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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 streaming data via Amazon Kinesis Data Streams, processes it with an AWS Lambda function, and stores results in Amazon DynamoDB. The Lambda function sometimes fails due to 'ProvisionedThroughputExceededException' on the DynamoDB table. Which combination of steps should a data engineer take to resolve this issue?

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

Enable DynamoDB auto scaling and configure a dead-letter queue for the Lambda function.

Enabling DynamoDB auto scaling allows the table to adjust its provisioned throughput based on actual traffic patterns, which helps prevent 'ProvisionedThroughputExceededException' when the Lambda function writes to DynamoDB. Additionally, configuring a dead-letter queue (DLQ) for the Lambda function ensures that records that fail due to throttling are captured and can be reprocessed later, preventing data loss. Option B is incorrect because increasing the Lambda function timeout does not address DynamoDB throughput limits. Option C is incorrect because increasing the number of Kinesis shards may increase the rate of Lambda invocations, potentially worsening the throttling issue. Option D is incorrect because increasing Lambda reserved concurrency could allow more concurrent invocations, which may exacerbate throughput exceedance, and disabling retries would cause data loss.

Answer analysis

Option-by-option breakdown

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

  • Enable DynamoDB auto scaling and configure a dead-letter queue for the Lambda function.

    Why this is correct

    Auto scaling adjusts throughput; DLQ captures failed records for reprocessing.

  • Increase the Lambda function timeout and enable batch windows.

    Why it's wrong here

    Timeout increase does not reduce DynamoDB request rate.

  • Increase the number of Kinesis shards to reduce Lambda invocations.

    Why it's wrong here

    More shards increase Lambda invocations, potentially worsening throttling.

  • Increase Lambda reserved concurrency and disable retries.

    Why it's wrong here

    Disabling retries may cause data loss.

Visual reference

Client Recursive Resolver Root DNS (13 root servers) TLD DNS (.com, .org, …) Authoritative example.com query IP addr answer

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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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.