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

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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: ronny
Question 76

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

Options:

A.

Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile

application to send batch requests during prediction.

B.

Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application

to use the mlmodel file directly.

C.

Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

D.

Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.

Discussion
Question 77

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

Options:

A.

Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.

B.

Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline

C.

Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

D.

Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery

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Question 78

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

Options:

A.

Use a machine type with more memory

B.

Decrease the number of workers per machine

C.

Increase the CPU utilization target in the autoscaling configurations

D.

Decrease the CPU utilization target in the autoscaling configurations

Discussion
Question 79

You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

Options:

A.

Compare the loss performance for each model on a held-out dataset.

B.

Compare the loss performance for each model on the validation data

C.

Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

D.

Compare the mean average precision across the models using the Continuous Evaluation feature

Discussion
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