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

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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: prince
Question 56

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

Options:

A.

Create a hot-encoding of words, and feed the encodings into your model.

B.

Identify word embeddings from a pre-trained model, and use the embeddings in your model.

C.

Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.

D.

Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.

Discussion
Question 57

You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible What should you do?

Options:

A.

Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and

deep neural networks.

B.

Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.

C.

Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.

D.

Use Cloud Composer to build the training pipelines for custom deep learning-based models.

Discussion
Question 58

You work with a learn of researchers lo develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

Options:

A.

Configure a v3-8 TPU VM.

B.

Configure a v3-8 TPU node.

C.

Configure a c2-standard-60 VM without GPUs.

D, Configure a n1-standard-4 VM with 1 NVIDIA P100 GPU.

Discussion
Question 59

You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

Options:

A.

Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

B.

Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

C.

Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.

D.

Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

Discussion
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