AWS Certified Data Engineer Associate DEA-C01 Practice Question
An online gaming company delivers about 5 MB/s of gameplay telemetry to AWS. The engineering team must store each record for 7 days, support millisecond-latency writes and multiple parallel reads, and invoke AWS Lambda functions that calculate near-real-time leaderboards. They want the lowest operational overhead and predictable pricing. Which service should they use as the primary data store?
Insert each record into an on-demand Amazon DynamoDB table and export the table to Amazon S3 after 7 days.
Send the data to Amazon S3 through Kinesis Data Firehose and have Lambda query the objects with Amazon Athena.
Deploy an Amazon MSK cluster and write the telemetry to a Kafka topic configured for 7-day retention.
Create an Amazon Kinesis Data Streams stream with a 7-day retention period and configure AWS Lambda as a consumer.
Amazon Kinesis Data Streams is purpose-built for high-throughput, low-latency ingestion. A stream shard provides single-digit-millisecond put and get latency, and the service can retain data for up to 365 days when the retention period is extended, so a 7-day requirement is easily met. Lambda can be configured as an event source, allowing records to be processed almost immediately after they are written, and the fully managed nature of the service eliminates cluster management while costs scale linearly with provisioned shard capacity.
Writing directly to Amazon S3 via Kinesis Data Firehose meets the retention goal but cannot provide millisecond-level reads or support multiple independent consumer applications in real time. Persisting each record in DynamoDB gives low-latency access, but costs rise rapidly for write-heavy workloads and the service is not optimized for sequential stream processing by multiple consumers. Amazon MSK offers Kafka-compatible streams with the required retention but introduces significant operational burden to manage brokers, scaling, and patching, which contradicts the low-overhead requirement.
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What is the primary purpose of Amazon Kinesis Data Streams?
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How does AWS Lambda integrate with Amazon Kinesis to support near-real-time processing?
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Why is operational overhead lower with Amazon Kinesis compared to Amazon MSK?
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Why is Amazon Kinesis Data Streams preferred over Amazon MSK in this use case?
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How does Kinesis Data Streams achieve millisecond-latency access for reads and writes?
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What is the benefit of using AWS Lambda as a consumer with Kinesis Data Streams?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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