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Last Attempt Professional-Machine-Learning-Engineer Questions

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Question 32

You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

Options:

A.

Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

B.

Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

C.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.

D.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

Question 33

Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?

Options:

A.

Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available.

B.

Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget.

C.

Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when anomalies are detected.

D.

Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when feature drift is detected.

Question 34

You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job. What should you do?

Options:

A.

Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.

B.

Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.

C.

Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.

D.

Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.

Question 35

You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

Options:

A.

Enable VPC Service Controls for peering’s, and add Vertex Al to a service perimeter

B.

Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM)

authentication to secure access to the endpoint from the training job.

C.

Configure VPC Peering with Vertex Al and specify the network of the training job

D.

Download the data to a Cloud Storage bucket before calling the training job

Page: 8 / 20
Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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