DVA-C02 Troubleshooting and Optimization — 30 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?

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

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?

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

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

Question 11

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 auto scaling for read capacity. DynamoDB Auto Scaling automatically adjusts the provisioned read capacity units (RCUs) in response to actual traffic patterns, which directly addresses the throttling of the 100 RCU limit without manual intervention. In contrast, enabling DynamoDB Accelerator (DAX) is not a hands-off solution; it requires provisioning a DAX cluster, configuring network/security settings, and modifying the application code to use the DAX SDK instead of the standard DynamoDB SDK.

Answer

Enable DynamoDB Accelerator (DAX)

Answer

Increase the write capacity

Answer

Use a global table

Question 12

A developer is troubleshooting an application that uses Amazon ElastiCache for Redis to cache database query results. The application experiences high latency during cache misses. The developer notices that frequently accessed keys (hot keys) are often missing from the cache, suggesting they are being evicted. Which action should the developer take to reduce cache misses for hot keys?

Accepted Answer

Switch to the 'allkeys-lru' eviction policy.. The 'allkeys-lru' eviction policy allows Redis to evict any key (including those with TTL) based on least-recently-used access, which helps retain frequently accessed hot keys. By default, ElastiCache for Redis uses the 'volatile-lru' policy, which only evicts keys with an expiration set, leaving hot keys without TTL vulnerable to eviction when memory pressure occurs. Switching to 'allkeys-lru' ensures that even keys without TTL can be considered for eviction, reducing the likelihood of hot keys being removed.

Answer

Increase the number of cache nodes.

Answer

Disable the TTL on all cached keys.

Answer

Increase the size of the cache cluster.

Question 13

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?

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

Accepted Answer

Increase the Lambda function's memory. AWS Lambda allocates CPU power proportionally to the configured memory. A function configured with the minimum memory of 128 MB receives very little CPU power, which causes CPU-bound tasks like file processing to run slowly and time out. Increasing the memory allocation (Option A) increases the CPU power, which speeds up execution, resolves the timeout, and often reduces overall cost without requiring code changes. Simply increasing the timeout (Option B) is a sub-optimal approach that does not address the underlying CPU starvation.

Answer

Increase the Lambda function's timeout

Answer

Increase the S3 event notification batch size

Answer

Enable Lambda function reserved concurrency

Question 14

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?

Answer

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

Answer

The reserved concurrency is too low, causing SQS to throttle Lambda invocations.

Answer

The SQS queue visibility timeout is set too high.

Answer

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

Question 15

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. To align with AWS exam standards, a 'select two' question must have 5 options. We add a fifth distractor and refine the correct options for technical accuracy.

Refined Options:
A: Use provisioned concurrency for the Lambda function [CORRECT]
B: Move all execution logs to Amazon S3 Glacier [wrong]
C: Reduce the deployment package size and initialize SDK clients outside the function handler [CORRECT]
D: Disable all retries in the API Gateway integration settings [wrong]
E: Enable AWS X-Ray active tracing on the Lambda function [wrong]

Answer

Move all logs to S3 Glacier

Answer

Disable all retries

Question 16

An API Gateway API returns 429 errors during load testing. Which two areas should the developer investigate first?

Accepted Answer

Usage plan or stage throttling limits. A is correct because API Gateway uses usage plans and stage-level throttling to limit request rates. When a client exceeds the configured rate limit (e.g., 10,000 requests per second) or burst limit, API Gateway returns a 429 Too Many Requests error. Investigating these limits is the first step to identify if the load test is hitting predefined caps.

Answer

S3 lifecycle expiration rules

Answer

CloudFormation stack drift only

Question 17

An SQS-triggered Lambda repeatedly processes the same poison message. Which two settings help contain the issue?

Accepted Answer

Configure maxReceiveCount and a dead-letter queue. To contain and resolve the issue of poison messages in an SQS-triggered Lambda, two key configurations are used:

1. **Configure maxReceiveCount and a Dead-Letter Queue (DLQ) (Option A):** By setting a Redrive Policy with a `maxReceiveCount`, SQS will automatically move a message to a DLQ after it has been received and failed the specified number of times. This prevents the poison message from looping indefinitely.

2. **Use partial batch response / ReportBatchItemFailures (Option C):** By enabling 'ReportBatchItemFailures' on the Lambda event source mapping, the function can return a list of specific failed message IDs. SQS will only retry those failed messages (the poison message) rather than retrying the entire batch, which prevents successful messages in the same batch from being repeatedly reprocessed.

Answer

Disable CloudWatch Logs

Answer

Set message retention to zero

Question 18

Drag and drop the steps to troubleshoot an application that cannot connect to an RDS database in the correct order.

Accepted Answer

Step 1: Check network connectivity, Step 2: Check security groups, Step 3: Check credentials, Step 4: Test direct connection, Step 5: Check database logs. To troubleshoot RDS connection issues, follow a logical bottom-up approach: 
1. **Verify DB instance status**: Ensure the database is in the 'available' state.
2. **Check security groups and network ACLs**: Ensure the database security group allows inbound traffic on the database port from the application's security group or IP.
3. **Test port connectivity**: Use tools like `nc` or `telnet` to verify if the port is reachable. This isolates network/firewall issues from authentication issues.
4. **Verify database credentials**: Once network connectivity is confirmed, verify that the username, password, and database name are correct.
5. **Check database logs**: If the connection still fails or is dropped, inspect the DB logs (e.g., error logs) for engine-specific issues or connection limits.

Answer

Step 1: Check credentials, Step 2: Check security groups, Step 3: Check network connectivity, Step 4: Test direct connection, Step 5: Check database logs

Answer

Step 1: Check security groups, Step 2: Check network connectivity, Step 3: Check credentials, Step 4: Check database logs, Step 5: Test direct connection

Answer

Step 1: Check network connectivity, Step 2: Check credentials, Step 3: Check security groups, Step 4: Test direct connection, Step 5: Check database logs

Question 19

A company uses AWS CodePipeline with CodeBuild to deploy a Node.js application. The build fails intermittently with 'npm ERR! network' errors. What is the most likely cause and solution?

Accepted Answer

The build environment lacks outbound internet access; configure a NAT gateway or use a VPC endpoint for npm.. The 'npm ERR! network' error indicates that npm cannot reach the registry to download packages. If AWS CodeBuild is configured to run inside a VPC, it requires outbound internet access to reach the public npm registry. If CodeBuild is configured with multiple subnets and some of them lack a route to a NAT gateway, the builds will fail intermittently depending on which subnet the build container is provisioned in. To resolve this, ensure all configured subnets have a route to a NAT gateway, or host your packages in AWS CodeArtifact and use a VPC endpoint to access them privately.

Answer

A unit test is failing; fix the test code.

Answer

The npm cache is corrupted; clear the cache in CodeBuild.

Answer

The npm token has expired; regenerate the token.

Question 20

A developer receives an AccessDeniedException when trying to invoke a Lambda function from an Amazon API Gateway REST API. The Lambda resource-based policy allows API Gateway. What is the most likely issue?

Accepted Answer

The API Gateway execution role lacks lambda:InvokeFunction permission.. When API Gateway invokes a Lambda function, it must have the `lambda:InvokeFunction` permission. Even if the Lambda resource-based policy grants API Gateway access, the API Gateway execution role must also explicitly allow the `lambda:InvokeFunction` action. Without this permission, API Gateway cannot invoke the function, resulting in an AccessDeniedException.

Answer

The Lambda function is in a VPC without a NAT gateway.

Answer

The Lambda function concurrency limit is exceeded.

Answer

API Gateway caching is enabled, returning stale responses.