CCNA Development with AWS Services Questions

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Option A is wrong because Lambda functions do not have a configurable retry count for SQS-triggered invocations; Lambda's built-in DLQ is for asynchronous invocations (e.g., from S3 or SNS), not for SQS event source mappings, and setting retry attempts on the function itself is not supported. Option B is wrong because setting reserved concurrency to 0 would prevent the Lambda function from executing at all, causing all messages to fail immediately, and the DLQ on the function is again irrelevant for SQS-triggered invocations. Option D is wrong because SNS topics are not used to retry or manage DLQ behavior for SQS-triggered Lambda functions; the retry and DLQ logic must be configured on the SQS queue itself, not via an SNS topic.

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Option A is wrong because AWS KMS is a service for creating and controlling encryption keys used to encrypt data at rest, not for managing SSL/TLS certificates. Option C is wrong because IAM is used for managing user identities and permissions, not for issuing or managing SSL/TLS certificates. Option D is wrong because AWS Secrets Manager is designed to securely store and rotate secrets like database credentials or API keys, not for managing SSL/TLS certificates.

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Option B (AWS CodeCommit) is wrong because it is a source control service for storing code, not a service for executing build or test commands. Option C (AWS CloudFormation) is wrong because it is an infrastructure-as-code service for provisioning AWS resources, not for running unit tests. Option D (AWS CodeDeploy) is wrong because it automates code deployment to compute services like EC2 or Lambda, but it does not execute unit tests; tests must be run before deployment.

Why these pairings

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Option B is wrong because increasing Lambda reserved concurrency only ensures more concurrent function executions, but it does not resolve the bottleneck at the DynamoDB layer; if the table is throttling, more concurrent invocations will only increase the number of throttled requests and worsen latency. Option C is wrong because moving the Lambda function into a VPC with a DynamoDB VPC endpoint reduces network latency and avoids NAT gateway costs, but it does not address the throughput capacity of the DynamoDB table itself; the primary latency issue is throttling, not network path. Option D is wrong because DynamoDB Accelerator (DAX) is an in-memory cache for read-heavy workloads and does not improve write performance; the Lambda function is storing metadata (write operations), so DAX would not reduce write latency.

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Option A is wrong because Amazon S3 with server-side encryption only encrypts objects at rest but does not provide automatic rotation of secrets or native secret management features. Option B is wrong because AWS KMS is a key management service that creates and controls encryption keys, not a service for storing application configuration data or rotating secrets directly. Option C is wrong because AWS Systems Manager Parameter Store can store configuration data securely with encryption via KMS, but it does not offer built-in automatic rotation of secrets; rotation requires custom implementation using AWS Lambda or other automation.

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Option A is wrong because KEYS_ONLY captures only the key attributes of the modified item, not the full old or new images, so the processor would lack the complete item data needed for downstream logic. Option C is wrong because NEW_IMAGE only captures the item's state after the update, omitting the previous state, which fails the requirement to receive both old and new images. Option D is wrong because OLD_IMAGE only captures the item's state before the update, omitting the new state, which also fails the requirement for both images.

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Option A is wrong because polling a CodeCommit repository every minute introduces unnecessary latency and cost, and AWS recommends event-driven triggers over polling for efficiency. Option B is wrong because CodeCommit does not support configuring webhooks directly; webhooks are typically used with third-party repositories like GitHub, not CodeCommit. Option D is wrong because an SNS topic cannot directly trigger a CodePipeline execution; SNS can send notifications but not invoke pipeline executions without a custom integration or Lambda function.

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Option B is wrong because AWS IAM is designed for signing AWS API requests with access keys and is not suitable for authenticating mobile app users against a REST API without modifying the backend code. Option C is wrong because Amazon API Gateway with a Lambda authorizer would require replacing the existing ALB and EC2 setup, which is not a direct integration with the current architecture. Option D is wrong because Amazon CloudFront with Lambda@Edge can perform authentication but requires custom code and does not natively integrate with an ALB to offload authentication without backend changes.

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Option A is wrong because Amazon Cognito User Pools authorizer is a proprietary solution that requires users to authenticate through Cognito, not a generic OIDC identity provider; it does not support arbitrary OIDC providers. Option B is wrong because a Custom Lambda authorizer involves writing and managing custom code to validate tokens, which contradicts the requirement to offload authentication and authorization to API Gateway. Option D is wrong because AWS IAM authorizer uses AWS Signature Version 4 for request signing and is designed for AWS credentials, not OIDC tokens from a third-party identity provider.

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Option A is wrong because increasing the session timeout would keep sessions in memory longer, worsening the memory pressure and potentially causing more timeouts and errors. Option C is wrong because reducing the number of EC2 instances behind the load balancer does not affect the Redis cluster's memory usage; it only reduces application capacity and could increase load on remaining instances. Option D is wrong because Memcached does not support replication, persistence, or advanced eviction policies like LRU, and migrating would not resolve the memory issue—it would only change the caching engine without addressing the root cause of memory exhaustion.

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Option A is wrong because resolvers are functions that map GraphQL operations (queries, mutations, subscriptions) to data sources like DynamoDB; they do not themselves provide real-time push capabilities. Option C is wrong because queries are request-response operations that fetch data on demand, not real-time updates. Option D is wrong because mutations are write operations that modify data; while they can trigger subscriptions, they are not the mechanism for delivering real-time updates to clients.

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Option B is wrong because AWS X-Ray is a distributed tracing service for analyzing and debugging request flows across microservices, not for viewing real-time log output from a single Lambda function. Option C is wrong because Amazon S3 is an object storage service and does not provide any capability for streaming or viewing real-time logs; it can store log files after they are generated but not display them live. Option D is wrong because AWS CloudTrail records API activity and management events for auditing, not the runtime logs or real-time output of a Lambda function's execution.

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Option B is wrong because it sets MaxAttempts to 5, which exceeds the required 3 retries, and uses a BackoffRate of 3, which is not the standard exponential backoff pattern requested. Option C is wrong because it sets MaxAttempts to 2, which is fewer than the required 3 retries. Option D is wrong because it retries on 'Lambda.ServiceException', which is a different error type; the actual error from a Lambda timeout is 'States.Timeout', not 'Lambda.ServiceException'.

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Option A is wrong because AWS CloudFormation Parameters are used to pass values into CloudFormation templates at stack creation or update time, not for runtime retrieval of feature flags with low latency and controlled rollout. Option B is wrong because AWS IAM Access Analyzer is a security tool that analyzes resource policies to identify unintended access, not a service for managing feature flags or application configuration. Option C is wrong because Amazon Inspector is a vulnerability management service that scans workloads for software vulnerabilities and network exposure, not a runtime configuration or feature flag service.

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Option B is wrong because increasing Lambda memory only increases CPU and network bandwidth, but does not change the DynamoDB API's 1 MB page size limit or the need to handle pagination; the query would still return only the first page. Option C is wrong because a global secondary index (GSI) with the same key would not solve the pagination issue; it would simply provide an alternative query path that also returns paginated results. Option D is wrong because strongly consistent reads ensure the most up-to-date data but do not affect the number of items returned per page or the pagination mechanism; they are unrelated to the pagination problem.

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Option B is wrong because if the security group did not allow inbound traffic from the ALB on the health check port, health checks would fail consistently, not intermittently. Option C is wrong because a misconfigured health check path returning a 404 would cause persistent health check failures, not intermittent ones. Option D is wrong because an incorrect target group protocol (e.g., HTTP vs HTTPS) would cause all health checks to fail consistently, not intermittently.

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Option A is wrong because SSEAlgorithm is not a valid parameter in the put_object call; the correct parameter is ServerSideEncryption, and 'AES256' is used for SSE-S3, not SSE-KMS. Option B is wrong because SSEKMSKeyId is an optional parameter used to specify a specific KMS key ID or alias, but it is not the parameter that enables SSE-KMS; you must first set ServerSideEncryption to 'aws:kms'. Option D is wrong because ServerSideEncryption: 'AES256' is the value for SSE-S3, not SSE-KMS; SSE-KMS requires 'aws:kms'.

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Option A is wrong because Amazon S3 is an object storage service, not optimized for frequently changing small configuration data; it lacks built-in validation, staged rollouts, and real-time deployment controls. Option B is wrong because AWS Systems Manager Parameter Store is designed for static or infrequently changing parameters (e.g., database passwords, AMI IDs) and has a throughput limit of 40 transactions per second per region by default, making it unsuitable for high-frequency updates. Option D is wrong because Amazon DynamoDB is a NoSQL database for high-scale transactional workloads, not a configuration management service; it requires additional code for validation, rollback, and deployment orchestration, adding unnecessary complexity.

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Option B is wrong because increasing read capacity to 10000 RCUs would double the provisioned throughput cost without addressing the root cause of latency, and it does not leverage caching to reduce response times. Option C is wrong because DynamoDB global tables replicate data across regions for disaster recovery and low-latency writes, not to improve read performance within a single region; they add complexity and cost without reducing read latency for GetItem calls. Option D is wrong because DynamoDB Streams capture item-level changes for asynchronous processing (e.g., triggers or replication), but they do not cache data or accelerate read operations; they are irrelevant to improving GetItem latency.

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Option A is wrong because CodePipeline does not poll SQS queues; it relies on event-driven triggers via CloudWatch Events or webhooks, not queue-based polling. Option C is wrong because using a periodic Lambda function to check for new files introduces unnecessary latency, cost, and complexity; it is an anti-pattern when a native event-driven trigger exists. Option D is wrong because CodePipeline cannot be directly subscribed to an SNS topic; SNS can send notifications but cannot invoke a pipeline—only CloudWatch Events/EventBridge can target CodePipeline directly.

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Option A is wrong because the Retry clause in a Lambda function's configuration (e.g., in the function's reserved concurrency or event source mapping) does not control Step Functions retries; Step Functions retries are defined in the state machine definition, not in the Lambda function itself. Option C is wrong because wrapping the Lambda invocation in a custom while loop within the function code would not integrate with Step Functions' retry mechanism and would violate the serverless orchestration pattern, as Step Functions manages retries at the state machine level. Option D is wrong because the Catch field is used to handle errors by redirecting to a different state (e.g., a fallback or error-handling state), not to implement retry logic; retries are handled exclusively by the Retry field.

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Option A is wrong because Amazon S3 with mountpoint-s3 is an object storage service that uses a custom FUSE-based mount, which is not POSIX-compliant (e.g., it does not support file locking, hard links, or atomic renames) and is designed for high-latency, throughput-oriented workloads rather than low-latency shared file system access. Option C is wrong because Amazon EBS with multi-attach enabled supports only up to 16 Nitro-based EC2 instances, not Fargate tasks, and requires the volume to be attached to instances in the same Availability Zone, making it unsuitable for a serverless container environment. Option D is wrong because Amazon FSx for Windows File Server uses the SMB protocol and is not POSIX-compliant; it is designed for Windows-based workloads and does not natively support Linux containers without additional translation layers.

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Option A is wrong because Amazon EFS is a file storage service, not a credential store; it cannot be used to store or provide access keys for IAM permissions. Option B is wrong because Fargate tasks do not use instance profiles; instance profiles are used with EC2 launch type to grant permissions to the underlying EC2 instance, not to the containers. Option D is wrong because storing AWS credentials in the container image violates security best practices, as credentials would be exposed in the image layers and cannot be rotated or scoped to least privilege.

Why this order

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Option B is wrong because usage plans with quota and throttle settings limit the rate or total number of requests, but they do not return previously computed responses for duplicate payloads; they simply reject or delay requests. Option C is wrong because request validation in API Gateway only checks the structure and presence of required headers or body fields, not the content or duplication of payloads; it cannot reject requests based on a client ID being repeated. Option D is wrong because reducing the Lambda batch size to 1 is irrelevant (Lambda functions process one event at a time by default) and implementing caching inside the function with ElastiCache would still invoke Lambda for every request, missing the goal of reducing invocations; API Gateway caching avoids Lambda invocation entirely for cached responses.

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Option A is wrong because Elastic Load Balancing handles traffic distribution but does not provide service discovery by logical names or built-in encryption for service-to-service communication, and AWS Systems Manager is for operational management, not service mesh. Option B is wrong because Amazon Route 53 can provide DNS-based service discovery, but AWS Direct Connect is a dedicated network connection to on-premises, not relevant for service-to-service encryption or discovery within ECS. Option C is wrong because AWS Lambda and Amazon API Gateway are for serverless API backends, not for managing inter-service communication and discovery in a microservices architecture on ECS.

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Option A is wrong because setting API Gateway throttling limits to a high value only controls the request rate at the API layer, not the Lambda concurrency; if Lambda concurrency limits are exceeded, requests will still be throttled with 429 errors regardless of API Gateway settings. Option B is wrong because using an SQS queue to buffer requests introduces asynchronous processing, which is unsuitable for a synchronous API Gateway integration that expects immediate responses; the queue would decouple the request-response cycle and cause timeouts or lost responses. Option D is wrong because increasing the Lambda function timeout to the maximum value (900 seconds) does not address concurrency limits or cold starts; it only allows the function to run longer, which does not prevent throttling errors when traffic spikes exceed the available concurrency.

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Option B is wrong because the Event invocation type is asynchronous; it queues the invocation and returns immediately with an HTTP status code of 202, without waiting for the function to execute or return a result. Option C is wrong because DryRun is not a valid Lambda invocation type; it is a parameter used with other AWS services (e.g., EC2) to test permissions without executing the action. Option D is wrong because 'None of the above' is incorrect since RequestResponse is a valid and correct invocation type for synchronous invocation.

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Option A is wrong because migrating to DynamoDB changes the database paradigm entirely, which is not a connection management solution and may not be feasible for existing relational workloads. Option B is wrong because increasing Lambda concurrency would actually worsen the problem by allowing more concurrent invocations to compete for the same limited pool of database connections. Option D is wrong because ElastiCache caches data, not database connections; it cannot pool or share TCP connections to RDS.

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Option A is wrong because DynamoDB global tables replicate data across regions for low-latency access and disaster recovery, not for automatic expiration of items. Option B is wrong because DynamoDB transactions provide ACID guarantees for multi-item operations, not scheduled deletion based on time. Option C is wrong because DynamoDB export to S3 is used for point-in-time backups or data lake integration, not for expiring items within the table.

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Option A is wrong because adding -Xmx1024m to the buildspec commands modifies the build process (a command-line change), which violates the constraint of not changing the source code; also, it only adjusts the JVM heap limit, not the underlying compute resources. Option B is wrong because changing the build image to a smaller one would reduce available memory, worsening the out-of-memory error. Option C is wrong because CodeBuild does not have a configurable 'memory parameter' in the build project settings; memory is tied directly to the compute type selection.

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Option A is wrong because S3 operations work across regions; a bucket in a different region does not cause an access denied error—it would instead result in a redirect or a different error. Option B is wrong because if SSE-KMS were used, the function would need kms:GenerateDataKey or kms:Encrypt, not kms:Decrypt, and the error would typically be a 403 Forbidden with a KMS-specific message, not a generic access denied. Option C is wrong because the question states the IAM role has an inline policy allowing s3:PutObject, so the execution role is correctly assigned; the error is not due to a missing role but due to a conflicting bucket policy.

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Option A is wrong because DynamoDB Streams capture a time-ordered sequence of item-level changes in a table, but they do not provide atomicity or transactional coordination across multiple tables. Option C is wrong because DynamoDB Accelerator (DAX) is an in-memory caching layer that improves read performance but does not offer transactional write capabilities. Option D is wrong because DynamoDB Global Tables provide multi-region replication for disaster recovery and low-latency reads, but they do not enable atomic multi-table transactions within a single region.

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Option B is wrong because 1 RCU per item is not a fixed rule; RCU consumption depends on item size (1 RCU = one strongly consistent read of up to 4 KB per second) and eventually consistent reads consume 0.5 RCUs, not a fixed 1 RCU per item. Option C is wrong because strongly consistent reads and eventually consistent reads do not consume the same RCUs; eventually consistent reads use half the RCUs (0.5 RCU per 4 KB item) compared to strongly consistent reads (1 RCU per 4 KB item). Option D is wrong because provisioning for peak traffic with DAX adds cost and complexity; DAX is a caching layer that reduces read load but incurs additional charges, contradicting the goal to minimize costs.

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Option A is wrong because Amazon EFS is a file storage service for EC2 instances, not an event-driven communication service; it cannot route events or support producer-to-multiple-target patterns. Option B is wrong because AWS CloudHSM provides hardware security modules for cryptographic key storage, not event routing or asynchronous messaging. Option D is wrong because AWS DataSync is a data transfer service for moving large datasets between on-premises and AWS storage, not for event-driven, loosely coupled communication with rules.

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Option A is wrong because Amazon CloudWatch Events (now Amazon EventBridge) is used for scheduling or reacting to AWS service events via a rule, but it is not the direct trigger for S3 object creation; you would need S3 to send events to EventBridge, which adds unnecessary complexity. Option B is wrong because Amazon SQS is a message queue that can receive S3 notifications, but it cannot directly invoke a Lambda function; you would need an additional SQS trigger on the Lambda, making it an indirect and less efficient solution. Option C is wrong because Amazon SNS is a pub/sub messaging service that can receive S3 notifications and fan out to subscribers, but it cannot directly invoke Lambda; you would need to subscribe Lambda to the SNS topic, which is an extra hop and not the native integration.

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Option B is wrong because using an SQS queue as a buffer between API Gateway and Lambda would introduce asynchronous processing, which is not suitable for a REST endpoint that expects synchronous responses; the client would not receive a timely response, and 5xx errors would persist. Option C is wrong because increasing Lambda memory may reduce duration but does not address DynamoDB throttling under high traffic, and DynamoDB auto-scaling reacts too slowly to sudden spikes, so errors would still occur. Option D is wrong because switching to HTTP API and enabling API Gateway caching only improves read performance for cached responses, not for write operations to DynamoDB, and does not address the Lambda duration spikes or database throttling.

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Option A is wrong because a delete marker is created only when deleting the latest version of an object without specifying a version ID, not when attempting to permanently delete a specific version with MFA Delete enabled. Option B is wrong because permanent deletion of a specific version requires MFA authentication when MFA Delete is enabled; without it, the operation fails. Option D is wrong because marking an object version with a delete marker is not a valid S3 operation; delete markers are only applied to the current version of an object, not to specific versions.

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Option B is wrong because Amazon S3 is an object storage service designed for durable, long-term storage of static assets (e.g., images, backups), not for low-latency, in-memory session state; its read/write latency and lack of native TTL or atomic operations make it unsuitable for session management. Option C is wrong because Amazon DynamoDB is a fully managed NoSQL database that can store session data, but it is not an in-memory data store—it uses SSD-backed storage and has higher latency than an in-memory cache, and while it supports TTL, it is not optimized for the sub-millisecond access patterns required for session state in a high-traffic web app. Option D is wrong because Amazon RDS for MySQL is a relational database that stores data on disk, introducing significant latency for session reads/writes and requiring schema management; it is not designed for ephemeral, high-throughput session state and would create unnecessary overhead and performance bottlenecks.

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Option A is wrong because Lambda does not reject messages due to concurrency limits; messages remain in the queue and are retried, and a dead-letter queue is only used after the maximum retry count is exceeded. Option B is wrong because Lambda cannot scale beyond the reserved concurrency of 10, which is a hard limit set by the user, not an automatic scaling target. Option C is wrong because reserved concurrency is a static configuration that cannot be dynamically increased by Lambda in response to a burst; it must be changed manually or via an auto-scaling mechanism like Application Auto Scaling.

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Option A is wrong because the MemoryReservation metric is specific to Amazon ECS and Fargate tasks, not to EC2 instances managed by Elastic Beanstalk, and step scaling policies are not the recommended approach for CPU-based scaling in this context. Option B is wrong because while CloudFront can reduce latency by caching responses at edge locations, it does not automatically scale the application's compute capacity based on CPU utilization; it only offloads requests for cached content. Option D is wrong because a target tracking policy based on NetworkIn metric would scale based on network traffic rather than CPU utilization, which does not meet the developer's explicit requirement to scale based on CPU utilization.

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Option B (Lambda custom integration) is wrong because it requires you to explicitly define request/response mapping templates for each HTTP method, increasing configuration complexity and defeating the goal of minimizing code and configuration. Option C (HTTP integration) is wrong because it proxies requests to an HTTP endpoint, not to a Lambda function, so it cannot directly invoke the same Lambda for multiple methods without an intermediate HTTP service. Option D (Mock integration) is wrong because it returns a static response defined in API Gateway without invoking any backend, so it cannot handle dynamic business logic for different HTTP methods.

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Option A is wrong because Amazon Kinesis Data Firehose is a real-time data streaming service designed to load data into destinations like S3, Redshift, or Elasticsearch, not a native log storage and retention service; it lacks built-in log retention policies and is not optimized for storing logs directly for 90 days. Option C is wrong because AWS CloudTrail records API activity and governance events across AWS services, not application-level logs from EC2 instances; it is focused on auditing, not application log collection. Option D is wrong because Amazon S3 with S3 Server Access Logs captures detailed records about requests made to an S3 bucket, not application logs from EC2 instances; it is a bucket-level logging feature, not a centralized log collection service for EC2.

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Option A is wrong because it includes 's3:*' on the bucket, which grants all S3 actions (e.g., delete, put) far beyond the required 's3:GetObject', violating least privilege. Option B is wrong because it includes 'logs:*' (all CloudWatch Logs actions) and 's3:*' on the bucket, both overly permissive and not minimal. Option C is wrong because it omits 'logs:CreateLogGroup' and 'logs:CreateLogStream', which are required for the Lambda runtime to initialize logging, and includes 's3:ListBucket' instead of the necessary 's3:GetObject' for reading objects.

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Option B is wrong because Amazon CloudFront as a custom domain is a content delivery network (CDN) that can cache content at edge locations, but it is not an API Gateway feature; it is a separate service that would be placed in front of API Gateway, not enabled within API Gateway itself. Option C is wrong because Lambda@Edge is used for customizing CloudFront behavior (e.g., modifying requests/responses) and is not a caching mechanism; it runs code at edge locations but does not provide built-in response caching like API Gateway caching. Option D is wrong because S3 Transfer Acceleration is designed to speed up uploads to S3 over long distances using AWS edge locations, but it does not cache responses or reduce latency for GET requests served through API Gateway.

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Option A is wrong because a NAT Gateway provides outbound internet access for private subnets but does not manage or secure database connections; it would not help with connection pooling or credential management. Option C is wrong because a VPC Endpoint (Gateway or Interface) enables private connectivity to AWS services like S3 or DynamoDB, not to RDS databases; it does not handle connection pooling or authentication for MySQL. Option D is wrong because AWS PrivateLink is used to expose services privately across VPCs or accounts via Network Load Balancers and interface endpoints, but it does not provide the connection pooling, IAM integration, or failover capabilities that RDS Proxy offers for Lambda-to-RDS connections.

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Option A is wrong because Amazon Kinesis Data Firehose streams logs to S3, but then sending them to CloudWatch Logs requires an additional Lambda or subscription filter, adding unnecessary complexity and cost; the goal is minimal overhead, not a multi-hop pipeline. Option B is wrong because modifying application code to use the AWS SDK for CloudWatch Logs tightly couples the application to AWS APIs, increases development effort, and violates the principle of keeping logging infrastructure separate from application logic. Option C is wrong because installing the CloudWatch agent inside a Fargate container is not supported—Fargate does not allow running sidecar agents that require host-level access; the awslogs driver handles this at the container runtime level without any agent.

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Option A is wrong because Amazon SNS fans out messages to multiple subscribers asynchronously and does not guarantee any ordering; messages can arrive at Lambda in a different order than they were published. Option B is wrong because Amazon Kinesis Data Streams provides ordering within a shard but does not guarantee global ordering across shards, and it is designed for real-time streaming analytics, not for simple message queue processing with strict FIFO semantics. Option C is wrong because an SQS standard queue does not preserve message order; it uses best-effort ordering and can deliver messages out of sequence, even with a batch size of 10, making it unsuitable for ordered processing.

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Option A is wrong because reserved concurrency limits the number of concurrent Lambda executions but does not cause duplicate message processing; it may actually throttle invocations. Option C is wrong because a dead-letter queue is used to capture messages that fail processing after a maximum number of retries, not to cause duplicate processing. Option D is wrong because setting the batch size to more than 1 allows Lambda to process multiple messages in a single invocation, which reduces the chance of duplicates by processing them together, not causing duplicates.

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Option B is wrong because increasing reserved concurrency to 10 would increase throughput but directly violates the constraint of not increasing reserved concurrency, and it would also increase the risk of throttling other functions sharing the account concurrency limit. Option C is wrong because reducing the batch size to 1 would drastically decrease throughput, as each invocation would process only one message, requiring more invocations to handle the same backlog and potentially increasing throttling due to more concurrent executions. Option D is wrong because enabling long polling for the SQS queue reduces the number of empty responses and improves efficiency in message retrieval, but it does not increase the number of messages processed per invocation or reduce throttling caused by the Lambda function's concurrency limit.

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Option B is wrong because using customer ID as the partition key would cause all orders for the same customer to be stored in the same partition, leading to hot partitions and potential throttling, and it does not provide efficient retrieval by order ID (which would require a scan or a query with a known customer ID). Option C is wrong because scanning the entire table to find orders by customer ID is extremely inefficient and costly, as it reads every item in the table and incurs read capacity for all items, even those not matching the query. Option D is wrong because a Local Secondary Index (LSI) requires the same partition key as the base table (customer ID), which would still cause hot partitions for high-volume customers, and LSIs share the base table's read/write capacity, so they do not provide the same cost flexibility as a GSI.

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Option A is wrong because creating a separate database per microservice on a single RDS instance does not prevent a microservice from connecting to another microservice's database if it has the same database user credentials or network access; it only provides logical separation, not access control. Option C is wrong because security groups control network-layer access to the RDS instance as a whole, not to individual tables or databases within it; once a microservice can connect to the RDS endpoint, it can access any table unless further database-level permissions are enforced. Option D is wrong because Amazon RDS Proxy manages connection pooling and provides some IAM authentication support, but it does not enforce table-level access control; it still relies on the underlying database user permissions for authorization.

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Option B is wrong because reserved concurrency of one limits the Lambda function to a single concurrent execution, which does not affect how individual messages within a batch are handled; it only throttles overall throughput. Option C is wrong because maximum message size increase is a queue-level setting in SQS that controls the maximum payload size (up to 256 KB for standard queues), not a mechanism for handling partial batch failures. Option D is wrong because SQS short polling returns immediately with available messages but does not provide any per-message failure handling within a batch; it only affects message retrieval latency.

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Option A is wrong because the task execution role is used to pull container images and write logs, not to retrieve secrets; the task role (which has the policy shown) is used for application-level API calls like GetSecretValue. Option C is wrong because while a VPC endpoint can improve network connectivity, it is not required for Fargate tasks to reach Secrets Manager over the public internet or via NAT gateway; the error is an AccessDeniedException, not a network timeout. Option D is wrong because the secret name matches the pattern `prod-db-*` in the policy; the error is an access denial, not a resource mismatch.

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Option A is wrong because the log group is automatically created by the Lambda service on the first invocation if the execution role has the required permissions; its absence is a symptom, not the root cause. Option C is wrong because if the function were failing before any logging code, the Lambda runtime itself would still attempt to write execution logs (e.g., START, END, REPORT messages) to CloudWatch, which would produce stored bytes. Option D is wrong because the log group name is predetermined by the Lambda service (e.g., /aws/lambda/<function-name>) and cannot be changed by the developer; a different log group name would not prevent logs from being written to the default group.

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Option A is wrong because granting full administrative access (e.g., dynamodb:* on all resources) violates least privilege and could allow unintended actions like deleting tables. Option B is wrong because storing AWS access keys and secret access keys as environment variables exposes long-term credentials in plaintext, increasing the risk of leakage through logs or function output. Option D is wrong because creating an IAM user and embedding its credentials in the function requires managing long-term keys, which is less secure than using an execution role that automatically rotates temporary credentials.

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Option A is wrong because 5 is the maximum number of messages that can be sent in a single SendMessageBatch API call, not received. Option B is wrong because 100 is the maximum number of messages that can be sent or received in a single batch for Amazon SNS or Kinesis, but SQS limits ReceiveMessage to 10. Option C is wrong because 20 is the maximum batch size for AWS Lambda event source mappings when polling an SQS queue, not the limit for a single ReceiveMessage API call.

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Option A is wrong because reserved concurrency limits the maximum number of concurrent Lambda executions, but timeout errors are not caused by concurrency limits—they occur when the function execution exceeds its configured timeout. Option B is wrong because Lambda throttling occurs when the number of concurrent invocations exceeds the account or function concurrency limit, not from too many messages in the queue; throttling results in invocation failures (e.g., 429 errors), not timeout errors within the function. Option D is wrong because a 30-second visibility timeout is not too long; in fact, it is too short relative to the Lambda timeout, causing premature message reappearance—a longer visibility timeout would help prevent the issue.

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Option A is wrong because a timeout would produce a 'Task timed out' error in the logs, not an 'AccessDeniedException'. Option B is wrong because throttling from DynamoDB would result in 'ProvisionedThroughputExceededException' errors, not 'AccessDeniedException'. Option D is wrong because a request payload size issue would cause a '413 Request Entity Too Large' error from API Gateway, not a 502 Bad Gateway, and the Lambda logs would not show an 'AccessDeniedException'.

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Option A is wrong because AWS KMS generates data keys for encryption, not for storing secrets, and storing a data key in an environment variable does not provide automatic rotation or secure secret management. Option C is wrong because AWS Systems Manager Parameter Store can store passwords but does not natively support automatic rotation of secrets; it requires custom solutions or integration with Secrets Manager for rotation. Option D is wrong because storing an encrypted password in DynamoDB adds unnecessary complexity, does not provide automatic rotation, and requires custom encryption/decryption logic, whereas Secrets Manager handles both securely.

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Option A is wrong because disabling caching would force every request to go to the origin S3 bucket, increasing load and degrading performance, which is the opposite of the desired outcome. Option B is wrong because Lambda@Edge is used for custom logic at edge locations (e.g., authentication, header manipulation) and does not directly reduce origin load or improve caching for static, rarely changing content. Option C is wrong because decreasing the TTL causes CloudFront to re-validate content with the origin more often, increasing origin requests and latency, which contradicts the goal of reducing load and improving performance.

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Option B is wrong because request validation in API Gateway checks for required headers, query strings, or body structure, but it does not detect or reject duplicate requests based on content or client ID. Option C is wrong because a usage plan with throttling limits the rate of requests per client (e.g., requests per second), but it does not cache responses or prevent Lambda invocation for duplicate requests within a time window; it simply rejects excess requests. Option D is wrong because stage variables are used to pass configuration values (like endpoint URLs) to integration functions at deployment time, not to store or return previous responses.

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Option A is wrong because suffix filters only control which objects trigger notifications based on file extension; they do not prevent duplicate invocations for the same object. Option B is wrong because while an SQS queue can buffer events and reduce throttling, it does not eliminate duplicate events—S3 still sends at-least-once notifications to SQS, so duplicates can still occur. Option D is wrong because increasing the Lambda timeout only allows the function to run longer; it does not address the root cause of duplicate invocations or provide idempotency.

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Option B is wrong because deploying two separate SAM templates duplicates infrastructure code and increases maintenance overhead, which is less efficient than using a single parameterized template. Option C is wrong because using a custom resource to toggle caching after deployment adds unnecessary complexity and latency, and SAM already supports conditional resource properties natively. Option D is wrong because enabling caching globally and using a usage plan with a quota does not disable caching for production; usage plans control throttling and API keys, not the API Gateway cache behavior, and caching would still be active in production, violating the requirement for data freshness.

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Option A is wrong because increasing memory allocates more CPU and network bandwidth, but it does not resolve network connectivity issues like VPC misconfiguration. Option B is wrong because reserved concurrency limits the number of concurrent executions but does not affect individual function connection timeouts. Option D is wrong because IAM permissions control authorization to perform RDS API actions (e.g., creating snapshots), not network-level connectivity to the database; connection timeouts are a network issue, not an authorization issue.