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Google Updated Professional-Machine-Learning-Engineer Exam Questions and Answers by alexia

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Google Professional-Machine-Learning-Engineer Exam Overview :

Exam Name: Google Professional Machine Learning Engineer
Exam Code: Professional-Machine-Learning-Engineer Dumps
Vendor: Google Certification: Machine Learning Engineer
Questions: 285 Q&A's Shared By: alexia
Question 40

You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

Options:

A.

AutoML Natural Language

B.

Cloud Natural Language API

C.

AI Hub pre-made Jupyter Notebooks

D.

AI Platform Training built-in algorithms

Discussion
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Question 41

You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:

Questions 41

Which endpoints should the Enrichment Cloud Functions call?

Options:

A.

1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language

B.

1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API

C.

1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision

D.

1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API

Discussion
Question 42

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery,

processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are

writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines.

Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to

avoid additional costs. What should you do?

Options:

A.

Delegate feature engineering to BigQuery and remove it from the pipeline.

B.

Add a GPU to the model training step.

C.

Enable caching in all the steps of the Kubeflow pipeline.

D.

Comment out the part of the pipeline that you are not currently updating.

Discussion
Question 43

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

Options:

A.

Use the batch prediction functionality of Al Platform

B.

Create a serving pipeline in Compute Engine for prediction

C.

Use Cloud Functions for prediction each time a new data point is ingested

D.

Deploy the model on Al Platform and create a version of it for online inference.

Discussion
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