Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 39 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 39
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow
Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage. After the file is saved, start the training job on a GKE cluster.
B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files. As soon as a file arrives, initiate the training job.
C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster.
D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job, check the timestamp of objects in your Cloud Storage bucket. If there are no new files since the last run, abort the job.

Show Suggested Answer

Suggested Answer: C 🗳️

by Paul_Dirac at June 26, 2021, 10:56 a.m.

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Paul_Dirac

Highly Voted 3 years, 7 months ago

C https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#triggering-and-scheduling-kubeflow-pipelines

upvoted 16 times

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Paul_Dirac

Highly Voted 3 years, 7 months ago

C https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#triggering-and-scheduling-kubeflow-pipelines

upvoted 7 times

ori5225

3 years, 5 months ago

On a schedule, using Cloud Scheduler. Responding to an event, using Pub/Sub and Cloud Functions. For example, the event can be the availability of new data files in a Cloud Storage bucket.

upvoted 1 times

tavva_prudhvi

1 year, 7 months ago

Option D requires the job to be scheduled at regular intervals, even if there are no new files. This can waste resources and lead to unnecessary delays in the training process.

upvoted 1 times

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PhilipKoku

Most Recent 8 months ago

Selected Answer: C

C) PUB/sub trigger from Cloud Storage & Cloud Function

upvoted 1 times

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fragkris

1 year, 2 months ago

Selected Answer: C

C - This is the google reccomended method.

upvoted 1 times

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Sum_Sum

1 year, 2 months ago

Selected Answer: C

C- because you don't want to re-engineer the pipeline

upvoted 1 times

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M25

1 year, 8 months ago

Selected Answer: C

Went with C

upvoted 1 times

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Fatiy

1 year, 11 months ago

Selected Answer: C

The scenario involves automatically running a Kubeflow Pipelines training job on GKE as soon as new data becomes available. To achieve this, we can use Cloud Storage to store the cleaned dataset, and then configure a Cloud Storage trigger that sends a message to a Pub/Sub topic whenever a new file is added to the storage bucket. We can then create a Pub/Sub-triggered Cloud Function that starts the training job on a GKE cluster.

upvoted 1 times

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behzadsw

2 years, 1 month ago

Selected Answer: A

The question says: As part of your CI/CD workflow, you want to automatically run a Kubeflow.. C is also an option but it seems more cumbersome. One thing hat could be against A is that the data engineering team is separate team so they might not access your CI/CD if any changes from their side is needed..

upvoted 1 times