DVA-C02 Troubleshooting and Optimization — Timed 10 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 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?

10 minute time limit — choose an answer to begin.

Accepted Answer

Increase the Lambda function memory and CPU allocation.. 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 2

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 reserved concurrency to 100. For SQS FIFO queues, the maximum batch size allowed for a Lambda event source mapping is 10. Therefore, options B and C are incorrect because they attempt to increase the batch size to 100, which is unsupported for FIFO queues. To resolve the throttling and increase throughput, the reserved concurrency of the Lambda function must be increased (Option A), allowing more concurrent executions to process the backlog.

Answer

Increase batch size to 100

Answer

Increase both batch size to 100 and reserved concurrency to a higher value

Answer

Decrease batch size to 1 and increase reserved concurrency to 50

Question 3

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 reserved concurrency of the Lambda function. When an AWS Lambda function processing SQS messages experiences throttling because it reaches its reserved concurrency limit (100), the most direct and effective solution is to increase the reserved concurrency limit. Increasing the batch size (Option A) does not reduce the concurrency requirement because processing more messages per batch increases the execution time of each invocation proportionally, keeping the overall concurrency (Invocations per second * Execution duration) the same. Therefore, increasing the reserved concurrency is the correct solution.

Answer

Increase the batch size of the SQS event source mapping

Answer

Decrease the batch window of the event source mapping

Answer

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

Question 4

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

Reduce the batch size to 5. Reducing the batch size to 5 is the most effective and simplest solution. Since the function currently takes ~28 seconds to process 10 messages (about 2.8 seconds per message), reducing the batch size to 5 will lower the average execution time to approximately 14 seconds. This is well below the 30-second Lambda timeout, preventing timeouts without requiring any code changes.

Increasing the Lambda timeout to 60 seconds (Option B) or SQS visibility timeout to 60 seconds (Option D) in isolation is incorrect because AWS recommends setting the SQS visibility timeout to at least 6 times the Lambda function timeout to prevent duplicate processing. Option A is incorrect because making asynchronous calls with callbacks in Lambda can lead to the function exiting before the callbacks complete, or requires complex concurrency management and code refactoring.

Answer

Use async HTTP calls with a callback

Answer

Increase the function's timeout to 60 seconds

Answer

Increase the SQS visibility timeout to 60 seconds

Question 5

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 Lambda function is experiencing cold starts due to low invocation frequency.. API Gateway integration latency measures the total time from when API Gateway sends the request to the backend (Lambda) until it receives the response. This includes the Lambda service's container initialization time (cold start). However, the Lambda 'Duration' metric only measures the execution time of the handler code itself, excluding the initialization phase. Therefore, when a function experiences cold starts, the integration latency is high while the Lambda duration remains low.

Answer

The DynamoDB on-demand capacity is throttling requests.

Answer

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

Answer

The API Gateway request/response payload size is large, increasing transfer time.

Question 6

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?

10 minute time limit — choose an answer to begin.

Accepted Answer

The function is writing to a single partition key, causing hot partition throttling. The most likely cause is that the function is writing to a single partition key, creating a 'hot partition'. In DynamoDB, provisioned throughput (500 WCUs) is divided across physical partitions. If a table has multiple partitions (due to data size or index structure), each partition is allocated a fraction of the total provisioned throughput. When all writes target a single partition key, that specific partition's allocated throughput is quickly exhausted, leading to ProvisionedThroughputExceededException even though the overall table-level capacity of 500 WCUs is not fully utilized.

Answer

The function's reserved concurrency is too high, causing excessive write requests

Answer

The table's read capacity is insufficient, causing write throttling

Answer

The function is exceeding the DynamoDB item size limit of 400 KB

Question 7

A developer is monitoring an AWS Lambda function that processes events from an Amazon Kinesis stream. The function's CloudWatch metrics show high IteratorAge and the function is often throttled. The function's batch size is 100, maximum record age is 60s, and reserved concurrency is 100. The Kinesis stream has 10 shards, each with 5000 records/sec. Which action is most effective to reduce the IteratorAge and throttle rate?

Accepted Answer

Increase the batch size to 1000. The Lambda function is experiencing high IteratorAge and is being throttled. Throttling indicates that the function is hitting its reserved concurrency limit of 100. If we increase the number of shards (Option B), the number of concurrent executions will increase even further, worsening the throttling issue because the reserved concurrency is capped at 100. By increasing the batch size to 1000 (Option A), the function processes 10x more records per invocation. This significantly reduces the number of invocations required to process the stream, which lowers the concurrent execution count (resolving the throttling) and processes the backlog faster (reducing the IteratorAge).

Answer

Increase the number of shards

Answer

Decrease the maximum record age

Answer

Increase the function's memory and CPU allocation

Question 8

A developer is troubleshooting an AWS Lambda function that processes streaming data from Amazon Kinesis Data Streams. The function processes records in batches. The developer notices that the function often experiences high latency even though the average invocation rate is well below the account concurrency limit. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. When a Lambda function processes an Amazon Kinesis data stream, the concurrency of the Lambda function is limited by the number of shards in the stream (one concurrent execution per shard by default). If the function experiences high latency (often manifested as an increase in the IteratorAge metric), it means the Lambda function cannot keep up with the stream's write throughput. Increasing the number of shards increases the parallelization of the Lambda function, allowing more records to be processed concurrently and effectively reducing processing latency. Provisioned concurrency only addresses cold starts, which does not resolve ongoing processing latency in a continuous stream.

Answer

Increase the batch size for the Kinesis event source mapping.

Answer

Enable reserved concurrency for the function.

Answer

Use provisioned concurrency for the function.

Question 9

A developer is monitoring an AWS Lambda function that processes messages from an SQS queue. CloudWatch metrics show that the function's throttles are high when the queue backlog grows. The function has a reserved concurrency of 50 and a batch size of 10. The SQS queue has a visibility timeout of 30 seconds. The function processes each batch in about 5 seconds. Which action will most effectively reduce throttles?

Accepted Answer

Increase the Lambda function's reserved concurrency. When an SQS-triggered Lambda function has a reserved concurrency limit (50) and the queue backlog grows, Lambda attempts to scale up the number of concurrent executions to drain the queue. If the required concurrency exceeds the reserved limit, Lambda will throttle the invocations. Increasing the reserved concurrency (Option B) directly allows Lambda to scale higher and handle the backlog without throttling. Increasing the batch size (Option C) is risky because the function currently takes 5 seconds to process 10 messages. If the batch size is increased significantly, the processing time could easily exceed the queue's 30-second visibility timeout, causing messages to become visible again and be reprocessed, leading to duplicate processing and further throttling.

Answer

Increase the SQS queue visibility timeout

Answer

Increase the Lambda function's batch size

Answer

Decrease the SQS queue message retention period

Question 10

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. AWS X-Ray's console search feature allows developers to query traces using annotations, which are key-value pairs indexed by X-Ray specifically for filtering. Unlike metadata (which is not indexed and cannot be searched), annotations can be used in filter expressions in the X-Ray console 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