MLA-C01 · Question #4
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central mod
The correct answer is A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.. Option A is correct because Amazon SageMaker Clarify is the purpose-built AWS tool for detecting bias in ML models, and invoking it via a Lambda function enables on-demand (workflow-triggered) execution - exactly what the requirement specifies. Clarify integrates natively with Sa
Question
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application. Which action will meet this requirement?
Options
- AConfigure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
- BInvoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in
- CUse AWS Glue Data Quality to monitor bias.
- DUse SageMaker notebooks to compare the bias.
How the community answered
(46 responses)- A80% (37)
- B7% (3)
- C11% (5)
- D2% (1)
Explanation
Option A is correct because Amazon SageMaker Clarify is the purpose-built AWS tool for detecting bias in ML models, and invoking it via a Lambda function enables on-demand (workflow-triggered) execution - exactly what the requirement specifies. Clarify integrates natively with SageMaker endpoints and can run bias drift analysis against deployed real-time models.
Why the distractors fail:
- B - The
sagemaker-model-monitor-analyzercontainer handles data quality and model quality drift, not bias drift specifically; it also requires a monitoring schedule, not on-demand invocation. - C - AWS Glue Data Quality is designed for ETL pipeline data validation, not ML model bias monitoring against deployed endpoints.
- D - SageMaker notebooks can run ad hoc analysis but are not a scalable, automated workflow mechanism; they require manual execution and aren't suited for triggering on-demand monitoring pipelines.
Memory tip: Think "Clarify = fairness/bias, Model Monitor = drift/quality." Whenever a question asks about bias specifically (not generic drift), Clarify is your anchor - and Lambda is AWS's standard glue for triggering on-demand workflows without a persistent schedule.
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