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Machine Learning Engineer Professional-Machine-Learning-Engineer Release Date

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Question 8

You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?

Options:

A.

Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.

B.

Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.

C.

Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.

D.

Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.

Question 9

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 10

You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

Options:

A.

data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})

B.

data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})

C.

data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})

D.

data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

Question 11

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company’s profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

Options:

A.

Use latitude, longitude, and product type as features. Use profit as model output.

B.

Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.

C.

Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.

D.

Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 22, 2024
Questions: 285
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