DVA-C02 Troubleshooting and Optimization Practice Test 14 — 15 Questions

Question 1

A developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The developer notices that the function is processing records slowly, and the iterator age is increasing. CloudWatch Logs show that the function is not experiencing errors or throttling, but the execution time per invocation is close to the 5-minute timeout. The stream has 10 shards. What is the most cost-effective way to increase processing throughput?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Increase the parallelization factor to 10. Increasing the parallelization factor to 10 allows each shard to be processed by up to 10 concurrent Lambda invocations, which directly increases throughput without additional shard costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation processing time; parallelization reduces the iterator age by processing multiple batches per shard simultaneously.

Answer

Increase the batch size to 1000

Answer

Increase the memory of the Lambda function

Answer

Split the stream into more shards

Question 2

A web application runs on Amazon EC2 instances behind an Application Load Balancer (ALB). During rolling updates of the Auto Scaling group, users intermittently receive HTTP 502 (Bad Gateway) errors. The developer checks the ALB access logs and notices that requests are being routed to instances that are in the 'Draining' state. The ALB has connection draining enabled with a timeout of 30 seconds. The Auto Scaling group terminates instances after they are taken out of service. What is the most likely cause of the 502 errors?

Accepted Answer

The connection draining timeout is too short, causing the ALB to terminate connections before in-flight requests finish.. The 502 errors occur because the ALB's connection draining timeout of 30 seconds is too short to allow all in-flight requests to complete before the Auto Scaling group terminates the instances. When an instance enters the 'Draining' state, the ALB stops sending new requests but waits up to the draining timeout for existing connections to finish. If the timeout expires before requests complete, the ALB forcibly closes connections, resulting in HTTP 502 (Bad Gateway) errors for clients whose requests were still in progress.

Answer

The health check interval is set too long, causing the ALB to consider unhealthy instances as healthy.

Answer

Cross-zone load balancing is disabled, so the ALB is routing requests to instances that are already draining.

Answer

The Auto Scaling group's minimum size is too small, causing the ALB to have no healthy targets.

Question 3

A developer notices that an AWS Lambda function, which processes messages from an SQS queue, is taking longer than expected. The function has a reserved concurrency of 5 and a batch size of 10. The SQS queue has a large backlog. CloudWatch metrics show that the function's throttles are high. The function is idempotent and can process up to 100 messages per invocation. What is the most effective way to increase throughput without increasing reserved concurrency?

Accepted Answer

Increase the batch size to 100.. Increasing the batch size to 100 directly reduces the number of Lambda invocations required to process the backlog, thereby decreasing throttling without increasing reserved concurrency. The function's capacity to handle up to 100 messages per invocation makes this alignment optimal. SQS event source mappings support batch sizes up to 10,000 for standard queues, so a batch size of 100 is feasible. Short polling (option D) would not improve throughput; it causes frequent empty responses and does not reduce throttling. Increasing reserved concurrency violates the constraint, and changing timeout (option C) does not address throttling.

Answer

Increase reserved concurrency to 10.

Answer

Change the function timeout to 15 minutes.

Answer

Enable SQS short polling to reduce latency.

Question 4

An application running on Amazon ECS (Fargate) uses an Application Load Balancer (ALB) with connection draining enabled. The application is experiencing intermittent 502 (Bad Gateway) errors during rolling updates of the ECS service. The developer notices that the ALB is routing requests to tasks that are in the 'Draining' state. The ECS service is configured with a deployment circuit breaker that automatically rolls back a failed deployment. What is the most likely cause of the 502 errors?

Accepted Answer

The application is not handling the SIGTERM signal from ECS, causing it to terminate abruptly while the ALB still routes traffic to it.. When ECS sends a SIGTERM signal to a Fargate task during a rolling update, the task is expected to gracefully shut down. If the application does not handle SIGTERM, it terminates immediately, but the ALB may still have the task registered as a target and continue routing requests to it. Since the task is already dead or unresponsive, the ALB receives no valid HTTP response and returns a 502 Bad Gateway error. Connection draining is enabled, but it only works if the task signals the ALB that it is deregistering; without proper SIGTERM handling, the task dies before the draining process completes.

Answer

The ALB's idle timeout is too short, causing connections to be dropped before the application responds.

Answer

The ALB's connection draining timeout is set to 0 seconds, causing connections to be dropped immediately when deregistering targets.

Answer

The ECS deployment circuit breaker is incorrectly configured to roll back on health check failures.

Question 5

A developer notices that an AWS Lambda function, which uses Amazon RDS Proxy to connect to an Aurora MySQL database, is experiencing increased latency and occasional connection timeouts. The function is configured with a reserved concurrency of 100 and is deployed in a VPC. The RDS Proxy's maximum connections is set to 1000. CloudWatch metrics show that the DatabaseConnections metric for the proxy is consistently at 1000. What is the most likely cause of the increased latency and timeouts?

Accepted Answer

The Lambda function is not reusing database connections properly, exhausting the proxy connection pool. The RDS Proxy's DatabaseConnections metric is consistently at 1000, which equals the proxy's maximum connections setting. This indicates the proxy connection pool is fully saturated. When all connections are in use, new connection requests from Lambda invocations must wait, causing increased latency, and if the wait exceeds the timeout, connection timeouts occur. The most likely cause is that the Lambda function is not reusing database connections (e.g., not using connection pooling or keeping connections open across invocations), exhausting the pool.

Answer

The RDS Proxy target group is not configured with the correct DB instance

Answer

The Lambda function's execution role is missing the rds-db:connect permission

Answer

The VPC does not have a NAT Gateway for outbound traffic

Question 6

A developer monitors an AWS Lambda function that processes records from an Amazon SQS queue and writes results to an Amazon DynamoDB table. CloudWatch Logs show that execution time has increased over the past week, and the function frequently times out at the 5-minute timeout. The function's code has not been changed recently. CloudWatch metrics show a high rate of DynamoDBProvisionedThroughputExceededException errors. The DynamoDB table has 5 write capacity units (WCUs). What action will MOST effectively reduce the function's execution time?

Accepted Answer

Increase the write capacity units (WCUs) on the DynamoDB table.. The high rate of DynamoDBProvisionedThroughputExceededException errors indicates that the Lambda function is being throttled by DynamoDB due to insufficient write capacity. When writes are throttled, the Lambda function must retry, which increases execution time and can lead to timeouts. Increasing the WCUs on the DynamoDB table directly addresses the root cause by allowing the function to write without throttling, thereby reducing execution time.

Answer

Increase the Lambda function's timeout to 10 minutes.

Answer

Increase the Lambda function's memory allocation to 3008 MB.

Answer

Use an Amazon SQS FIFO queue instead of a standard queue for the Lambda trigger.

Question 7

A developer is troubleshooting performance issues in an application that uses Amazon DynamoDB as the primary data store. The application reads a large set of items using a Query operation on a Global Secondary Index (GSI). The developer notices high read latency and throttled requests on the GSI. The base table has sufficient read capacity. The GSI is projected with KEYS_ONLY. Which action would most likely reduce the latency and throttling?

Accepted Answer

Increase the read capacity units (RCU) of the GSI.. A Global Secondary Index (GSI) has its own provisioned read capacity, separate from the base table. When a Query operation reads from a GSI, it consumes RCUs from the GSI's capacity, not the base table's. Since the base table has sufficient read capacity but the GSI is experiencing throttling and high latency, increasing the GSI's RCU directly addresses the bottleneck by allowing more read requests per second against the index.

Answer

Increase the read capacity units (RCU) of the base table.

Answer

Change the GSI projection to ALL.

Answer

Create a Local Secondary Index instead.

Question 8

A developer monitors an AWS Lambda function that processes messages from an Amazon SQS queue. CloudWatch logs show that the function's execution time has increased significantly over the past week. The function's code has not been changed recently. The function makes calls to an Amazon DynamoDB table. CloudWatch metrics show a high rate of DynamoDBProvisionedThroughputExceededException errors. The DynamoDB table has 5 read and 5 write capacity units (RCU/WCU). What is the most effective action to reduce the function's execution time?

Accepted Answer

Increase the DynamoDB table's read and write capacity units.. The high rate of DynamoDBProvisionedThroughputExceededException errors indicates that the Lambda function is being throttled by DynamoDB due to insufficient read and write capacity units. This throttling causes the function to retry operations, significantly increasing execution time. Increasing the RCU/WCU from 5 to a higher value directly addresses the bottleneck, allowing operations to complete without retries and reducing overall execution time.

Answer

Increase the Lambda function's memory allocation.

Answer

Increase the Lambda function's reserved concurrency.

Answer

Increase the Lambda function's timeout.

Question 9

A developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The developer notices that the function is processing records slowly, and the iterator age is increasing. CloudWatch Logs show that the function is not experiencing errors or throttling, but the execution time per invocation is close to the 5-minute timeout. The stream has 10 shards. What is the most cost-effective way to increase processing throughput?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Increase the function's memory. Increasing the function's memory is the correct first step because Lambda allocates CPU proportionally to memory, and more CPU reduces processing time for CPU-bound tasks like decompressing or parsing large files. This directly addresses the timeout by making the function complete faster, without requiring code changes. The current 128 MB setting is the minimum, which provides the least CPU, so even a modest increase can significantly reduce execution time.

Answer

Increase the function's timeout

Answer

Increase the function's reserved concurrency

Answer

Increase the S3 event notification batch size

Question 10

A developer is troubleshooting performance issues in an application that uses Amazon ElastiCache for Redis. The application experiences periodic latency spikes during peak hours. The developer checks CloudWatch metrics and sees that the 'Evictions' metric is consistently high and the 'CacheHitRate' metric is low. The cluster uses a single cache.t3.small node. Which action will most likely improve the cache hit rate and reduce latency?

Accepted Answer

Use a larger instance type. The symptoms—high evictions and low cache hit rate—indicate that the single cache.t3.small node is running out of memory. Using a larger instance type increases the available memory, allowing more data to be cached, reducing evictions, and improving the cache hit rate. This directly addresses the root cause of memory pressure without changing the cluster architecture or data expiration behavior.

Answer

Increase the number of replicas

Answer

Enable cluster mode and add more shards

Answer

Increase the TTL of cached items

Question 11

A developer is troubleshooting a DynamoDB table that is experiencing high write throttling (ProvisionedThroughputExceededException) on certain days. The table has provisioned write capacity of 1000 WCU. The table has a partition key of 'user_id' which is a UUID. The table is accessed by multiple services. CloudWatch metrics show that the WriteThrottleEvents are spiking during specific hours, and the ConsumedWriteCapacityUnits often reaches 1000. What is the most likely cause of the throttling?

Accepted Answer

The provisioned write capacity is insufficient to handle the traffic spikes.. The ConsumedWriteCapacityUnits consistently reaches the provisioned 1000 WCU during specific hours, and WriteThrottleEvents spike at those same times. This indicates that the provisioned capacity is insufficient to handle peak traffic, causing requests to be throttled. The partition key (UUID) is well-distributed, so a hot partition is unlikely.

Answer

The partition key is not distributed evenly, causing a hot partition.

Answer

The table does not have DynamoDB Accelerator (DAX) enabled.

Answer

The table is configured with eventual consistency, which throttles writes.

Question 12

A developer is troubleshooting an Amazon API Gateway REST API that returns 504 Gateway Timeout errors for certain requests. The backend is a Lambda function that performs a resource-intensive operation that occasionally takes up to 30 seconds. API Gateway has a default integration timeout of 29 seconds. The developer cannot reduce the execution time. What should the developer do to resolve the timeout issue?

Accepted Answer

Refactor the Lambda function to use asynchronous invocation, return a 202 immediately, and have the client poll for results.. It decouples the client from the long-running Lambda execution. By invoking the Lambda asynchronously, the API Gateway can return a 202 Accepted response immediately, well within the 29-second integration timeout. The client then polls a separate endpoint (e.g., using a presigned S3 URL or a DynamoDB status record) to retrieve the final result, completely sidestepping the timeout limitation.

Answer

Increase the API Gateway integration timeout to 30 seconds.

Answer

Enable API Gateway caching to avoid repeated calls.

Answer

Use multiple Lambda functions to parallelize processing.

Question 13

A developer is troubleshooting an application that uses Amazon ElastiCache for Redis to improve performance. The application periodically experiences high latency during peak hours. The developer checks the ElastiCache metrics and sees that the 'Evictions' metric is consistently high and the 'CacheHitRate' metric is low. The cluster has a single node with a cache.t3.small instance type. Which action will most likely improve the cache hit rate and reduce latency?

Accepted Answer

Scale up to a larger node type (e.g., cache.t3.medium) to increase available memory.. The high 'Evictions' and low 'CacheHitRate' metrics indicate that the Redis node is running out of memory, forcing it to evict keys to make room for new data. Scaling up to a larger node type (cache.t3.medium) increases the available memory, allowing more data to be cached and reducing evictions, which directly improves the cache hit rate and reduces latency.

Answer

Enable cluster mode and distribute data across multiple shards to reduce memory pressure.

Answer

Change the eviction policy to 'allkeys-lfu' to better manage which keys are evicted.

Answer

Add a read replica for the Redis cluster to offload read traffic.

Question 14

An AWS Lambda function processes messages from an Amazon SQS queue and writes results to an Amazon DynamoDB table. The function is configured with a reserved concurrency of 5 and a batch size of 10. CloudWatch metrics show high throttling and a growing queue backlog. The function's execution time averages 1 second per message. What is the MOST effective action to reduce throttling while improving throughput?

Accepted Answer

Increase the reserved concurrency to 20.. The Lambda function is throttling because its reserved concurrency of 5 limits it to 5 concurrent executions. With a batch size of 10 and 1-second execution time, the function can process at most 5 * 10 = 50 messages per second. Increasing reserved concurrency to 20 allows 20 concurrent executions, raising throughput to 200 messages per second, which directly reduces throttling and clears the backlog.

Answer

Increase the batch size to 100.

Answer

Decrease the reserved concurrency to 2.

Answer

Increase the provisioned write capacity of the DynamoDB table.

Question 15

A developer is debugging an issue where an Amazon S3 bucket policy is not allowing cross-account access for a user from another AWS account. The bucket policy grants access to the other account's root user. The IAM user in the other account has an IAM policy that allows s3:GetObject on the bucket. When the user tries to download an object, they get an Access Denied error. What is the most likely cause?

Accepted Answer

The bucket is encrypted with SSE-KMS and the user does not have kms:Decrypt permission. The most likely cause is that the bucket is encrypted with SSE-KMS. When an S3 bucket uses AWS KMS customer master keys (CMKs) for server-side encryption, the bucket policy granting access to the root user of the other account is not sufficient. The IAM user in the other account must also have explicit kms:Decrypt permission on the KMS key, because S3 GetObject calls require decrypting the object before returning it. Without this KMS permission, the request fails with Access Denied even though the S3 bucket policy and IAM policy appear correct.

Answer

The bucket policy does not specify the user's ARN

Answer

The object's ACL is set to private

Answer

The IAM policy does not include s3:ListBucket