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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 124 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 124
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

  • A. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
  • B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
  • C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  • D. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
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Suggested Answer: B 🗳️

Comments

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fitri001
6 months ago
Selected Answer: B
TFX for TensorFlow Models: TensorFlow Extended (TFX) is an end-to-end machine learning platform built on top of TensorFlow. It provides a set of pre-built components specifically designed for TensorFlow models, simplifying development and ensuring compatibility. Vertex AI Pipelines for Orchestration: Vertex AI Pipelines, a managed service from Google Cloud, is ideal for orchestrating ML pipelines. It integrates seamlessly with TFX and provides features like monitoring, scheduling, and scaling, reducing infrastructure maintenance needs.
upvoted 4 times
fitri001
6 months ago
A. Kubeflow Pipelines with Predefined Components: While Kubeflow Pipelines offer a DSL for building pipelines, using standard TFX components within Vertex AI Pipelines offers a more streamlined solution designed for TensorFlow models. C & D. Kubeflow Pipelines with Manual Deployment: Both options involve using Kubeflow Pipelines, but deploying it on Google Kubernetes Engine requires additional infrastructure management compared to using the managed service, Vertex AI Pipelines.
upvoted 4 times
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NickHapton
1 year, 3 months ago
B. why not C? as the question content mentioned, this model is built by tensorflow
upvoted 2 times
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SamuelTsch
1 year, 3 months ago
Selected Answer: B
B should be correct
upvoted 2 times
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M25
1 year, 5 months ago
Selected Answer: B
Went with B
upvoted 3 times
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JamesDoe
1 year, 6 months ago
Selected Answer: B
B. Straight from the docs: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk
upvoted 4 times
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TNT87
1 year, 7 months ago
Selected Answer: B
B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines. TFX provides a set of standard components for building end-to-end ML pipelines, including data validation and model analysis. Vertex AI Pipelines is a fully managed service for building and orchestrating machine learning pipelines on Google Cloud.
upvoted 2 times
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ares81
1 year, 9 months ago
Selected Answer: B
It's B!
upvoted 2 times
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egdiaa
1 year, 10 months ago
Selected Answer: B
Reference: https://www.tensorflow.org/tfx/guide/tfdv
upvoted 3 times
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hiromi
1 year, 10 months ago
Selected Answer: B
B (not sure)
upvoted 3 times
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B (20%)
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