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Exam Professional Machine Learning Engineer topic 1 question 99 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 99
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

  • A. Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
  • B. Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
  • C. Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
  • D. Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.
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Suggested Answer: D 🗳️
by mymy9418 at Dec. 17, 2022, 10 a.m.

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Pau1234
8 months ago
Selected Answer: B
https://cloud.google.com/vertex-ai/docs/evaluation/introduction
upvoted 2 times
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pinimichele01
1 year, 3 months ago
Selected Answer: B
https://cloud.google.com/vertex-ai/docs/model-registry/versioning Model versioning lets you create multiple versions of the same model. With model versioning, you can organize your models in a way that helps navigate and understand which changes had what effect on the models. With Vertex AI Model Registry you can view your models and all of their versions in a single view. You can drill down into specific model versions and see exactly how they performed.
upvoted 1 times
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gscharly
1 year, 3 months ago
Selected Answer: B
agree with pico
upvoted 1 times
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Mickey321
1 year, 8 months ago
Selected Answer: B
either B or D so leaning towards B
upvoted 1 times
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pico
1 year, 10 months ago
Selected Answer: B
Vertex AI provides a managed environment for machine learning, and creating model versions for each season per year is a structured way to organize and compare models. You can use the Evaluate tab to compare performance metrics easily. This approach is well-suited for the task.
upvoted 2 times
pico
1 year, 10 months ago
not D: Vertex ML Metadata is designed for tracking metadata and lineage in machine learning pipelines. While it can store model version information and performance statistics, it might not provide as straightforward a way to compare models across years and seasons as Vertex AI's model versioning and evaluation tools.
upvoted 1 times
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andresvelasco
1 year, 10 months ago
Selected Answer: D
I absolutely do not master this topicm but I would say correct answer is D. It does not sound right to systematically create versions of a model beased on seasonality, if the model has not changed. "Events" in metadata sound right.
upvoted 2 times
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PST21
2 years, 1 month ago
Ans D- With Vertex ML Metadata, you can store the performance statistics of each version of your models as events. You can associate these events with specific seasons and years, making it easy to organize and retrieve the data based on the relevant time periods. By storing performance statistics as events, you can capture the necessary information for comparing model versions across years.
upvoted 2 times
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Voyager2
2 years, 1 month ago
Selected Answer: D
D. Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices. https://cloud.google.com/vertex-ai/docs/ml-metadata/analyzing#filtering Which versions of a trained model achieved a certain quality threshold?
upvoted 1 times
pico
1 year, 10 months ago
https://cloud.google.com/vertex-ai/docs/evaluation/using-model-evaluation#console
upvoted 1 times
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M25
2 years, 2 months ago
Selected Answer: D
Went with D
upvoted 1 times
iskorini
2 years, 2 months ago
why choose D instead of B?
upvoted 1 times
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CloudKida
2 years, 2 months ago
Selected Answer: B
https://cloud.google.com/vertex-ai/docs/model-registry/versioning Model versioning lets you create multiple versions of the same model. With model versioning, you can organize your models in a way that helps navigate and understand which changes had what effect on the models. With Vertex AI Model Registry you can view your models and all of their versions in a single view. You can drill down into specific model versions and see exactly how they performed.
upvoted 1 times
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Yajnas_arpohc
2 years, 4 months ago
Selected Answer: B
You can compare evaluation results across different models, model versions, and evaluation jobs --> https://cloud.google.com/vertex-ai/docs/evaluation/using-model-evaluation Metadata mgmt has a very different purpose
upvoted 1 times
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TNT87
2 years, 4 months ago
Selected Answer: D
Answer D
upvoted 1 times
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hiromi
2 years, 7 months ago
Selected Answer: D
D - https://cloud.google.com/vertex-ai/docs/ml-metadata/introduction
upvoted 2 times
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mil_spyro
2 years, 7 months ago
Selected Answer: D
Vote D. It is easy to compare via Vertex ML Metadata UI the performance statistics across the different slices and see how the model performance varies over time.
upvoted 2 times
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mymy9418
2 years, 7 months ago
Selected Answer: D
i think it is D
upvoted 1 times
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