CCNA New Solutions Questions

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Option A is wrong because a Kinesis stream cannot directly send records to an SQS queue; the dead-letter handling must be configured on the event source mapping, not on the stream itself. Option C is wrong because the Lambda function's dead-letter queue property (DLQ) is only applicable for asynchronous invocations, not for stream-based event source mappings like Kinesis. Option D is wrong because Lambda destinations are used for routing invocation results (success/failure) to other services, but they do not provide the automatic retry and dead-letter queue behavior for failed records from a Kinesis stream; the on-failure destination on the event source mapping is the correct mechanism.

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Option A is wrong because the condition does not check the request region in a vacuum; it is part of a Deny statement that explicitly blocks actions when the region is not us-east-1, so the user cannot stop the instance due to the deny, not because of a generic region check. Option C is wrong because while the first statement allows ec2:StopInstances, the second statement's explicit deny overrides that allow, making the action impossible. Option D is wrong because the condition applies to the Deny statement, and the Allow statement is unconditional, but in IAM, an explicit deny always overrides an allow, so the unconditional allow does not help when a deny is triggered.

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Option A is wrong because Amazon ECS Service Discovery is not a standalone service; it is a feature of AWS Cloud Map that is exposed through Amazon ECS, and it lacks the broader API-based discovery and health-check integration that Cloud Map provides. Option B is wrong because an Application Load Balancer (ALB) is a Layer 7 load balancer that distributes traffic to targets, not a service registry for dynamic service-to-service discovery; it does not support service registration or DNS-based discovery for internal microservices. Option D is wrong because Amazon Route 53 private hosted zones provide DNS resolution within a VPC but do not support dynamic service registration, health checking, or API-based discovery; they are a static DNS solution, not a service discovery registry.

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Option A is wrong because Amazon S3 is an object storage service that does not support real-time analysis; it is typically used for archival or batch processing, not for streaming log collection and live querying. Option B is wrong because Amazon Kinesis Data Firehose is a streaming data delivery service that requires additional downstream services (like Amazon Elasticsearch Service or S3) for analysis and storage, adding complexity and cost for a simple log collection use case. Option C is wrong because Amazon Elasticsearch Service (now Amazon OpenSearch Service) is a search and analytics engine that can ingest logs but requires separate log collection agents and additional configuration for real-time analysis, making it less cost-effective and more complex than CloudWatch Logs for this scenario.

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Option B is wrong because Amazon RDS for MySQL with Multi-AZ and cross-Region read replicas uses asynchronous replication for cross-Region replicas, which can introduce replication lag exceeding 5 seconds, and Multi-AZ only provides automated failover within a single Region, not across Regions. Option C is wrong because Amazon RDS for PostgreSQL with cross-Region read replicas and Multi-AZ also relies on asynchronous replication for cross-Region copies, which cannot guarantee an RPO of less than 5 seconds, and Multi-AZ failover is limited to the same Region. Option D is wrong because Amazon DynamoDB Global Tables is a NoSQL database, not a relational database, and the question explicitly requires a relational database solution.

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Option A is wrong because a 500 Internal Server Error would produce a 5xx status, not a 302 redirect. Option B is wrong because a timeout would result in a 504 Gateway Timeout or no response, not a 302 redirect. Option C is wrong because if the security group were blocking health check traffic, the ALB would receive no response (connection timeout or refused), not a 302 redirect.

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Option A is wrong because Multi-AZ is a high-availability feature within a single Region, not a disaster recovery strategy; taking frequent snapshots to another Region cannot achieve an RPO of 15 minutes due to snapshot scheduling overhead and recovery time. Option B is wrong because AWS DMS for continuous replication adds significant cost for a replication instance and ongoing data transfer, and it is not the lowest-cost solution for the given RPO/RTO. Option C is wrong because Amazon Aurora Global Database, while offering very low RPO (~1 second), is more expensive than a cross-Region read replica for MySQL and exceeds the required RPO of 15 minutes, making it over-engineered and not the lowest-cost option.

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Option B (Amazon RDS for MySQL) is wrong because relational databases introduce unnecessary latency and overhead for session state, which is ephemeral and requires fast reads/writes; RDS is optimized for persistent, ACID-compliant transactional data, not high-throughput key-value access. Option C (Amazon S3) is wrong because S3 is an object store with eventual consistency (unless using S3 Select or additional features) and higher latency (tens to hundreds of milliseconds), making it unsuitable for real-time session retrieval. Option D (Amazon DynamoDB) is wrong because while DynamoDB offers single-digit millisecond latency and is highly available, it is a NoSQL database with disk-based storage and higher per-request cost compared to an in-memory cache like Redis; for session state, which is transient and benefits from TTL-based expiration, Redis's in-memory model is more cost-effective and performant.

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Option A is wrong because AWS Step Functions is a workflow orchestration service, not an event router; it would require custom logic to fan out events and does not natively support independent, reliable delivery to multiple downstream services. Option B is wrong because Amazon Kinesis Data Streams is designed for real-time streaming data ingestion and processing with shard-level ordering, but it does not natively fan out events to multiple independent consumers—each consumer must share the same stream and manage its own checkpointing, which can lead to contention and does not guarantee independent processing of every event. Option C is wrong because Amazon SQS is a message queue that delivers each message to a single consumer; to fan out to multiple downstream services, you would need multiple queues and a publisher to send copies, which adds complexity and does not provide built-in event filtering or transformation.

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Option B is wrong because S3 bucket policies operate at the object or prefix level, not at the column level, so they cannot restrict access to specific columns within a Parquet file. Option C is wrong because it introduces unnecessary complexity and administrative overhead by requiring a separate Redshift cluster and Redshift Spectrum setup, whereas Lake Formation directly integrates with Athena and the Glue Data Catalog. Option D is wrong because IAM condition keys for Athena workgroups can limit which workgroup a user can use, but they cannot enforce column-level restrictions on the query results.

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Option A is wrong because Amazon Route 53 with an alias record only provides DNS resolution, not HTTPS termination; it cannot serve content over HTTPS directly. Option C is wrong because Amazon S3 static website hosting does not support custom SSL certificates or HTTPS for custom domains; it only provides HTTPS via the S3 website endpoint (which uses a domain like s3-website-<region>.amazonaws.com) and cannot bind a custom SSL certificate. Option D is wrong because an Elastic Load Balancer with SSL termination is designed for dynamic content behind EC2 or containers, not for static S3-hosted websites, and it adds unnecessary complexity and cost without leveraging S3's native static hosting benefits.

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Option A is wrong because Elastic Load Balancing distributes traffic only within a single AWS Region and does not provide global edge caching or native DDoS protection at the application layer; it relies on other services for global latency reduction. Option B is wrong because AWS Global Accelerator improves latency by routing traffic over the AWS global network to the optimal regional endpoint, but it does not cache content or provide application-layer DDoS protection; it focuses on TCP/UDP traffic optimization and uses static anycast IPs. Option D is wrong because Amazon Route 53 is a DNS service that resolves domain names to IP addresses and can perform health checks and routing policies, but it does not cache content or provide DDoS mitigation beyond basic DNS-level protection; it is not an entry point for application traffic.

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Option A is wrong because AWS Lambda has a maximum execution timeout of 15 minutes, which cannot accommodate transcoding jobs that may exceed this limit, especially for high-resolution or long-duration videos. Option C is wrong because provisioning a cluster of EC2 instances with Auto Scaling based on SQS queue depth incurs significant idle costs during low-activity periods and requires ongoing operational overhead for patching and scaling, making it less cost-effective than a fully managed service. Option D is wrong because Amazon Elastic Transcoder is a legacy service that is being phased out in favor of Elemental MediaConvert; it lacks advanced features like per-title encoding, Dolby Vision, and HDR10+ support, and its pricing model is generally less flexible for sporadic workloads.

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Option A is wrong because Route 53 private hosted zones with health checks are designed for DNS resolution and health monitoring of static resources, not for dynamic service discovery where service endpoints change frequently due to scaling or restarts. Option C is wrong because VPC peering connects entire VPCs, not individual services, and does not provide service discovery; it is a network connectivity mechanism, not a discovery mechanism. Option D is wrong because an Application Load Balancer with path-based routing is used for traffic distribution and routing to backend targets, not for service discovery; it does not provide a registry or DNS-based resolution for individual service instances.

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Option A is wrong because Amazon Redshift is a data warehouse optimized for analytical queries on large datasets, not a transactional relational database for a web application, and concurrency scaling adds cost without addressing unpredictable traffic for OLTP workloads. Option C is wrong because Amazon RDS for MySQL with Single-AZ deployment lacks high availability—it does not provide automatic failover to a standby instance in another Availability Zone, which is required for the stated goal. Option D is wrong because Amazon DynamoDB is a NoSQL key-value and document database, not a relational database, and while on-demand capacity handles unpredictable traffic, it does not support SQL queries or relational data models needed for a web application with a relational database requirement.

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Option A is wrong because Amazon S3 Glacier Deep Archive has retrieval times of 12-48 hours, which fails the requirement for immediate access when needed for Athena and SageMaker. Option C is wrong because Amazon S3 Standard is designed for frequently accessed data and would be more expensive for infrequently accessed data, not minimizing storage costs. Option D is wrong because Amazon S3 One Zone-Infrequent Access stores data in a single Availability Zone, which risks data loss if that AZ fails, and it lacks automatic cost optimization for varying access patterns.

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Option A is wrong because AWS CodeDeploy is a deployment service that automates code deployments to compute services like ECS, EC2, or Lambda, but it does not build or test Docker images. Option C is wrong because Amazon ECR is a container image registry for storing, managing, and deploying Docker images; it does not perform build or test operations. Option D is wrong because AWS CodeCommit is a source control service for hosting Git repositories; it does not build or test code.

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Option B is wrong because DynamoDB global tables do not support strong consistency reads; the global tables feature is designed for eventual consistency only, and enabling strong consistency in the configuration is not possible. Option C is wrong because DynamoDB Streams provide an ordered stream of item-level changes but do not offer strong consistency reads across regions; they can be used for cross-region replication but still result in eventual consistency due to asynchronous processing. Option D is wrong because DynamoDB Accelerator (DAX) is an in-memory cache that can provide strongly consistent reads for a single-region DynamoDB table, but it does not extend strong consistency to global tables or across multiple regions.

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Option A is wrong because the key policy does grant `kms:Decrypt` to the root account (AWS account root user), and the IAM role MyAppRole inherits permissions from the root account via IAM policies, so decryption via S3 would succeed. Option B is wrong because the key policy does not directly grant `kms:Decrypt` to MyAppRole; it only grants it to the root account with a condition on encryption context, and the role's permissions depend on IAM policies, not the key policy alone. Option D is wrong because the encryption context condition in the key policy only applies to the root account's decrypt permission; if the application calls KMS Decrypt directly, the encryption context must match, but the fundamental issue is the lack of explicit permission for the role, not just the context.

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Option B is wrong because Amazon S3 is an object storage service designed for high throughput and large data volumes, not for low-latency single-digit millisecond access typical of microservices stateful data; its read/write latency is higher and it lacks native support for fine-grained updates. Option C is wrong because Amazon RDS with Multi-AZ provides high availability through synchronous replication to a standby instance, but it is a relational database that introduces overhead from SQL parsing and connection management, making it less suitable for the sub-10ms latency requirements of microservices compared to DynamoDB. Option D is wrong because Amazon ElastiCache for Redis is an in-memory cache, not a durable primary data store; while it offers low latency, its data is not durable by default (unless using Redis AOF with persistence, which still risks data loss on failure) and it is not designed for long-term stateful storage with the same durability guarantees as DynamoDB.

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Option A is wrong because a single Network Load Balancer (NLB) in one primary region cannot distribute traffic across multiple AWS Regions; cross-zone load balancing only distributes traffic within a single region's Availability Zones, not across regions. Option C is wrong because Amazon CloudFront with origins in each region is a content delivery network (CDN) optimized for caching static content, not for routing dynamic, stateless web application traffic with low latency across regions; it adds unnecessary caching complexity and does not provide regional load balancing for dynamic requests. Option D is wrong because AWS Global Accelerator uses Anycast IPs to route traffic to the nearest regional endpoint, but it requires Network Load Balancers or Elastic IPs as endpoints, not Application Load Balancers, and while it improves latency, it does not natively support latency-based routing to multiple regions as precisely as Route 53 latency-based routing; additionally, Global Accelerator is better suited for TCP/UDP traffic and non-HTTP protocols, whereas the question specifies a stateless web application (HTTP/HTTPS) where ALB is more appropriate.

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Option A is wrong because Amazon EBS volumes are block-level storage attached to a single EC2 instance, not designed for storing and retrieving user-uploaded images at scale, and they incur costs even when not accessed, lacking lifecycle management for infrequent access. Option C is wrong because Amazon S3 Glacier Flexible Retrieval has retrieval times of minutes to hours, which does not provide low-latency access for frequently accessed images during the first 30 days. Option D is wrong because storing all images in Amazon S3 Standard incurs higher storage costs for data that becomes rarely accessed after 30 days, failing to minimize storage costs as required.

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Option A is wrong because DynamoDB Accelerator (DAX) is an in-memory cache for read operations only; it does not cache or accelerate write operations, so it cannot prevent write throttling. Option C is wrong because a global secondary index (GSI) is a secondary index that supports read and write operations, but it does not replace the primary index for writes; writes are still directed to the base table's partition key, and using a GSI as the primary index is not a valid design—GSIs have their own throughput and can also experience throttling if not properly provisioned. Option D is wrong because a local secondary index (LSI) shares the same partition key as the base table and does not improve write distribution; it only provides an alternative sort key for querying within a partition, so it does not address hot partition issues.

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Option A is wrong because enabling STRICT_TRANS_TABLES SQL mode only enforces data type and constraint validation within a single RDS instance, not across multiple databases, and does not address cross-service consistency. Option C is wrong because RDS read replicas provide eventually consistent reads for a single database, not consistency across separate RDS instances per microservice, and they do not handle write conflicts or distributed transactions. Option D is wrong because two-phase commit (2PC) across RDS instances introduces tight coupling, performance overhead, and scalability issues, and is not recommended for microservices architectures due to its blocking nature and lack of support across separate RDS endpoints.

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Option A is wrong because Amazon API Gateway is a fully managed API gateway for creating, publishing, and securing REST/HTTP/WebSocket APIs at the edge, not a service mesh for internal service-to-service communication within a microservices architecture. Option C is wrong because AWS Transit Gateway is a network transit hub that connects VPCs and on-premises networks via a central router, operating at Layer 3, not at the application layer required for service mesh capabilities like traffic splitting and observability. Option D is wrong because AWS Cloud Map is a cloud resource discovery service that allows services to register and discover instances via DNS or API calls, but it does not provide traffic management, observability, or security features inherent to a service mesh.

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Option B is wrong because a COGNITO_USER_POOLS authorizer alone requires users to authenticate directly against a Cognito user pool, not a third-party IdP; it does not support federated OIDC without additional configuration. Option C is wrong because a Lambda authorizer requires custom code to validate the OIDC token, which adds complexity and maintenance overhead, contradicting the goal to offload authentication to API Gateway. Option D is wrong because an IAM authorizer uses AWS Signature Version 4 for signing requests, which is incompatible with OIDC tokens from a third-party IdP; it is designed for AWS IAM-based access control, not federated identity.

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Option A is wrong because VPC Peering does not provide internet access; it only connects two VPCs privately, and neither VPC inherently has internet access without an Internet Gateway. Option C is wrong because routing a private subnet directly to an Internet Gateway would allow inbound internet traffic, violating the security model of a private subnet; Internet Gateways require public IP addresses and are used with public subnets. Option D is wrong because assigning public IP addresses to instances in a private subnet does not grant internet access; the subnet still lacks a route to an Internet Gateway, and public IPs alone cannot reach the internet without a gateway.

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Option A is wrong because Amazon DynamoDB, while highly available and scalable, is a NoSQL database designed for document and key-value storage with higher latency compared to in-memory caches; it is not optimized for the low-latency, high-throughput session state access patterns required by web applications, and its cost per operation is typically higher than ElastiCache for this use case. Option B is wrong because storing session state on the local instance store of each EC2 instance ties session data to a specific instance; if the instance is terminated, replaced, or scaled in, all session data is lost, breaking high availability and user experience. Option C is wrong because Amazon S3 is an object storage service with significantly higher latency (typically tens to hundreds of milliseconds) compared to in-memory stores, and it is not designed for frequent, low-latency read/write operations required for session management; additionally, S3 lacks native session expiration and atomic operations needed for session handling.

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Option A is wrong because SSE-S3 uses Amazon S3 managed keys, which are not managed by the customer's own HSM, and lifecycle policies cannot re-encrypt objects with new keys — they only manage object transitions or deletions. Option B is wrong because SSE-KMS with a customer managed key uses AWS KMS, not the customer's own HSM; automatic key rotation in KMS is fixed at yearly (365 days), not 90 days, and cannot be configured to a custom interval. Option D is wrong because SSE-C requires the customer to provide the encryption key with each PUT request, but the keys are not stored or managed by the customer's own HSM — they are ephemeral and must be supplied per operation, and rotating keys every 90 days would require re-encrypting all objects, which is impractical and not a managed rotation solution.

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Option A is wrong because Amazon DynamoDB is a NoSQL key-value and document database that, while fully managed and scalable, is not optimized for session state management due to higher latency compared to in-memory caches and lack of native TTL (time-to-live) expiration for session data without additional configuration. Option C is wrong because Amazon S3 is an object storage service designed for large-scale data blobs, not for low-latency, high-throughput session state access; it lacks in-memory performance and introduces significant latency and cost overhead for frequent read/write operations. Option D is wrong because Amazon RDS for MySQL is a relational database that, while fully managed, is not designed for high-speed session state operations; it incurs higher latency due to disk-based storage and ACID transaction overhead, and it is not optimized for the ephemeral, high-frequency access patterns of session data.

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Option A is wrong because YAML output requires explicitly setting `--output yaml` in the command, which is not present; the default output format is JSON. Option B is wrong because a text table with columns is produced only with `--output table`, which is not specified, and the `--query` extracts a flat list of IDs, not structured columns. Option D is wrong because a JSON object with keys would result from a query that returns a dictionary (e.g., `{InstanceId: ...}`), but the given JMESPath expression `Reservations[].Instances[].InstanceId` returns an array of scalar values (strings), not key-value pairs.

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Option A is wrong because Amazon DynamoDB is a NoSQL key-value and document database, not a relational MySQL database, and migrating a MySQL-based monolithic application to DynamoDB would require significant application redesign and schema changes. Option C is wrong because Amazon Redshift is a petabyte-scale data warehouse optimized for analytical queries, not for transactional microservice workloads requiring individual databases. Option D is wrong because Amazon Aurora is a MySQL-compatible relational database, but it is a separate service from RDS for MySQL; while Aurora offers higher performance and availability, the question specifically asks for a service to implement the database layer with minimal operational overhead, and RDS for MySQL is the standard managed MySQL service, whereas Aurora is a distinct offering with different cost and performance characteristics that may not be necessary for all microservices.

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Option A is wrong because the policy includes a Deny that blocks non-HTTPS requests and requests from IPs outside 192.0.2.0/24, so not all HTTPS requests from any IP are allowed. Option B is wrong because the Deny is not overridden; it explicitly denies requests that do not meet the conditions, and the Allow only applies to requests that satisfy both HTTPS and the IP range. Option D is wrong because the Deny requires aws:SecureTransport to be true, so HTTP requests from 192.0.2.0/24 are denied.

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Option A is wrong because `rds:List*` is not a valid IAM action prefix for RDS; RDS uses `Describe` actions for read operations, not `List`. Option B is wrong because `rds:Get*` is not a valid IAM action for RDS; AWS RDS API uses `Describe` actions (e.g., `DescribeDBInstances`) rather than `Get` actions. Option D is wrong because `ec2:DescribeRdsInstances` does not exist; EC2 and RDS are separate services with distinct action namespaces, and RDS actions must use the `rds:` prefix.

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Option B is wrong because Kinesis Data Firehose is a near-real-time delivery service with a minimum buffer interval of 60 seconds, which cannot achieve sub-second latency. Option C is wrong because AWS Lambda has a maximum concurrency limit and per-invocation duration constraints that make it unsuitable for processing 100,000 events per second with <1 second latency, and it lacks native streaming SQL capabilities. Option D is wrong because Apache Spark Streaming on Amazon EMR introduces higher startup and processing overhead (typically seconds of latency) compared to Kinesis Data Analytics SQL, and MSK requires more operational overhead for cluster management.

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Option B is wrong because the standard Kubernetes Horizontal Pod Autoscaler (HPA) with Prometheus requires custom metrics to be exposed via the Kubernetes custom metrics API, but it does not natively support direct scaling based on SQS queue depth without additional components like KEDA or a custom metrics adapter. Option C is wrong because the Kubernetes Cluster Autoscaler is responsible for scaling the number of worker nodes in the cluster, not the number of pods based on application-level metrics like SQS queue depth. Option D is wrong because AWS Auto Scaling with target tracking is an AWS-native service for scaling EC2 instances or other AWS resources, not Kubernetes pods, and it cannot directly interpret Kubernetes custom metrics or scale pods within an EKS cluster.

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Option A is wrong because AWS CloudHSM provides dedicated hardware security modules (HSMs) that you manage yourself, not AWS-managed keys; it requires you to handle key lifecycle and scaling, and does not offer the same level of integration with AWS services as KMS. Option B is wrong because AWS Secrets Manager is designed to securely store and rotate secrets (e.g., database credentials, API keys), not to create or manage encryption keys; it can use KMS to encrypt those secrets, but it is not a key management service itself. Option D is wrong because AWS Certificate Manager (ACM) is used to provision, manage, and deploy public and private SSL/TLS certificates for securing network traffic (in transit), but it does not create or manage encryption keys for data at rest; it relies on KMS for private key protection in some cases, but its primary function is certificate lifecycle management.

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Option B is wrong because a Network Load Balancer (NLB) operates at Layer 4 and does not support HTTP/HTTPS path-based routing or host-based routing, which are often needed for containerized applications; also, step scaling policies are less precise than target tracking for maintaining a specific CPU utilization metric. Option C is wrong because while it uses an ALB, step scaling policies are not the recommended approach for scaling based on CPU utilization—target tracking policies are simpler and more effective as they automatically adjust to maintain a target metric value. Option D is wrong because manual scaling does not provide automatic scaling based on CPU utilization, which is a requirement for the application to scale automatically.

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Option B is wrong because Amazon RDS does not support VPC endpoints (interface or gateway endpoints) for database connections; VPC endpoints are used for AWS service APIs (e.g., S3, DynamoDB), not for database traffic. Option C is wrong because placing a Network Load Balancer in front of RDS adds unnecessary complexity and cost, and the NLB still requires the Lambda function to be in the VPC to connect privately, making it redundant. Option D is wrong because creating a public endpoint for RDS exposes the database to the internet, violating the requirement for no public internet access and introducing significant security risks.

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Option A is wrong because S3 bucket policies with condition keys for tags can enforce access control based on resource tags, but they do not provide the ability to audit access at the granularity required; CloudTrail is needed for auditing, and bucket policies alone cannot enforce column-level or tag-based fine-grained access control on the data content itself. Option B is wrong because SSE-C encrypts data at rest using customer-provided keys, but it does not provide any access control or auditing capabilities; bucket policies are separate and do not enable fine-grained tag-based access control or auditing. Option C is wrong because AWS Lake Formation with column-level permissions provides fine-grained access control at the table and column level, but it does not natively support tag-based access control on S3 objects and does not inherently provide auditing; CloudTrail would still be required for auditability, and Lake Formation is more suited for database-style permissions rather than S3 object tag-based policies.

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Option A is wrong because increasing the node type only improves capacity and performance, not availability; it does not address downtime during patching, as the single node still experiences an outage during maintenance. Option B is wrong because a blue/green deployment for Redis would require manual endpoint switching and data synchronization, which is complex and introduces potential data loss or inconsistency; ElastiCache's built-in Multi-AZ failover is simpler and more reliable for patching. Option D is wrong because a single-node cluster in one Availability Zone has no redundancy; patching or any failure causes complete downtime, and snapshots only aid recovery, not high availability.

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Option A is wrong because cross-zone load balancing distributes traffic evenly across instances, not across availability zones; the ALB does not balance by zone when cross-zone is enabled. Option B is wrong because it incorrectly assumes a zone-based distribution proportional to instance count (2 instances in us-east-1a, 1 in us-east-1b), but cross-zone load balancing overrides zone-level weighting to give each instance an equal share. Option D is wrong because the Application Load Balancer uses a round-robin algorithm by default, not least outstanding requests; the least outstanding requests routing algorithm is used by the Network Load Balancer, not the ALB.

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Option A is wrong because the Principal field in an S3 bucket policy can accept an IAM role ARN (e.g., `arn:aws:iam::123456789012:role/MyAppRole`) and is syntactically correct; using the role name alone would be invalid. Option B is wrong because the Resource ARN `arn:aws:s3:::my-bucket/*` correctly specifies all objects within the bucket, and changing it to `arn:aws:s3:::my-bucket` would apply to the bucket itself, not its objects, which would not match the `s3:GetObject` action. Option C is wrong because the condition key `s3:x-amz-server-side-encryption` is spelled correctly; the issue is not a typo but a fundamental misunderstanding of what the key evaluates.

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Option A is wrong because adding an SQS queue introduces unnecessary latency and cost without benefit, as Lambda can be invoked directly by S3 events and the 500 MB payload exceeds SQS's 256 KB message size limit, requiring S3 presigned URLs or separate metadata handling. Option B is wrong because AWS Fargate is a container-based compute service that incurs higher cost and operational overhead compared to Lambda for this use case, and it does not natively integrate with S3 events without additional components like EventBridge or SQS. Option D is wrong because using Step Functions to orchestrate a Lambda function and an EC2 instance adds complexity and cost, and EC2 requires manual scaling and management, which is less cost-effective and scalable than a pure serverless approach.

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Option A is wrong because a NAT instance is used for outbound traffic from private subnets, not for SSL termination or HTTP-to-HTTPS redirection; it does not support load balancing or certificate management. Option B is wrong because installing the certificate on each EC2 instance and configuring the web server to redirect HTTP to HTTPS is inefficient, requires manual certificate renewal, and does not leverage Elastic Beanstalk's managed load balancer for centralized SSL termination. Option D is wrong because while CloudFront can redirect HTTP to HTTPS, it adds unnecessary complexity and cost for a simple single-region web app; the question specifically asks about configuring the Elastic Beanstalk environment, not an external CDN.

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Option A is wrong because the application is in a private subnet and uses an S3 Gateway Endpoint (vpce-12345678), which is explicitly specified in the bucket policy's `aws:SourceVpce` condition; the policy allows access from that endpoint, so the endpoint is being used. Option B is wrong because the bucket policy does not explicitly deny encryption in transit; rather, it likely requires it via the `aws:SecureTransport` condition, and the failure is due to using HTTP, not a lack of encryption-in-transit allowance. Option D is wrong because the `x-amz-server-side-encryption` header is for server-side encryption at rest, not for transport security; the error is about HTTPS (encryption in transit), not encryption at rest.

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Option B (Amazon S3) is wrong because S3 is an object storage service with higher latency and eventual consistency for read-after-write, making it unsuitable for real-time session state that requires sub-millisecond access and strong consistency. Option C (Amazon DynamoDB) is wrong because while DynamoDB can store session state, it is a NoSQL database with higher latency compared to in-memory caching, and it is not optimized for the extremely low-latency, high-throughput session lookups that ElastiCache provides. Option D (Amazon CloudFront) is wrong because CloudFront is a content delivery network (CDN) that caches static and dynamic content at edge locations, not a session store; it cannot be used to store or share mutable session state across Lambda invocations.