Exam Professional Machine Learning Engineer topic 1 question 199 discussion

My Answer: D According with: https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments “Vertex AI Experiments is a tool that helps you track and analyze different model architectures, hyperparameters, and training environments, letting you track the steps, inputs, and outputs of an experiment run. Vertex AI Experiments can also evaluate how your model performed in aggregate, against test datasets, and during the training run. You can then use this information to select the best model for your particular use case.”. Considering that both options A and B could demonstrate some form of lineage, I believe option D is the most suitable. The text explicitly states "show lineage for your model and predictions," which aligns perfectly with the functionality provided by Vertex AI Experiments.

Vertex AI Pipelines are suited to do artifact lineage https://cloud.google.com/vertex-ai/docs/pipelines/lineage Experiments can do it also, but their main goal is to "track and analyze different model architectures, hyperparameters, and training environments"

Vertex AI Experiments helps track all your training runs, including: Dataset version Hyperparameters Model metrics Code version This enables full lineage and traceability from data → training → model artifact.

Answer is B D is wrong as there is only one model not models, experiments is used for multiple runs of a model/multiple models, also lineage is tracked using a pipeline

Vertex AI Pipelines will track the Model lineage while the batch prediction component in Vertex AI Pipelines will provide lineage tracking because each prediction is part of the pipeline and is connected to the corresponding training process.

Vertex AI Pipeline for lineage tracking

My answer is B.

It's a bit ambiguously worded this question. Model lineage involves knowledge of the data it was trained on, so that should be A. That being said, I think the question is implying D from it's wording, experiment tracking. I went for A, but suspect it's wrong.

Option A/B doesn't mention anything about lineage. C is definitely wrong as there is no need to upload the dataset to Bigquery. Only correct answer is D

running your custom model in production -> need pipeline -> B

Agree with guilhermebutzke

A because to track lineage you need a managed dataset and vertex ai pipelines

A D cannot provide lineage for the source of your data. Has to be A to go with Vertex AI managed dataset.

Managed data set to help track lineage https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets

B) REF https://cloud.google.com/vertex-ai/docs/pipelines/lineage Track the lineage of pipeline artifacts When you run a pipeline using Vertex AI Pipelines, the artifacts and parameters of your pipeline run are stored using Vertex ML Metadata. Vertex ML Metadata makes it easier to analyze the lineage of your pipeline's artifacts, by saving you the difficulty of keeping track of your pipeline's metadata. An artifact's lineage includes all the factors that contributed to its creation, as well as artifacts and metadata that are derived from this artifact. For example, a model's lineage could include the following: The training, test, and evaluation data used to create the model. The hyperparameters used during model training. Metadata recorded from the training and evaluation process, such as the model's accuracy. Artifacts that descend from this model, such as the results of batch predictions.

D. Sample on how to keep track of experiments lineage -> https://cloud.google.com/vertex-ai/docs/experiments/user-journey/uj-model-training

Vertex AI Pipelines provides ability to track the lineage for your model and predictions