20+ practice questions focused on Troubleshooting and Optimization — one of the most tested topics on the AWS Certified Developer Associate DVA-C02 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Troubleshooting and Optimization PracticeA 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?
Explanation: 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.
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?
Explanation: 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.
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?
Explanation: 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.
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?
Explanation: 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.
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?
Explanation: 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.
+15 more Troubleshooting and Optimization questions available
Practice all Troubleshooting and Optimization questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Troubleshooting and Optimization. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Troubleshooting and Optimization questions on the DVA-C02 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Troubleshooting and Optimization is tested as part of the AWS Certified Developer Associate DVA-C02 blueprint. Practicing with targeted Troubleshooting and Optimization questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Troubleshooting and Optimization is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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