DVA-C02 Mock Exam 76 — 25 Questions

Question 1

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

Question 2

A developer is using AWS X-Ray to trace a microservices application. The trace shows that a downstream service is failing with HTTP 500 errors intermittently. The developer wants to set up trace annotations to capture the error details for further analysis. Which AWS service can the developer use to search and filter traces based on these annotations?

Accepted Answer

AWS X-Ray console search. Option B is correct because AWS X-Ray's console search feature allows developers to query traces using annotations and metadata that are indexed by X-Ray. Annotations are key-value pairs attached to trace segments, and the X-Ray console search can filter traces based on these annotations, enabling the developer to isolate the intermittent HTTP 500 errors and analyze their details.

Answer

Amazon CloudWatch Logs Insights

Answer

Amazon Athena queries on X-Ray data

Answer

Amazon Kinesis Data Analytics

Question 3

A developer is using Amazon DynamoDB and notices that read requests are frequently throttled. The table has provisioned read capacity of 100 read capacity units (RCUs) and is used by a web application that experiences bursty traffic. The developer wants to minimize throttling without manual intervention. Which action should the developer take?

Accepted Answer

Enable DynamoDB Accelerator (DAX). Enabling DynamoDB Accelerator (DAX) reduces the number of read requests hitting the table by serving them from an in-memory cache. This offloads read traffic, effectively reducing the consumed RCUs and minimizing throttling during bursty traffic without requiring manual intervention or capacity changes.

Answer

Increase the write capacity

Answer

Use a global table

Answer

Enable auto scaling for read capacity

Question 4

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 5

A developer notices that an AWS Lambda function processing S3 events is being retried frequently due to throttling errors from Amazon DynamoDB. The function writes records to a DynamoDB table and has reserved concurrency set to 100. The DynamoDB table uses on-demand capacity mode. What should the developer do to reduce retries and improve overall throughput?

Accepted Answer

Implement exponential backoff and retry in the Lambda function code for DynamoDB API calls.. Option B is correct because implementing exponential backoff and retry in the Lambda function code for DynamoDB API calls directly addresses the throttling errors. Even with on-demand capacity, DynamoDB can throttle requests if they exceed the table's burst capacity or if there are hot partitions. Exponential backoff reduces the retry rate, allowing DynamoDB to recover and improving overall throughput without changing the Lambda concurrency or capacity mode.

Answer

Increase the Lambda function's reserved concurrency to 500.

Answer

Disable the Lambda function's S3 event source mapping and use Amazon SQS to buffer events.

Answer

Switch the DynamoDB table to provisioned capacity with a high write capacity unit setting.

Question 6

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 7

A developer has deployed an AWS Lambda function that is triggered by an Amazon S3 event. The function processes image files and stores metadata in an Amazon DynamoDB table. CloudWatch metrics show that the function's error count has increased. The developer checks CloudWatch Logs and sees errors related to insufficient memory. The function is configured with 128 MB of memory. What should the developer do to resolve the errors?

Accepted Answer

Increase the function's memory to 256 MB or higher.. The error is caused by insufficient memory, which directly impacts the CPU and execution resources allocated to the Lambda function. Increasing the memory allocation to 256 MB or higher provides more CPU throughput and memory, resolving the out-of-memory errors without requiring code changes.

Answer

Increase the function's timeout to 30 seconds.

Answer

Reduce the size of the images being uploaded to S3.

Answer

Move the DynamoDB write operation to an asynchronous invocation.

Question 8

A developer has an AWS Lambda function that processes messages from an Amazon SQS queue. The function is configured with a reserved concurrency of 5. Recently, the SQS queue has experienced a high volume of messages, and the developer notices that many invocations are being throttled, leading to increased processing time. What is the most likely cause of the throttling?

Accepted Answer

The reserved concurrency is too low, causing SQS to throttle Lambda invocations.. The correct answer is B because reserved concurrency limits the maximum number of concurrent executions for a Lambda function. When the SQS queue has a high volume of messages, Lambda attempts to scale up to process them, but with a reserved concurrency of 5, it can only run 5 concurrent invocations. Any additional invocation requests are throttled with a 429 error, causing messages to remain in the queue and increasing processing time.

Answer

The function's execution role lacks permissions to invoke the function.

Answer

The SQS queue visibility timeout is set too high.

Answer

The Lambda function has a VPC configuration that causes cold starts.

Question 9

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 10

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 correct answer is B because 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 11

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 12

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 13

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 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

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 16

A Lambda function reading from Kinesis is falling behind. Which two metrics/settings should be reviewed first?

Accepted Answer

IteratorAge for the event source mapping. The IteratorAge metric measures how far behind the Lambda function is in processing records from the Kinesis stream. A high IteratorAge indicates the function is falling behind, making it the primary metric to review. The batch size, parallelization factor, and shard count directly control the concurrency and throughput of the event source mapping, so adjusting these settings can help catch up.

Answer

S3 bucket public access settings

Answer

Route 53 hosted zone count

Question 17

A Lambda function behind API Gateway intermittently times out only during cold starts. Which two actions can reduce cold-start impact?

Accepted Answer

Use provisioned concurrency for predictable low latency. Provisioned concurrency keeps a specified number of Lambda execution environments initialized and ready to respond immediately, eliminating the cold-start latency for those invocations. This is the most direct way to ensure predictable low latency for a function that intermittently times out during cold starts.

Answer

Move all logs to S3 Glacier

Answer

Disable all retries

Question 18

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 19

A Lambda function using a Kinesis event source repeatedly retries one bad record and blocks progress in the shard. Which feature helps isolate failed records after retry limits?

Accepted Answer

Configure failure handling with bisect batch on error and an on-failure destination where supported. Option C is correct because Lambda's Kinesis event source mapping supports a 'bisect batch on error' feature that splits a failed batch into two smaller batches, allowing the bad record to be isolated and retried separately. Additionally, configuring an on-failure destination (e.g., an SQS queue or SNS topic) sends the record to a dead-letter destination after the retry limit is exhausted, preventing the shard from blocking progress.

Answer

Increase memory to 10 GB only

Answer

Disable batch processing

Answer

Convert the stream to an S3 bucket

Question 20

A developer needs to trace a request across API Gateway, Lambda, and downstream AWS service calls. Which service should be enabled?

Accepted Answer

AWS X-Ray. AWS X-Ray is the correct service because it provides end-to-end tracing for requests flowing through distributed applications, including API Gateway, Lambda functions, and downstream AWS services like DynamoDB or S3. It captures trace data as the request traverses each component, allowing developers to identify performance bottlenecks and errors across the entire request path. X-Ray integrates natively with API Gateway and Lambda via the X-Ray SDK or active tracing configuration, requiring no code changes for basic tracing.

Answer

AWS Budgets

Answer

AWS Artifact

Answer

AWS License Manager