DVA-C02 Troubleshooting and Optimization — Timed 50 Questions

Question 1

A developer deployed a new version of an AWS Lambda function that is part of a serverless application. The function uses an Amazon DynamoDB table as a data store. After deployment, the developer notices that the function's latency has increased significantly for some requests. CloudWatch traces show that the increase is due to DynamoDB throttle events. The function is configured with a reserved concurrency of 100 and the DynamoDB table has 5 read capacity units (RCUs) and 5 write capacity units (WCUs). What is the most effective way to reduce the throttling while maintaining application performance?

Accepted Answer

Increase the read and write capacity units on the DynamoDB table. The primary cause of the throttling is insufficient DynamoDB capacity to handle the request volume from the Lambda function. Increasing the read and write capacity units (RCUs/WCUs) directly addresses the throttle events by providing more throughput to match the function's concurrency of 100. This is the most effective solution because it resolves the bottleneck at the data store level without reducing the application's ability to process requests concurrently.

Answer

Decrease the reserved concurrency of the Lambda function to 10

Answer

Enable DynamoDB Accelerator (DAX) for caching reads

Answer

Enable auto scaling on the DynamoDB table

Question 2

A developer is running an AWS Lambda function that is triggered by Amazon S3 events. The function writes processed data to an Amazon DynamoDB table. Over time, the function's execution time has increased significantly. CloudWatch Logs show many DynamoDBProvisionedThroughputExceededException errors. The table is configured with 5 read capacity units (RCUs) and 5 write capacity units (WCUs). The function performs both reads and writes. Which optimization will MOST effectively reduce throttling errors while maintaining performance?

Accepted Answer

Switch the DynamoDB table to on-demand capacity mode. The DynamoDBProvisionedThroughputExceededException errors indicate that the Lambda function is exceeding the provisioned write capacity of 5 WCUs. Switching to on-demand capacity mode eliminates the need to manage throughput, automatically scaling to handle the workload without throttling. This directly resolves the root cause—capacity exhaustion—without requiring manual adjustments or architectural changes.

Answer

Increase the RCUs and WCUs of the table to 50 each

Answer

Implement a DynamoDB Accelerator (DAX) cluster for caching reads

Answer

Increase Lambda function memory to 1024 MB

Question 3

A web application runs on Amazon EC2 instances behind an Application Load Balancer (ALB). During peak hours, users report receiving HTTP 503 (Service Unavailable) errors. The developer checks Amazon CloudWatch metrics and finds that the ALB's request count is high but below the limit, and the target group's healthy host count drops to zero intermittently. The Auto Scaling group for the instances is configured with a minimum of 2, maximum of 10, and a simple scaling policy to add 2 instances when CPU utilization exceeds 70% for 5 consecutive minutes. What is the most likely cause of the 503 errors?

Accepted Answer

The Auto Scaling group's cooldown period prevents new instances from being added quickly enough during rapid traffic spikes. The 503 errors occur because the simple scaling policy has a cooldown period (default 300 seconds) that prevents the Auto Scaling group from launching new instances during rapid traffic spikes. When CPU exceeds 70% for 5 minutes, the policy adds 2 instances, but the cooldown blocks further scaling actions until it expires, even if the newly launched instances are still initializing and the healthy host count drops to zero. This mismatch between traffic demand and scaling responsiveness causes the ALB to have no healthy targets, resulting in 503 errors.

Answer

The ALB's idle timeout is set too low, causing dropped connections

Answer

The Auto Scaling group's maximum capacity of 10 is insufficient

Answer

The health check grace period is preventing instances from being marked healthy

Question 4

A developer is troubleshooting an AWS Lambda function that processes large CSV files (up to 1 GB) uploaded to an Amazon S3 bucket. The function uses Python and the pandas library to perform data transformations. Recently, the function started timing out on large files. CloudWatch Logs show that the function's execution time is close to the 15-minute Lambda timeout, and memory utilization peaks at around 80% of the configured 3,008 MB. The function has not been modified in months. Which action will most likely resolve the timeout issue without requiring code changes?

Accepted Answer

Increase the memory allocation of the Lambda function to the maximum available (10,240 MB). Increasing the memory allocation to the maximum (10,240 MB) proportionally increases the CPU and network throughput allocated to the Lambda function, which directly reduces execution time for CPU-bound pandas operations. Since memory utilization is only at 80% of the current 3,008 MB, the bottleneck is likely CPU, not memory, and Lambda's CPU scales linearly with memory configuration. This action resolves the timeout without any code changes.

Answer

Increase the function timeout to the maximum allowed (900 seconds is already the max)

Answer

Use S3 Select to filter columns and rows before invoking the Lambda function

Answer

Increase the batch size of the S3 event notification to invoke the function with multiple files

Question 5

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 iterator age is increasing, indicating that the function is not keeping up with the stream. 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. Which action will most likely increase processing throughput?

Accepted Answer

Increase the Lambda function memory and CPU allocation.. Option C is correct because the function's execution time is already near the 5-minute timeout, indicating a CPU-bound or memory-bound operation. Increasing memory proportionally increases CPU allocation in Lambda, which directly reduces execution time per invocation, allowing each batch to be processed faster and thus increasing overall throughput without changing the batch size or shard count.

Answer

Increase the batch size to 500.

Answer

Increase the parallelization factor to 10.

Answer

Split the stream into more shards.

Question 6

A developer is troubleshooting an AWS Lambda function that is invoked from an Amazon S3 bucket via event notifications. The function processes images and stores metadata in Amazon DynamoDB. The developer notices that some images are being processed multiple times, resulting in duplicate entries in DynamoDB. The S3 event notification is configured to send events to the Lambda function with the 's3:ObjectCreated:*' event type. The function uses the 'uuid' library to generate a unique ID for each image upon processing. What is the most likely cause of the duplicate processing?

Accepted Answer

S3 event notifications are delivered at least once, and the Lambda function is not idempotent.. Amazon S3 event notifications are delivered on an 'at least once' basis, meaning the same event can be sent to Lambda multiple times. If the Lambda function is not idempotent—i.e., processing the same event multiple times produces duplicate side effects—then duplicate DynamoDB entries will occur. The use of a 'uuid' library inside the function does not help because a new UUID is generated on each invocation, so the same image gets different IDs and is stored as a separate item each time.

Answer

The Lambda function's concurrency is set too high, causing race conditions.

Answer

The DynamoDB table does not have a primary key that prevents duplicates.

Answer

The S3 bucket is configured with versioning, causing multiple object creation events.

Question 7

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?

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 8

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 9

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 allows each invocation to process up to 100 messages instead of 10, directly increasing throughput per invocation without changing the reserved concurrency of 5. Since the function is idempotent and can handle up to 100 messages per invocation, this change maximizes the number of messages processed per Lambda execution, reducing the backlog more efficiently.

Answer

Increase reserved concurrency to 10.

Answer

Change the function timeout to 15 minutes.

Answer

Enable SQS short polling to reduce latency.

Question 10

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.. Option D is correct because 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 11

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 12

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 13

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.. The correct answer is C because 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 14

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.. Option C is correct because 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 15

A developer is using AWS Lambda with a function that processes messages from an SQS queue. The function is configured with a batch size of 10 and reserved concurrency of 5. The queue has a large backlog, and messages are being throttled, leading to retries and eventual DLQ. The function is idempotent and can handle up to 100 messages per invocation. What is the most effective way to increase throughput without increasing throttling?

Accepted Answer

Increase both batch size to 100 and reserved concurrency to a higher value. Option C is correct because increasing both the batch size to 100 and the reserved concurrency to a higher value directly addresses the two bottlenecks: the batch size limits how many messages are processed per invocation, and reserved concurrency limits how many concurrent invocations can run. With a batch size of 10 and reserved concurrency of 5, the maximum messages processed per second is 50 (10 × 5), assuming each invocation takes 1 second. Increasing batch size to 100 allows each invocation to process more messages, reducing the number of invocations needed, while increasing reserved concurrency allows more parallel processing, together eliminating throttling without exceeding Lambda's account-level concurrency limits.

Answer

Increase reserved concurrency to 100

Answer

Increase batch size to 100

Answer

Decrease batch size to 1 and increase reserved concurrency to 50

Question 16

An AWS Lambda function that processes messages from an SQS queue is experiencing throttling (TooManyRequestsException). The function has reserved concurrency set to 100. The SQS queue has a redrive policy configured with maxReceiveCount of 5. CloudWatch metrics show that the function's concurrent executions occasionally spike to 100, and throttling occurs. The function execution time averages 2 seconds. What is the most effective way to reduce throttling?

Accepted Answer

Increase the batch size of the SQS event source mapping. Increasing the batch size allows the Lambda function to process more messages per invocation, reducing the number of concurrent executions needed to handle the same message volume. Since the function already spikes to its reserved concurrency of 100, processing more messages per batch lowers the invocation rate and thus reduces throttling without requiring additional concurrency.

Answer

Increase the reserved concurrency of the Lambda function

Answer

Decrease the batch window of the event source mapping

Answer

Add a dead-letter queue (DLQ) for the Lambda function

Question 17

A developer deployed an AWS Lambda function that is invoked by an Amazon SQS queue. The function is configured with a batch size of 10 and a timeout of 30 seconds. CloudWatch metrics show that the function's Duration is consistently around 28 seconds, but occasionally spikes to 35 seconds causing timeouts. The function makes a synchronous HTTP call to an external API. Which approach will MOST effectively prevent timeouts while maximizing throughput?

Accepted Answer

Use async HTTP calls with a callback. Option A is correct because using async HTTP calls with a callback prevents the Lambda function from blocking on the synchronous HTTP request. This allows the function to process the batch of 10 messages concurrently, reducing the overall execution time below the 30-second timeout even when individual API calls occasionally take longer. By not waiting for each response sequentially, the function maximizes throughput and avoids timeouts.

Answer

Increase the function's timeout to 60 seconds

Answer

Reduce the batch size to 5

Answer

Increase the SQS visibility timeout to 60 seconds

Question 18

A developer is troubleshooting slow response times in a serverless application. The application consists of an Amazon API Gateway REST API that invokes an AWS Lambda function, which then writes data to an Amazon DynamoDB table with on-demand capacity. The function also calls an external API for enrichment. The developer observes that the API Gateway integration latency is high, but the Lambda function duration is low. What is the most likely cause?

Accepted Answer

The API Gateway request/response payload size is large, increasing transfer time.. Option D is correct because high API Gateway integration latency combined with low Lambda duration indicates that the delay is occurring in the data transfer between API Gateway and Lambda, not within the function itself. A large request/response payload increases the network transfer time over the HTTPS connection, which is measured as part of integration latency but not reflected in the function's billed duration.

Answer

The Lambda function is experiencing cold starts due to low invocation frequency.

Answer

The DynamoDB on-demand capacity is throttling requests.

Answer

The external API call is adding network latency due to its geographic location.

Question 19

A developer is troubleshooting an AWS Lambda function that is triggered by an S3 event. The function occasionally fails with a timeout error. CloudWatch logs show that the timeout occurs during the processing of large files. The function has a memory setting of 128 MB and a timeout of 3 seconds. The developer wants to process large files without modifying the code. Which parameter should the developer adjust first?

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 20

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 correct answer is D because 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