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Data Operations and SupporthardMultiple ChoiceObjective-mapped

DEA-C01 Data Operations and Support Practice Question

A company runs a data pipeline that ingests user activity logs from an API gateway into an Amazon Kinesis Data Firehose delivery stream. The Firehose stream writes data to an S3 bucket. The data is then processed by a scheduled AWS Glue job that runs every hour. Recently, the company noticed that the data in S3 is incomplete: some logs from the API are missing. The Glue job processes all files in the S3 bucket. The Firehose stream has a buffer size of 5 MB and a buffer interval of 60 seconds. The API sends data at a rate of approximately 2 MB per minute. What should the company do to reduce data loss?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Configure a Dead Letter Queue (DLQ) for the Firehose stream.

Configuring a Dead Letter Queue (DLQ) for the Kinesis Data Firehose delivery stream allows failed records to be captured and stored for later analysis and reprocessing. While Firehose automatically retries delivery, persistent failures can lead to data loss. A DLQ (or error backup destination) ensures that records that cannot be delivered are preserved. Option A: Decreasing the buffer interval from 60 to 30 seconds would cause more frequent deliveries, increasing the likelihood of transient failures affecting smaller batches and not reducing overall data loss. Option B: Increasing the buffer size to 10 MB does not prevent data loss; it only changes the threshold for flushing data. Since the data rate is 2 MB/min, the buffer size is rarely the trigger (interval is more likely). Option D: Enabling data transformation with AWS Lambda to compress data reduces the size of records but does not address delivery failures; it could even introduce additional failure points from the Lambda function. Thus, only C directly addresses data loss by capturing failed records.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Decrease the buffer interval to 30 seconds.

    Why it's wrong here

    This increases write frequency and may increase chance of failure.

  • Increase the Firehose buffer size to 10 MB.

    Why it's wrong here

    This may increase buffering but does not prevent data loss if delivery fails.

  • Configure a Dead Letter Queue (DLQ) for the Firehose stream.

    Why this is correct

    A DLQ captures failed deliveries so data can be reprocessed.

  • Enable data transformation with AWS Lambda to compress data.

    Why it's wrong here

    Compression does not prevent data loss.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.