DOP-C02 Question 99

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  B. Scaling EC2 instances based on metrics is valid but requires manual infrastructure management, which is less efficient than serverless. C is correct: AWS Lambda integrates natively with Kinesis Data Streams and automatically scales with the number of shards. It removes the need to manage EC2 instances, reducing operational overhead. Lambda handles parallel processing of stream records and automatically polls the stream. You can configure batch size and parallelization factor to optimize throughput. Built-in checkpointing ensures records are not lost or reprocessed unnecessarily. This approach provides high scalability, low maintenance, and cost efficiency, which aligns with the requirement for operational efficiency.

• Chose B1

  The GetRecords.IteratorAgeMilliseconds metric in Amazon Kinesis Data Streams measures the age of the last record in all GetRecords calls made against a Kinesis stream. This age is calculated as the difference between the current time and when the last record of the GetRecords call was written to the stream. This metric is crucial for monitoring the latency of your Kinesis consumer applications. If the IteratorAgeMilliseconds value is increasing, it could indicate issues such as slow record processing, read throttles, or connection timeouts. Monitoring this metric helps ensure that your consumer applications are processing records in a timely manner and not falling behind.

• Chose B1

  Option B because: 1. It uses GetRecords.IteratorAgeMilliseconds metric - This metric indicates how far behind the consumer is processing - Perfect indicator for when scaling is needed - Automatically detects processing lag 2. Horizontal scaling of EC2 consumers - Adds processing capacity when needed - Can scale down when demand decreases - Maintains existing application architecture 3. Increasing retention period - Provides buffer against temporary processing delays - Prevents data loss during scaling events - Default is 24 hours, can be increased up to 365 days