Free Amazon Web Services DVA-C02 Practice Exam with Questions & Answers | Set: 9

Why Option D is Correct:When using a container-based AWS Lambda function, you are responsible for updating the base image for runtime patches. Rebuilding the container with the latest Node.js patch version ensures the function is updated with the required security patches. Publishing a new version of the Lambda function makes the updated image available for use.

Why Other Options are Incorrect:

Option A: The runtime update mode applies to functions using AWS-managed runtimes, not container-based runtimes.

Option B: Redeploying the same version of the Lambda function does not apply the security patch.

Option C: Rebuilding with the AWS OS base image does not guarantee that the latest Node.js patch version is included.

AWS Documentation References:

Lambda Function Container Images

Node.js Updates in AWS Lambda

AWS Lambda is a service that lets you run code without provisioning or managing servers. Lambda functions can be configured to access resources in a VPC, such as an Aurora database, by specifying one or more subnets and security groups in the VPC settings of the function. A security group acts as a virtual firewall that controls inbound and outbound traffic for the resources in a VPC. To allow a Lambda function to communicate with an Aurora database, both resources need to be associated with the same security group, and the security group rules need to allow TCP traffic on Port 3306, which is the default port for MySQL databases. Reference: [Configuring a Lambda function to access resources in a VPC]

SNS as a Fan-out Mechanism: SNS is perfect for triggering multiple actions from a single event (here, the image upload).

Workflow:

SNS Topic: Create an SNS topic that will be the central notification point.

S3 Event Notification: Configure the S3 bucket to send ' Object Created ' event notifications to the SNS topic.

Lambda Subscription: Subscribe your thumbnail-creating Lambda function to the SNS topic.

Email Subscription: Subscribe an email address to the SNS topic to trigger notifications.

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.

The correct answer is C because the requirement is for a live feed of log events in near real time , filtered to show only lines that begin with “ERROR.” The live tail feature for AWS Lambda and CloudWatch Logs is specifically intended for this kind of operational debugging. It allows developers to watch logs as they are generated and apply filtering so they can focus only on relevant messages during active troubleshooting.

This is particularly suitable because the function is already in production , and the company wants immediate visibility into timeout-related issues without waiting for a full batch of logs to accumulate. With live tail and a filter expression for ERROR , the developer can observe only the important log lines as new invocations happen, which speeds up root-cause analysis.

Option A is not the best answer because CloudWatch Logs Insights is powerful for querying logs, but it is primarily used for searching and analyzing logs after they have been collected, not as a continuous near-real-time live stream. Option B is incorrect because a subscription filter is used to forward logs to another destination such as Lambda, Kinesis, or Firehose; it is not the simplest way to give an operator a live interactive filtered view. A metric filter also creates metrics from log patterns but does not present the actual live log lines for review. Option D is incorrect because Amazon Athena is not used directly for querying CloudWatch Logs in real-time operational troubleshooting.

Therefore, the best solution is to use live tail with an ERROR filter to inspect relevant Lambda log events as they occur.

Amazon S3 is a service that provides highly scalable, durable, and secure object storage. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and consistent performance with seamless scalability. AWS Lambda is a service that lets developers run code without provisioning or managing servers. The developer can configure an S3 event to invoke a Lambda function that inserts records into DynamoDB whenever a new file is added to the S3 bucket. This solution will meet the requirement of inserting a record into DynamoDB as soon as a new file is added to S3.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. Lambda provides a local file system that can be used to store temporary files during invocation. The local file system is mounted under the /tmp directory and has a limit of 512 MB. The temporary files are accessible only by the Lambda function that created them and are deleted after the function execution ends. The developer can store temporary files that are less than 10 MB in the /tmp directory and access and modify them multiple times during invocation.

The correct answer is B because when an IAM role is attached to an Amazon EC2 instance through an instance profile, changes to the permissions policy for that role propagate automatically to applications that use the instance role credentials. This is the least disruptive approach because the developer does not need to restart, hibernate, terminate, or replace the EC2 instance.

AWS documentation explains that applications running on EC2 can obtain temporary security credentials from the instance metadata service for the attached IAM role. When the IAM policy attached to that role is updated to allow Amazon S3 read access , new temporary credentials issued for the role will reflect the updated permissions. This makes it possible to correct the access issue without changing the application deployment model or interrupting the running instance.

Option A is unnecessary because terminating and relaunching the instance causes avoidable disruption. Option C is also unnecessary because hibernation and restart are not required for IAM policy changes to take effect. Option D is not the best answer because although bucket policies can grant access, the issue identified is specifically that the instance role lacks S3 read permission , and restarting the instance is still unnecessary.

The AWS best-practice approach is to grant the required permission directly to the IAM role used by the EC2 instance. This keeps authorization centralized and lets the running application continue using temporary role credentials with minimal operational impact.

Therefore, the solution with the least application disruption is to add the S3 read permission to the IAM role , making B the correct answer.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. Lambda layers are a distribution mechanism for libraries, custom runtimes, and other dependencies. The developer can create a Lambda layer with the required Python library and use the layer in both Lambda functions. This will reduce the size of the Lambda deployment packages and avoid reaching the quota for the maximum size of zipped deployment packages. The developer can also benefit from using layers to manage dependencies separately from function code.

The correct answer is A . For a Lambda-based application that must use third-party API keys while ensuring secrets never appear in client code, request URLs, headers, or logs, storing the keys as encrypted Lambda environment variables protected by AWS KMS is the most secure choice among the options given. AWS supports encryption at rest for Lambda environment variables, and the function’s execution role can be granted permission to decrypt those values at runtime. This keeps the secrets entirely on the server side and out of the browser and request path.

Option B is insecure because query string parameters are a poor location for secrets. Even when HTTPS is used, query parameters can still be exposed through logs, monitoring systems, browser history, proxies, or debugging tools. Option C is clearly unacceptable because embedding secrets in client-side JavaScript exposes them to users. Obfuscation or minification does not provide security. Option D is also not appropriate because AWS resource tags are not a secrets-management mechanism and are not intended for storing confidential credentials.

From AWS security best practices, secrets should never be embedded in client applications and should be stored in protected server-side configuration with strong access controls. Although AWS Secrets Manager would often be the stronger real-world recommendation for rotating and retrieving secrets, it is not listed here. Among the available choices, KMS-encrypted Lambda environment variables best satisfy the requirements for confidentiality, controlled access, and avoiding accidental exposure in application logs or HTTP request elements.

Therefore, A is the most secure answer.

The solution that will meet the requirements is to use the AWS SDK ssm PutParameter operation in the Lambda function from the existing custom resource to store the credentials as a parameter. Set the parameter value to reference the new credentials. Set the parameter type to SecureString. This way, the developer can store the API credentials with minimal operational overhead, as AWS Systems Manager Parameter Store provides secure and scalable storage for configuration data. The SecureString parameter type encrypts the parameter value with AWS Key Management Service (AWS KMS). The other options either involve adding additional resources to the CloudFormation template, which increases complexity and cost, or do not encrypt the parameter value, which reduces security.

The requirement is to redact PII before the object content reaches the consumer, while the analytics service continues to use standard S3 GET requests. The most operationally efficient way is Amazon S3 Object Lambda.

S3 Object Lambda lets you add your own code to process and transform data as it is retrieved from S3. Instead of the analytics service reading directly from the bucket, it reads through an S3 Object Lambda Access Point. When the service performs a GET request, S3 invokes the associated Lambda function, which can modify the object payload on the fly—for example, by calling an external or internal PII redaction API and returning a redacted version of the report. The original object remains unchanged in the bucket, while consumers receive the transformed (redacted) response.

This approach is operationally efficient because it avoids:

duplicating and storing redacted copies of every report,

refactoring the analytics service to use a different datastore or ingestion path,

building a separate proxy service layer.

Option A requires significant refactoring and ongoing ETL/warehouse ops.

Option C provides confidentiality via encryption but does not perform redaction (analytics would still see PII after decryption).

Option D changes the access pattern completely (analytics no longer uses GET) and adds messaging complexity without directly returning redacted object content.

Therefore, S3 Object Lambda + Object Lambda Access Point is the most efficient solution to redact data at retrieval time.

Amazon Cognito User Pools: A managed user directory service, simplifying user registration and login.

Social Identity Providers: Cognito supports integration with external providers (e.g., Google, Facebook), reducing development effort.

IAM Roles for Authorization: Cognito-managed IAM roles grant fine-grained access to AWS resources (like Lambda functions).

Operational Overhead: Cognito minimizes the need to manage user identities and credentials independently.

AWS CloudFormation is a service that enables developers to model and provision AWS resources using templates. The developer can use the following steps to avoid accidental database deletion in the future:

Set up AWS CodeDeploy to deploy the most recent version of the application at runtime. This will ensure that the application code is always up to date and does not depend on the AMI.

Remove any commands that perform operating system patching from the UserData script. This will reduce the time that the UserData script takes to run and speed up the instance launch process.