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

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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: 296 Q&A's Shared By: margaux
Question 60

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

Questions 60

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

A)

Questions 60

B)

Questions 60

C)

Questions 60

D)

Questions 60

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Discussion
Question 61

You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:

• Optimizer: SGD

• Image shape = 224x224

• Batch size = 64

• Epochs = 10

• Verbose = 2

During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

Options:

A.

Change the optimizer

B.

Reduce the batch size

C.

Change the learning rate

D.

Reduce the image shape

Discussion
Question 62

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

Options:

A.

Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.

B.

Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.

C.

Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.

D.

Use TensorFlow I/O’s BigQuery Reader to directly read the data.

Discussion
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Question 63

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

Options:

A.

Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

B.

Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

C.

Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

D.

Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Discussion
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