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Google Professional Machine Learning Engineer Exam - Topic 1 Question 103 Discussion

You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
D) Create an experiment in Kubeflow Pipelines to organize multiple runs
A) Create multiple models using AutoML Tables
B) Automate multiple training runs using Cloud Composer
C) Run multiple training jobs on Al Platform with similar job names

Google Professional Machine Learning Engineer Exam - Topic 1 Question 103 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 103
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: D

Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters.You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups.You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.

For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.


Kubeflow Pipelines documentation

Experiment | Kubeflow

by Willard at Jul 07, 2025, 04:37 AM

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Mitsue
9 months ago
Not sure about B, it might complicate things too much.
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Merilyn
9 months ago
I’d go with B for automation, seems more efficient!
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Derrick
9 months ago
Definitely agree with D! Kubeflow is super handy for this.
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Ming
9 months ago
Wait, can you really compare metrics in the same dashboard with D?
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Samira
9 months ago
I think option D is the best for organizing multiple runs.
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Yasuko
10 months ago
Creating experiments in Kubeflow Pipelines seems like a solid approach for organizing runs, but I’m not entirely confident about the setup process.
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Tamekia
10 months ago
Running multiple jobs on AI Platform sounds familiar, especially for comparing metrics, but I’m not certain if naming them similarly is enough for organization.
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Arlette
10 months ago
I think automating training runs with Cloud Composer could help manage multiple experiments, but I’m a bit unclear on how it integrates with Keras.
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Ilda
10 months ago
I remember we discussed using AutoML for quick model generation, but I’m not sure if it’s the best choice for customizing architectures.
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Gerry
10 months ago
I'm leaning towards option C and running multiple training jobs on AI Platform. That way I can easily scale up the training and compare the results. The similar job names should make it easy to keep track of everything.
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Lachelle
11 months ago
Option B with Cloud Composer sounds like a good way to automate the training runs. I've used Composer before and it seems pretty straightforward to set up. Plus, I can integrate it with my Keras code to make the whole process more streamlined.
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Aliza
11 months ago
Hmm, I'm a bit unsure about this one. I'm not super familiar with Kubeflow Pipelines, so I'm not sure if that's the best approach. Maybe I should look into the other options a bit more to see what might work better for my use case.
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Brandon
11 months ago
This seems like a pretty straightforward question. I'd go with option D and create an experiment in Kubeflow Pipelines to organize multiple model runs. That way I can easily compare the evaluation metrics and see which architecture performs best.
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Latrice
1 year ago
Wow, this question really has my head spinning. Maybe I should just train my model on a Ouija board and see what happens. At least that would be more entertaining than all this tech stuff.
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Josefa
1 year ago
D all the way! Kubeflow Pipelines is like the Swiss Army knife of ML ops. You can do everything from preprocessing to deployment, all in one place. Plus, the name just sounds cool.
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Rutha
11 months ago
I agree, it's so convenient to have everything in one place.
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Hollis
12 months ago
D all the way! Kubeflow Pipelines is like the Swiss Army knife of ML ops.
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Adaline
1 year ago
A? AutoML Tables? Nah, that's cheating! I want to build my own custom model, not let some AI do it for me. Where's the fun in that?
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Gayla
1 year ago
Hmm, I'm torn between B and D. Cloud Composer would automate a lot of the process, but Kubeflow Pipelines might give me more visibility and control. Decisions, decisions.
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Eliz
12 months ago
User 1: I think Cloud Composer would be more efficient for automating multiple training runs.
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Desmond
1 year ago
I think creating multiple models using AutoML Tables could also be beneficial for exploring different model architectures.
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Margurite
1 year ago
But wouldn't running multiple training jobs on AI Platform with similar job names also be a good option?
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Kris
1 year ago
I agree, it would help us compare the evaluation metrics easily.
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Elliot
1 year ago
Option D seems like the way to go. Kubeflow Pipelines can really help organize and track all those training runs. Plus, I heard the dashboard is pretty slick.
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Dorothy
1 year ago
Yeah, Kubeflow Pipelines will make it easier to compare the evaluation metrics of different model architectures. It's a smart move.
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Maryann
1 year ago
I agree, Kubeflow Pipelines is great for managing multiple training jobs. It's definitely a good choice for your project.
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Lettie
1 year ago
Option D seems like the way to go. Kubeflow Pipelines can really help organize and track all those training runs. Plus, I heard the dashboard is pretty slick.
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Merri
1 year ago
I think we should create an experiment in Kubeflow Pipelines to organize multiple runs.
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