CCNA Continuous Improvement for Existing Solutions Questions

The most likely cause of the timeout is that the Lambda function is processing larger files than before, which increases execution time beyond the 5-minute timeout. Option A is incorrect: moving to a different VPC might cause network latency but not necessarily a timeout due to file processing. Option B is incorrect: reserved concurrency controls the number of concurrent executions, not the execution duration of a single invocation.

Option C is incorrect: while increasing memory can improve CPU performance and reduce execution time, the immediate cause of a timeout is typically an increase in processing workload, such as larger files. Therefore, option D is correct.

Increasing the reserved concurrency to 20 allows more Lambda functions to run simultaneously, enabling more messages to be processed in parallel. This is the fastest way to increase throughput because it directly increases the number of concurrent executions handling messages from the SQS queue. Option A is incorrect because Lambda does not have a configurable maximum thread count.

Option C is incorrect because the SQS batch size is limited to a maximum of 10, not 100. Option D is incorrect because creating additional queues would require significant application changes and does not directly increase the processing capacity of the existing Lambda function.

Enabling sticky sessions (session affinity) on the ALB ensures that all requests from a user session are routed to the same EC2 instance, preventing logout on page refresh. Option A is incorrect because cross-zone load balancing distributes traffic across instances in multiple Availability Zones but does not affect session persistence. Option C is incorrect because replacing the ALB with a Classic Load Balancer does not provide a better solution; ALB also supports sticky sessions.

Option D is incorrect because while ElastiCache can store session data externally, it does not address the routing issue; the root cause is that requests are going to different instances, so sticky sessions directly solve the problem.

A target tracking scaling policy automatically adjusts capacity based on a specific metric, and using the ALB RequestCountPerTarget metric is appropriate for handling traffic spikes in a cost-effective manner. This policy maintains the desired metric value by adding or removing instances as needed, without manual intervention. Option A is incorrect because a scheduled scaling policy assumes predictable peaks, but the traffic spikes are periodic and may not follow a fixed schedule.

Option C is incorrect because manual intervention is not cost-effective and defeats the purpose of automation. Option D is incorrect because a simple scaling policy based on CPU utilization may not react quickly enough to sudden traffic spikes and could be less cost-effective than target tracking.

AWS CodeDeploy supports automatic rollback based on CloudWatch alarm metrics, such as the ALB error rate exceeding 5% for 10 minutes, with minimal operational overhead. Option A is incorrect because modifying Auto Scaling group health checks does not directly monitor application-level error rates for rollback. Option B is incorrect because manual approval gates require human intervention and do not provide automatic rollback.

Option D is incorrect because using a custom Lambda function adds complexity and is not the best practice; CodeDeploy with CloudWatch alarms provides a simpler, fully managed solution.

A Network Load Balancer (NLB) with Elastic IPs can distribute traffic to NAT instances across multiple Availability Zones, providing a fixed outbound IP and high availability. If one AZ fails, the NLB redirects traffic to healthy NAT instances in other AZs, ensuring the workload remains operational. Option A is incorrect because a proxy fleet behind an ALB would still result in multiple outbound IPs, as ALB instances have different IPs.

Option B is incorrect because using a single NAT gateway across all AZs is not possible – NAT gateways are AZ-specific and cannot span zones; also, a single NAT gateway is a single point of failure. Option C is incorrect because a single NAT instance in one AZ lacks high availability – if that AZ fails, outbound connectivity is lost.

An explicit deny in a bucket policy overrides any allow, but only if the deny statement applies to the principal making the request. If the deny statement specifies a different principal (e.g., an AWS account or role not matching the users), or includes a condition that does not evaluate to true for these users, the deny does not take effect. Therefore, the allow statement granting access based on IP range would still apply, allowing users outside the range to access the bucket.

Option A is incorrect because the condition key syntax is not the issue; the deny is not applying to the correct principals. Option C is incorrect because the question states an IAM policy is attached to an S3 bucket, which is possible (resource-based policy), and the condition key 'aws:SourceIp' is valid. Option D is incorrect because the policy is attached to the bucket, not to individual users.

ECS tasks should be launched in the same private subnet as the RDS instance to allow direct network communication via VPC routing, without needing internet gateways or NAT. Option B is incorrect because placing ECS tasks in a public subnet with a NAT gateway would require traffic to go through the NAT, but NAT gateways are for outbound internet access, not for inbound traffic to private resources; also, public subnets have direct internet access which is unnecessary and could introduce security risks. Option C is incorrect because a VPN connection is typically used for on-premises to VPC connectivity, not for internal VPC communication; VPC routing and security groups are sufficient.

Option D is incorrect because an Application Load Balancer is used for load balancing HTTP/HTTPS traffic to application targets, not for routing internal database traffic; it would add unnecessary complexity and latency.

Creating an Aurora read replica offloads read queries from the primary DB instance, reducing CPU load on it. Option C is correct because implementing an ElastiCache for Redis caching layer reduces the number of repeated read queries hitting the database, further lowering CPU utilization. Option B is wrong: while increasing the DB instance class could provide more CPU resources, it does not reduce load—it only scales up, which is not a best practice for immediate load reduction without code changes.

Option D is wrong because Performance Insights is a monitoring tool for identifying performance bottlenecks, not a direct mechanism to reduce database load. Option E is wrong because increasing the Auto Scaling group size only scales the application tier, which would increase traffic to the database, worsening the CPU issue.

AWS Config provides managed rules to evaluate resource compliance. The 's3-bucket-server-side-encryption-enabled' rule checks if S3 buckets have server-side encryption enabled. This can be applied to all buckets in the account.

Option A is incorrect because while AWS Trusted Advisor can check for bucket encryption, AWS Config is specifically designed for continuous compliance monitoring and auditing at scale, making it the better approach for identifying noncompliant buckets across thousands of resources. Option B is incorrect: CloudTrail logs record API calls (like PutBucketEncryption), but do not show the current encryption configuration of existing buckets. Option C is incorrect: S3 Inventory provides a list of objects and their metadata, but it does not directly indicate bucket-level encryption settings; it focuses on object-level encryption.

DynamoDB Accelerator (DAX) provides an in-memory cache that reduces read latency by serving frequently accessed items from cache, without increasing provisioned RCU. This is cost-effective as it adds minimal cost compared to increasing capacity. Option A is wrong because switching to strongly consistent reads consumes double the RCU (since they require a read of the primary replica), increasing cost and potentially worsening latency.

Option C is wrong because a Global Secondary Index (GSI) offloads reads from the base table but does not reduce latency for reads on the base table itself; it also incurs additional write costs. Option D is wrong because increasing RCU to 2000 would double the cost, though it might reduce throttling, it's not the most cost-effective. Option E is wrong because using ElastiCache adds operational complexity and cost, and DAX is a more seamless, DynamoDB-native caching solution.

You can assign Elastic IP addresses to the Network Load Balancer per Availability Zone, providing static IPs for external partners to whitelist. Option E is correct because AWS Global Accelerator provides two static IP addresses (or allows you to bring your own) and can be used with an NLB as an endpoint, offering static IPs that partners can whitelist. Option A is incorrect because attaching Elastic IPs to EC2 instances does not provide static IPs for the NLB frontend; the NLB uses its own IPs.

Option C is incorrect because an Application Load Balancer (ALB) does not support static IP addresses by default and operates at layer 7, not suitable for this requirement. Option D is incorrect because AWS WAF is a web application firewall that filters traffic based on rules but does not provide static IP addresses for the load balancer.

Uses AWS Config rules with automatic remediation to enforce compliance on both encryption and public access settings. Option C creates an SCP that denies s3:PutObject actions unless the x-amz-server-side-encryption header is set to aws:kms, ensuring new objects are encrypted with KMS. Option E is incorrect because it only denies put actions on bucket policies and public access blocks if encryption is not enabled, but does not itself prohibit public access; users could still enable public access if encryption is met.

Option B is incorrect because CloudTrail is for auditing, not remediation. Option D is incorrect because per-account bucket policies can be overridden and are less effective than organizational policies.

AWS Batch with a managed compute environment automatically handles job scheduling, scaling, and infrastructure provisioning. It can accommodate long-running jobs beyond Lambda's 15-minute limit and uses Spot Instances to reduce costs, minimizing operational overhead. Option A is incorrect because managing EC2 instances and Auto Scaling requires more operational effort than using a managed service like AWS Batch.

Option B is incorrect because even with increased memory, Lambda has a maximum execution time of 15 minutes, which may not be sufficient for processing files that exceed this limit. Option D is incorrect because while ECS with Fargate reduces infrastructure management, AWS Batch is specifically designed for batch processing and provides more features like job queues, automatic retries, and integration with Spot Instances, making it a better fit for this use case.

A rolling update with a lifecycle hook (Option B) is the best approach for minimizing downtime in a stateful web application using a shared file system. The lifecycle hook allows the instance to gracefully handle existing connections and unmount the file system before termination, ensuring no data corruption or abrupt disconnection. This reduces downtime compared to other methods.

Option A (in-place update without hooks) risks disrupting active connections. Option C (blue/green deployment) may not work seamlessly with stateful applications due to shared storage. Option D (terminate all instances) causes full downtime.

When a Lambda function's execution time increases due to a code update, each invocation holds a concurrency slot for longer. With reserved concurrency set to 100, if the function's invocations per second remain the same but each takes longer, the number of concurrent executions can exceed the account-level concurrency limit (default 1000), causing throttling (HTTP 429). Option A is wrong because CloudWatch Logs throttling would result in dropped log events, not Lambda throttling errors.

Option B is wrong because if the S3 bucket received the same rate of uploads, the invocation rate wouldn't change; the question states throttling occurred only after the code update, not due to increased uploads. Option C is wrong because missing IAM permissions would cause access denied errors (HTTP 403), not throttling errors.

Updating the bucket policy to explicitly deny uploads with SSE-KMS ensures that only SSE-S3 encryption is allowed. The current policy denies uploads without the x-amz-server-side-encryption header set to AES256, but it does not prevent uploads that specify SSE-KMS (which uses a different header value). By adding a deny condition for SSE-KMS, the company can enforce SSE-S3 exclusively.

Option A is incorrect because S3 Lifecycle policies manage object transitions and expiration, not encryption enforcement. They cannot change encryption of existing objects or prevent new uploads with unauthorized encryption.

Option B is incorrect because enabling default encryption on the bucket applies only to objects uploaded without explicit encryption headers. It does not block uploads that specify SSE-KMS or other encryption methods, as the request headers override default encryption.

Option D is incorrect because S3 Object Lambda is designed to transform data when objects are retrieved, not to enforce encryption settings on new uploads. It cannot change the encryption of objects at rest.

S3 Object Lock with a retention period exceeding 30 days prevents lifecycle expiration until the retention period ends. Option A is incorrect: when versioning is enabled, lifecycle expiration still applies – S3 creates a delete marker for the current version and permanently deletes noncurrent versions based on the noncurrent days configuration. Option B is incorrect: storage class (including Glacier Deep Archive) does not block expiration; objects in any class can be expired.

Option D is incorrect: S3 Lifecycle does not use an IAM role; it operates under the bucket's permissions, and while a bucket policy denying s3:DeleteObject could block deletion, the issue described is not with an IAM role.

The correct answers are B and C. Option B: Using the same instance type ensures uniform capacity, so round-robin routing distributes traffic evenly. Option C: Changing the routing algorithm to least outstanding requests (LOR) distributes traffic based on pending requests, naturally giving more traffic to larger instances (t3.xlarge) and balancing load despite size differences.

Option A (connection draining) helps with graceful deregistration but does not improve distribution. Option D (sticky sessions) worsens distribution by pinning users. Option E (increasing deregistration delay) does not affect distribution.

The strangler fig pattern allows incremental migration by routing specific API requests to new Lambda functions via API Gateway while keeping the monolithic application for the rest. This approach avoids downtime. Option A is incorrect because deploying all microservices in a new VPC and cutting over DNS risks downtime and is not incremental.

Option B is incorrect because creating a new version of the monolith that calls Lambda functions still leaves the monolith in place and does not fully utilize API Gateway for routing. Option C is incorrect because CodeDeploy blue/green deployment is for deploying to EC2 or Lambda, but it does not provide a pattern for incremental migration of a monolith to microservices; the strangler fig pattern is more appropriate.

AWS Batch automatically provisions and terminates EC2 instances based on job queue requirements, and it can use Spot Instances for significant cost savings. Option A (AWS Lambda) is wrong because the batch job takes 20 minutes, exceeding the 15-minute maximum execution time for Lambda functions. Option B (Reserved Instance) is wrong because it requires a 1-year commitment and still incurs costs for idle hours when the job is not running.

Option C (t3.micro running 24/7) is wrong because while it is low-cost, it still charges for idle time, making it less cost-effective than only paying for compute during job execution.

Options C, D, and E are correct because blue/green deployments for ECS require separate task definitions for each environment (C), CodeDeploy to manage traffic shifting (D), and an Application Load Balancer with separate target groups for each task set to route traffic (E). Option A is incorrect because storing artifacts in ECR is about container images, not a deployment step. Option B is incorrect because using a load balancer is a prerequisite for any ECS service, but it is not one of the specific steps required to implement blue/green deployments.

The blue/green-specific steps are C, D, and E.

The most cost-effective solution with minimal architectural changes. By replacing the ephemeral instance store with an EBS volume, the data becomes persistent. Then creating an AMI and using Auto Scaling with an Application Load Balancer provides high availability and disaster recovery automatically.

Option B only adds backups but does not provide high availability, and restoring from snapshots is slower. Option C, AWS Backup, cannot back up instance store data directly, so it is ineffective. Option D provides high availability but still uses instance store, so data is lost on instance failure; it also does not address data persistence.

Therefore, Option A is correct.

An AWS Config rule can detect stacks missing the required tag, and a custom IAM policy can deny the ability to create or update stacks that do not have the tag, providing preventive enforcement. Option A is incorrect because AWS Service Catalog can enforce tags on products it provisions, but the question is about all CloudFormation stacks, not just those from Service Catalog. Option C is incorrect because a Lambda function can alert or remediate after the fact, but it does not prevent creation of non-compliant stacks.

Option D is incorrect because tagging after creation does not enforce the tag requirement at creation time, and AWS Resource Groups is not an enforcement mechanism.

Options B, C, and D are correct: Using CodeBuild local cache (B) stores dependencies across builds, reducing download time. Running CodeBuild in a VPC with a NAT gateway (C) ensures consistent outbound connectivity, mitigating network timeouts. Storing dependencies in an S3 bucket and configuring the build to use cached dependencies (D) also reduces reliance on external downloads.

Option A is incorrect because increasing the timeout only delays the failure but does not address the root cause. Option E is incorrect because a larger compute type may improve build speed but does not fix network timeouts.

The Lambda function requires a resource-based policy (also known as a function policy) that grants the S3 service permission to invoke the function. The CloudFormation template does not include such a policy, so S3 cannot trigger the Lambda function. Option A is incorrect because the BucketNotification resource is correctly configured to send events to the Lambda function, but the function itself lacks the invoke permission.

Option C is incorrect because the Lambda execution role manages permissions for the function to access other services (like CloudWatch Logs), not for other services to invoke it. Option D is irrelevant because the trigger itself is the issue, not the function code.

AWS Config conformance packs can be deployed at the organization level using AWS Organizations, enabling centralized management of rules across hundreds of accounts. Automatic remediation is achieved by associating Systems Manager Automation documents with non-compliant resources. Option A is incorrect because CloudFormation StackSets require per-account deployment and management, which does not scale as efficiently as conformance packs.

Option B is incorrect because Config rules in each account with Lambda functions lack centralized deployment and management. Option D is incorrect because an AWS Config aggregator only provides a cross-account compliance view, not enforcement or remediation.

Options A and D are correct. Using a DynamoDB table to store processed file IDs (A) ensures idempotency by checking for duplicates before processing. Configuring S3 event notifications with prefix and suffix filters (D) reduces the chance of triggering multiple invocations for the same file.

Option B is incorrect because simply using an SQS queue does not guarantee idempotent processing; additional deduplication logic is needed. Option C is incorrect because increasing the Lambda timeout does not prevent multiple invocations. Option E is incorrect because S3 Transfer Acceleration improves upload speed, not idempotency.

The Deny statement with a StringNotEquals condition on the 's3:x-amz-server-side-encryption' header will block any PutObject request where the header is not present or is set to a value other than 'AES256'. Since the user does not specify the header, the condition evaluates to true, and the Deny applies, causing the upload to fail. Option B is incorrect because the Deny statement applies even when the header is missing, not only when it is present with a different value.

Option A is incorrect because the Allow statement's condition is not met, so it does not grant permission. Option D is incorrect because the condition is valid and correctly evaluated.

Reducing the batch size decreases the number of messages processed per invocation, which reduces the total processing time and helps avoid timeouts. Option A is incorrect because increasing the visibility timeout does not reduce the processing time within the function; it only prevents messages from becoming visible again before the function finishes. Option B is incorrect because increasing reserved concurrency increases the number of concurrent invocations but does not affect the processing time of a single invocation.

Option C is incorrect because increasing the function timeout to 15 minutes may allow the function to complete, but it does not address the root cause of long processing times and could lead to higher costs and delayed processing; reducing the batch size is a more targeted solution.

Using a custom CloudWatch metric for SQS queue depth enables scaling based on actual workload, providing more responsive scaling than the default CPU or network metrics. Option C: Reducing the cooldown period allows the Auto Scaling group to launch instances more quickly after a scaling activity, improving responsiveness. Option D: Step scaling policies adjust capacity incrementally based on the size of the alarm breach, allowing more aggressive scaling when queue depth is high.

Options B and E are incorrect: Decreasing warm-up time is not a supported CloudWatch feature, and increasing instance size does not improve scaling speed but rather increases capacity per instance.

(bucket policy denying PutObject without x-amz-server-side-encryption header set to 'aws:kms') enforces that all uploads must specify SSE-KMS at the request level. Option C (default encryption with SSE-KMS) ensures that objects uploaded without encryption headers are automatically encrypted with a KMS key. Together, they enforce encryption with customer-managed KMS keys.

Option B (CloudTrail auditing) logs requests but does not enforce encryption. Option D (bucket policy allowing PutObject only if encrypted) allows any encryption type, not specifically KMS, so it does not enforce customer-managed keys. Option E (disabling SSE-S3) is not possible; you cannot disable SSE-S3, only set default encryption, and it would not enforce KMS alone.

Options A and B are correct. A bucket policy that denies PutObject without the x-amz-server-side-encryption header enforces encryption at upload time, ensuring all objects are encrypted. Default encryption on the bucket automatically encrypts objects that are uploaded without encryption headers, covering cases where the policy may not apply.

Option C (Cross-Region Replication) replicates objects but does not enforce encryption. Option D (Versioning) provides object version management and can help protect against overwrites, but does not enforce encryption. Option E (Server Access Logs) provides logging but does not enforce encryption.

The Kinesis Client Library (KCL) distributes shard processing across workers. Increasing the number of EC2 instances running the consumer application adds more workers, allowing parallel processing of shards and thus improving throughput for the lagging consumer. Option A is incorrect because decreasing the batch size reduces the number of records processed per call, which typically lowers throughput.

Option B is incorrect because increasing the number of shards increases the stream's total capacity but does not directly help a consumer that is already falling behind; it may actually require more workers to handle the additional shards. Option C is incorrect because extending the retention period affects how long data is stored, not how quickly it is processed.

Implementing a timeout and automatic retry logic in the JDBC connection pool ensures that after a failover, the application can quickly detect the failed connection and reconnect to the new primary. Option A is incorrect because restarting the application via CloudWatch Events and Lambda does not directly address connection failures and may cause additional downtime. Option B is incorrect: while Amazon RDS Proxy can help manage connections and reduce latency, it does not eliminate the need for application-level retry logic; during failover, connections are dropped and need to be re-established.

Option D is incorrect because increasing the TTL of the RDS DNS record would actually cause the application to cache the old DNS record longer, delaying failover detection.

To improve scaling responsiveness, the team should use a target tracking scaling policy based on CPU utilization (Option B) because target tracking continuously adjusts the desired capacity to meet the target value, providing faster and more precise scaling than step scaling. Decreasing the cooldown period (Option D) allows new instances to start contributing to metrics sooner, enabling the Auto Scaling group to scale out more quickly during spikes. Option A (increasing instance size) does not improve the speed of scaling out; it only increases capacity per instance but not responsiveness.

Option C (increasing health check grace period) delays the detection of unhealthy instances, slowing down scaling. Option E (decreasing minimum number) reduces baseline capacity and does not help with scaling out speed.

API Gateway caches the authorizer result based on the token. If a previous request with the same token was denied (e.g., due to an invalid token at that time), the cached deny policy will be used for subsequent requests even if the token is now valid, causing a 401 error. Option A is incorrect because the Lambda authorizer is being invoked successfully and returning a policy; the issue is with caching.

Option B is incorrect because the problem states tokens are valid, not expired. Option C is incorrect because throttling would result in the authorizer not being invoked, but the logs show it is invoked.