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Machine Learning Engineer Professional-Machine-Learning-Engineer Exam Dumps

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Question 64

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

Question 65

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

Options:

A.

1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.

2 Reference tf .data.TFRecordDataset in the training script.

3. Train the model by using Vertex Al Training with a V100 GPU.

B.

1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

2 Reference tfds.fclder_da-asst.imageFclder in the training script.

3. Train the model by using Vertex AI Training with a V100 GPU.

C.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python script that creates sharded TFRecord files in a directory inside the instance

3. Reference tf. da-a.TFRecrrdDataset in the training script.

4. Train the model by using the Workbench instance.

D.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

3 Reference tf ds. f older_dataset. imageFolder in the training script.

4. Train the model by using the Workbench instance.

Question 66

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

Options:

A.

Export the model to BigQuery ML.

B.

Deploy and version the model on Al Platform.

C.

Use Dataflow with the SavedModel to read the data from BigQuery

D.

Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

Question 67

You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?

Options:

A.

Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.

B.

Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.

C.

Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.

D.

Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Oct 31, 2024
Questions: 270
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