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

CloudWatch for Log Analysis: CloudWatch is the best fit here because logs are already centralized. Here ' s the process:

Metric Filter: Create a metric filter on the CloudWatch Logs log group. Design a pattern to specifically identify application error messages.

Custom Metric: This filter generates a new custom CloudWatch metric (e.g., ApplicationErrors). This metric tracks the error count.

CloudWatch Alarm: Create an alarm on the ApplicationErrors metric. Configure the alarm with your desired threshold and a 5-minute evaluation period.

SNS Action: Set the alarm to trigger an SNS notification when it enters the alarm state.

The requirement has two parts: (1) authenticate users with a third-party SAML IdP , and (2) after authentication, allow those users to access AWS services like Amazon S3 and DynamoDB. The AWS pattern for this is to federate the external identity into AWS and then exchange that identity for temporary AWS credentials .

Amazon Cognito identity pools are designed to provide AWS credentials to authenticated users (via AWS STS) so the users can call AWS services directly or through an application backend. Identity pools support federation with external identity providers, including SAML 2.0 . When a user authenticates with the third-party SAML IdP, the identity pool can accept the SAML assertion, map the user to an IAM role, and return temporary, scoped credentials (AccessKeyId/SecretAccessKey/SessionToken) associated with that role. Those credentials can be restricted using IAM policies to only the required S3 buckets, DynamoDB tables, and actions, satisfying least privilege.

Option A is incorrect because a Cognito user pool is primarily a user directory and OIDC/OAuth provider for application authentication; while user pools can integrate with SAML IdPs for federation, user pools alone do not directly issue AWS service credentials. The key requirement here is access to S3/DynamoDB, which is the role of an identity pool . Option C is not appropriate because IAM is not intended to provide end-user sign-up/sign-in for external users; it is for AWS identities and permissions. Option D is unrelated: CloudFront signed URLs control access to CloudFront-distributed content and do not authenticate users with SAML or provide AWS credentials.

Therefore, B meets both requirements: federate SAML authentication through a Cognito identity pool and provide temporary AWS credentials for accessing S3 and DynamoDB.

Comprehensive and Detailed Explanation (250–300 words):

For in-place deployments , AWS CodeDeploy updates instances directly rather than using blue/green infrastructure. AWS documentation states that when automatic rollback is enabled and a deployment fails (for example, due to failed health checks or validation scripts), CodeDeploy initiates a rollback deployment .

In an in-place rollback, CodeDeploy redeploys the last known successful application revision to the affected instances. This rollback is treated as a new deployment , complete with its own deployment ID and lifecycle events. CodeDeploy does not use snapshots, Route 53 switching, or external promotion logic for in-place deployments.

Options A and B describe mechanisms that are used in other services or blue/green strategies, not in-place deployments. Option D is incorrect because CodePipeline orchestrates stages but does not perform rollback logic for CodeDeploy.

Therefore, redeploying the previous stable version as a new deployment is the correct behavior.

The requirement is to keep profile pictures private in S3 while allowing the browser to display them each time an employee logs in. The standard AWS pattern is to store objects in a private bucket and provide time-limited access through S3 presigned URLs . A presigned URL grants temporary permission to perform a specific operation (such as GET) on a specific object, and it expires automatically after a configured duration. This prevents public access while still allowing simple browser retrieval.

Option D is the viable long-term solution because it stores only the S3 object key (a stable identifier) in DynamoDB and generates a fresh presigned URL at login time . This is important because presigned URLs expire; storing a presigned URL (option A) would eventually store an expired URL and force extra complexity to refresh records. Generating a new presigned URL when needed ensures the link is always valid and keeps the expiration window tight, which improves security.

Option B is not appropriate for employee browsers in general: a VPC endpoint is for traffic from resources inside a VPC (EC2, Lambda in VPC, etc.). Employee browsers on the public internet cannot use a private VPC endpoint to reach S3. Option C is not viable for “no size limit” images: DynamoDB has strict item size limits, base64 inflates data size, and storing large binary blobs in DynamoDB is inefficient and expensive compared to S3.

Therefore, D best meets the requirement: keep images private in S3, store the S3 key in DynamoDB, and generate a presigned URL on each login to provide secure, temporary access for display.

This workload is overwhelmingly read-heavy and repeatedly queries the same relatively static reference data (postal codes mapped to coordinates). The best way to reduce latency and offload Amazon RDS is to introduce an application-side cache using a lazy loading (cache-aside) pattern and choose a TTL that reflects how often the underlying data changes.

With lazy loading , the application first checks the cache for the postal code. If the value is present (a cache hit), the application returns the coordinates immediately with low latency and without querying the database. If the value is absent (a cache miss), the application reads from RDS, returns the result, and then populates the cache for subsequent requests. This pattern is operationally simple and widely used because the application controls what is cached and when.

A large TTL is appropriate here because postal-code-to-coordinate mappings typically do not change frequently. A longer TTL maximizes cache hit ratio, which is critical because each postal code is requested thousands of times per day. High cache hit rates reduce database read load, decrease query contention, and substantially improve response times. In contrast, a small TTL would cause frequent expirations and repeated database lookups, reducing the performance benefit and increasing database load.

Write-through and write-behind caching (options C and B) are designed to coordinate caching with writes . They are useful when write consistency and write performance are central concerns. Here, writes are rare; the performance issue is repeated reads. Write-behind can also introduce consistency delays, which is unnecessary for this scenario.

Therefore, the best change is D : implement a lazy loading (cache-aside) caching strategy with a large TTL to keep hot, rarely changing postal code lookups in cache and dramatically reduce read latency and database load.

For a serverless web application that needs user registration and authentication with minimal configuration, the standard AWS solution is Amazon Cognito User Pools. User Pools provide managed user directories, sign-up/sign-in flows, MFA options, password reset, and token-based authentication (OAuth2/OIDC/JWT). This integrates naturally with API Gateway (Cognito authorizers) and Lambda.

Option A is best because it uses Cognito User Pools plus an app client and the Hosted UI, which provides ready-made sign-up/sign-in pages. Hosted UI minimizes custom UI and backend authentication code while still allowing branding customization and secure token issuance. The SPA can authenticate via Cognito, receive JWT tokens, and call API Gateway endpoints securely.

Option B (identity pool) is primarily for granting temporary AWS credentials to access AWS services directly (federation). It does not replace User Pools for user registration/login flows; typically identity pools are used in addition to user pools, not instead, and require more configuration.

Option C adds unnecessary infrastructure (EC2) and defeats the “minimize configuration” goal.

Option D is not appropriate for end-user authentication. IAM users/groups are for workforce/admin access, not for customer-facing apps.

Therefore, Cognito User Pool + Hosted UI is the minimal-configuration secure solution.

The correct answer is D because the most cost-effective way to prevent PutItem from overwriting an existing item in Amazon DynamoDB is to use a conditional write with attribute_not_exists(transactionID) . AWS documentation explains that conditional expressions allow write operations to succeed only when specified conditions are true. In this case, the write should occur only if the item does not already exist. That preserves the original processing date for the first received transaction and prevents duplicates from replacing it.

This is the most cost-effective solution because it uses a single write request instead of adding an additional read operation. Since duplicate transactions happen infrequently and most items are unique, using GetItem first, as in option A, would introduce an extra read for every transaction, increasing cost and adding latency. A conditional put avoids that overhead and keeps the logic atomic. It also protects against race conditions where two requests might check for existence at nearly the same time and both attempt to write.

Option B is incorrect because TTL only controls automatic expiration of items and does not prevent overwrites. Option C is incorrect because attribute_exists(transactionID) would allow the write only when the item already exists, which is the opposite of the required behavior.

Therefore, the correct DynamoDB pattern is to use PutItem with a condition expression of attribute_not_exists(transactionID) so that existing records are not updated and the earliest processing date is preserved.

Amazon Simple Queue Service (Amazon SQS) is a fully managed queue service that allows you to de-couple and scale for applications1. Amazon SQS offers two types of queues: Standard and FIFO (First In First Out) queues1. The FIFO queue uses the messageDeduplicationId property to treat messages with the same value as duplicate2. Therefore, changing the Amazon SQS standard queue to an Amazon SQS FIFO queue using the Amazon SQS message deduplication ID can help resolve the issue of the Lambda function processing some messages multiple times. Therefore, option A is correct.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and consistent performance with seamless scalability. The developer can create a DynamoDB table and configure the table with a primary key that consists of the title as the partition key and the release year as the sort key. This will enable querying for a given title and release year efficiently. The developer can also create a global secondary index that uses the genre as the partition key and the title as the sort key. This will enable querying for a given genre efficiently. The developer can store additional properties about the cast and production crew as attributes in the DynamoDB table. These attributes can have different data types and structures, and they do not need to be consistent across items.

Amazon API Gateway supports canary deployments , which are designed specifically for controlled production rollouts. Canary deployments allow a developer to direct a configurable percentage of traffic to a new deployment while the remainder continues to use the existing deployment.

AWS documentation states that canary releases help minimize customer impact by exposing only a subset of users to potential issues. Because the routing is request-based , individual users are less likely to encounter inconsistent behavior across multiple requests.

Route 53 weighted routing (Option B) operates at the DNS level and can result in users being routed unpredictably due to DNS caching. An Application Load Balancer (Option C) is not supported as a direct frontend for API Gateway stages and adds unnecessary complexity. Option A lacks traffic control guarantees.

Therefore, configuring a canary deployment on the production stage is the AWS-recommended and lowest-risk approach.

This solution will meet the requirements by using AWS Encryption SDK, which is a client-side encryption library that enables developers to encrypt and decrypt data using data encryption keys that are protected by AWS Key Management Service (AWS KMS). The SDK encrypts the data encryption key with a customer master key (CMK) that is managed by AWS KMS, and stores it (encrypted) as part of the returned ciphertext. The developer does not need to keep track of the data encryption keys used to encrypt data, as they are stored with the encrypted data and can be retrieved and decrypted by using AWS KMS when needed. Option A is not optimal because it will require manual tracking of the data encryption keys used for each data object, which is error-prone and inefficient. Option C is not optimal because it will store the data encryption keys automatically in Amazon S3, which is unnecessary and insecure as Amazon S3 is not designed for storing encryption keys. Option D is not optimal because it will store the data encryption key in the user data for the EC2 instance, which is also unnecessary and insecure as user data is not encrypted by default.

The correct answer is A because AWS Lambda requires permissions in the function’s execution role to create log groups, create log streams, and put log events into Amazon CloudWatch Logs . If the Lambda function is throwing errors but there are no CloudWatch log entries, the most likely cause is that the execution role does not include the necessary logging permissions. AWS documentation states that Lambda automatically sends logs from function code and the execution environment to CloudWatch Logs, but this behavior depends on the function’s IAM role having permissions such as logs:CreateLogGroup , logs:CreateLogStream , and logs:PutLogEvents .

This aligns with the scenario because the source code already attempts to write logs before saving data to DynamoDB. If the function is invoked and errors are visible in CloudWatch metrics, but logs never appear, then logging is failing before or during delivery to CloudWatch Logs. Assigning the proper IAM permissions resolves that problem directly.

Option B is incorrect because CloudWatch Lambda Insights provides enhanced monitoring and performance telemetry, but it does not replace the basic permissions required for CloudWatch Logs delivery. Option C is incorrect because AWS X-Ray helps trace requests and diagnose latency or service interactions, but it does not solve missing log stream permissions. Option D is incorrect because CloudWatch does not need to be added as a trusted principal to the Lambda execution role; the trusted entity for that role is the Lambda service .

Therefore, the developer should update the Lambda execution role to include the required CloudWatch Logs permissions.