Orchestrating SageMaker ML Workflows as DAGs in Studio with ML Lineage Tracking for Governance

Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Answer Correct answer: C — SageMaker Pipelines in Studio orchestrates the workflows as DAGs, while ML Lineage Tracking records experiment history plus audit and compliance evidence.

A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements?

Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications. Correct Answer
Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Community Votes

67%

33%

67% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

SageMaker Pipelines orchestrates the workflows and renders them as DAGs inside SageMaker Studio, while SageMaker ML Lineage Tracking records the lineage of datasets, models, and artifacts to provide the experiment history and the audit and compliance evidence.

Data scientists need fine-grained control of the ML workflows they orchestrate, must visualize SageMaker jobs and workflows as a directed acyclic graph, and must keep a running history of model discovery experiments plus establish model governance for auditing and compliance. The answer has to satisfy all three needs at once.

Substituting SageMaker Experiments for ML Lineage Tracking, or AWS CodePipeline for SageMaker Pipelines. Experiments manages experiment runs and statistics but is not the lineage and governance mechanism, and CodePipeline is a general CI/CD service rather than the SageMaker-native workflow orchestrator that integrates with Studio.

Community Discussion (10 comments)

a4002bd 👍 7 Selected: D

SageMaker Pipelines provides a robust way to manage and visualize ML workflows as directed acyclic graphs (DAGs), while SageMaker Experiments helps track and manage the history of model experiments and supports model governance

GiorgioGss 👍 5 Selected: C

https://docs.aws.amazon.com/sagemaker/latest/dg/define-pipeline.html I don't see how you can manage the "entire ML flow" (as question asks) with something else other than Studio.

aws_Tamilan 👍 1 Selected: C

🔑 Keyword: Fine-grained control, DAG visualization, model governance, auditing ✅ Correct Answer: C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications. Why? SageMaker Pipelines provides workflow orchestration with DAG visualization. SageMaker ML Lineage Tracking enables experiment history and model governance for auditing. Why Others Are Wrong? ❌ A & B. AWS CodePipeline is more suited for software CI/CD than for ML workflows. ❌ D. SageMaker Experiments focuses on tracking experiments but lacks full model lineage tracking.

chris_spencer 👍 1 Selected: C

Contrary to its name, Amazon SageMaker ML Lineage Tracking does: 1. Keep a running history of model discovery experiments. 2. Establish model governance by tracking model lineage artifacts for auditing and compliance verification. Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

abrarjahin 👍 1 Selected: D

SageMaker Pipelines handles the orchestration of workflows with fine-grained control. SageMaker Experiments provides the necessary tracking, organization, and governance features for experiments and compliance.

fnuuu 👍 3 Selected: C

ML Lineage for Audit/Compliance, Studio for DAG, SM Pipeline for entire workflow

khchan123 👍 4 Selected: C

The correct answer is C. Options B and D suggest using SageMaker Experiments, which is good for tracking experiments but doesn't provide the comprehensive lineage tracking and governance features that ML Lineage Tracking offers. Running history of experiments: SageMaker ML Lineage Tracking provides a comprehensive way to track the lineage of ML workflows, including datasets, algorithms, hyperparameters, and models. This fulfills the requirement for keeping a running history of model discovery experiments. Model governance for auditing and compliance: ML Lineage Tracking also supports model governance by providing detailed information about each step in the ML process, which is crucial for auditing and compliance verifications.

jackzhang846 👍 2 Selected: D

SageMaker Experiments https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html

jackzhang846 👍 2 Selected: C

SageMaker ML Lineage Tracking 提供模型和数据的血缘追踪功能，支持审计和合规性。

Saransundar 👍 4 Selected: C

https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html With SageMaker AI Lineage Tracking data scientists and model builders to Keep a running history of model discovery experiments. Establish model governance by tracking model lineage artifacts for auditing and compliance verification.

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Expert Analysis

Why the Answer Is Correct

The three requirements map onto specific SageMaker features. SageMaker Pipelines is the SageMaker-native workflow orchestrator and its integration with SageMaker Studio provides the fine-grained control and the directed acyclic graph visualization of jobs and workflows. For the running history of model discovery experiments and for model governance that supports auditing and compliance verification, SageMaker ML Lineage Tracking is the dedicated feature, because it tracks lineage across datasets, models, and artifacts. Option C is the only choice that satisfies all three requirements together. The vote was 67 for C, with khchan123, Saransundar, fnuuu, and aws_Tamilan all selecting C and citing the official define-pipeline and lineage-tracking documentation, while GiorgioGss pointed out that the entire ML flow is managed through Studio.

Why the Other Options Are Wrong

Options B and D both use SageMaker Experiments where the question requires the running experiment history and governance. Experiments tracks experiment runs, hyperparameters, and metrics, but it is not the lineage and governance mechanism that captures artifact lineage for auditing and compliance, as khchan123 argued. Options A and C are the correct lineage pairing, but option A uses AWS CodePipeline, which is a general-purpose CI/CD service for building and deploying software, not the SageMaker-native pipeline orchestrator that integrates with Studio to render ML workflows as DAGs.

Community Comment Notes

The community split 67 for C and 33 for D. a4002bd, who voted D, credited SageMaker Pipelines for DAG visualization and SageMaker Experiments for tracking experiment history, but did not address the governance and compliance requirement that specifically calls for lineage tracking. jackzhang846 addressed both sides in the same thread, citing the Experiments page for D and noting in Chinese that SageMaker ML Lineage Tracking supplies model and data lineage for audit and compliance, which points back to C.

Official Reference

https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

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