is a unique identifier that includes the Region name. This way, your application can use the appropriate secret based on the Region where it is deployed3.

Centralized Logging Benefits:Centralized logging is essential for operational visibility in scalable systems, especially those using multiple EC2 instances like our e-commerce website. CloudWatch provides this capability, along with other monitoring features.

CloudWatch Agent:This is the best way to send custom application logs from EC2 instances to CloudWatch. Here's the process:

Install the CloudWatch agent on each EC2 instance.

Configure the agent with a configuration file, specifying:

Which log files to collect.

The format in which to send logs to CloudWatch (e.g., JSON).

The specific CloudWatch Logs log group and log stream for these logs.

Viewing and Analyzing Logs:Once the agent is pushing logs, use the CloudWatch Logs console or API:

View and search the logs across all instances.

Set up alarms based on log events.

Use CloudWatch Logs Insights for sophisticated queries and analysis.

Using Amazon ElastiCache for Memcached to store and manage the session data will reduce the memory load and improve the performance of the web server. Using Amazon RDS for MySQL DB instance to store the application data will provide a scalable, reliable, and managed database service. Option A is not optimal because it does not address the memory issue of the web server. Option C is not optimal because it does not provide a persistent storage for the application data. Option D is not optimal because it does not provide a high availability and durability for the session data.

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Environment Isolation: Creating separate secrets for each environment (development, staging, etc.) ensures clear separation and prevents accidental leaks.

Automatic Rotation: Secrets Manager provides built-in rotation capabilities, enhancing security posture.

Versioning: Tracking changes to secrets is essential for auditing and compliance.

The solution that will solve this problem is to create and inspect the Lambda dead-letter queue. Troubleshoot the failed functions. Reprocess the events. This way, the developer can identify and fix any issues that caused the Lambda function to fail when invoked asynchronously by API Gateway. The developer can also reprocess any orders that were not processed due to failures. The other options either do not address the root cause of the problem, or do not help recover from failures.

TTL for Automatic Deletion: DynamoDB's Time-to-Live effortlessly deletes expired items without manual intervention.

DynamoDB Stream: Captures changes to the table, including deletions of expired items, triggering downstream actions.

Lambda for Processing: A Lambda function connected to the stream provides custom logic for handling the deleted items.

Code Efficiency: This solution leverages native DynamoDB features and stream-based processing, minimizing the need for custom code.