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

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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: alec
Question 48

You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

Options:

A.

Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.

B.

Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.

C.

Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

D.

Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

Discussion
Question 49

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

Options:

A.

Use TensorFlow Data Validation to detect and flag schema anomalies.

B.

Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.

C.

Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

D.

Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

Discussion
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Question 50

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table How should you perform the inference?

Options:

A.

Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

B.

Import the TensorFlow model by using the create model statement in BigQuery ML Apply the historical data to the TensorFlow model.

C.

Export the historical data to Cloud Storage in CSV format Configure a Vertex Al batch prediction job to generate predictions for the exported data.

D.

Configure a Vertex Al batch prediction job to apply the model to the historical data in BigQuery

Discussion
Question 51

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

Options:

A.

Create a Vertex Al pipeline that runs different model training jobs in parallel.

B.

Train an AutoML image classification model.

C.

Create a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

D.

Create a Vertex Al hyperparameter tuning job.

Discussion
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