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Professional-Machine-Learning-Engineer dumps questions answers

Google Professional-Machine-Learning-Engineer Dumps

Exam Code:
Professional-Machine-Learning-Engineer
Exam Name:
Google Professional Machine Learning Engineer
Last Update: Mar 25, 2024
268 Questions with Explanation
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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

Options:

A.

Expose each individual model as an endpoint in Vertex Al Endpoints. Create a custom container endpoint to orchestrate the workflow.

B.

Create a custom container endpoint for the workflow that loads each models individual files Track the versions of each individual model in BigQuery.

C.

Expose each individual model as an endpoint in Vertex Al Endpoints. Use Cloud Run to orchestrate the workflow.

D.

Load each model's individual files into Cloud Run Use Cloud Run to orchestrate the workflow Track the versions of each individual model in BigQuery.

Questions 2

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

Options:

A.

Increase the learning rate

B.

Increase the number of epochs

C.

Decrease the learning rate

D.

Increase the batch size

Questions 3

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.

1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.

2 Predictions will be stored in BigQuery

3. The model will be stored in a Cloud Storage bucket and will be updated frequently

You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

Options:

A.

Write a Cloud Function that loads the model into memory for prediction Configure the function to be

triggered when messages are sent to Pub/Sub.

B.

Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing

Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.

C.

Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for

prediction.

D.

Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.