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Data Operations and SupporthardMultiple ChoiceObjective-mapped

DEA-C01 Data Operations and Support Practice Question

A data pipeline uses AWS Lambda to process records from an Amazon Kinesis Data Stream. The Lambda function is idempotent and runs once per record. Recently, the function started failing with 'ProvisionedThroughputExceededException' when writing to a DynamoDB table. Which action should the data engineer take to resolve this?

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

Implement retry logic with exponential backoff in the Lambda function.

Implementing retry logic with exponential backoff allows the Lambda function to handle transient 'ProvisionedThroughputExceededException' errors by retrying write operations with increasing delays, which reduces the load on DynamoDB and gives the table capacity to recover. Since the function is idempotent, retries are safe. Option A is incorrect: decreasing the batch size reduces the number of records per invocation but does not directly address DynamoDB throttling; it may even increase the number of concurrent invocations, potentially worsening the issue. Option B is incorrect: increasing reserved concurrency allows more Lambda functions to run concurrently, which would increase the write rate to DynamoDB, exacerbating throttling. Option D is incorrect: increasing Kinesis shards increases the stream's ingestion capacity but does not affect DynamoDB's throughput limits; the bottleneck is at the DynamoDB table, not the stream.

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 Lambda function's batch size to process fewer records per invocation.

    Why it's wrong here

    Smaller batch size means more invocations, increasing DynamoDB write attempts.

  • Increase the Lambda function's reserved concurrency.

    Why it's wrong here

    More concurrency means more writes to DynamoDB, making throttling worse.

  • Implement retry logic with exponential backoff in the Lambda function.

    Why this is correct

    Exponential backoff reduces the write rate when throttled, eventually succeeding.

  • Increase the number of shards in the Kinesis stream.

    Why it's wrong here

    More shards increase the rate of Lambda invocations, not DynamoDB throughput.

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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