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

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Question 8

You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

Options:

A.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.

3. Feed the resulting BigQuery view into Vertex Al Training.

B.

1 Use BigQuery to scale the numerical features.

2. Feed the features into Vertex Al Training.

3 Allow TensorFlow to perform the one-hot text encoding.

C.

1 Use TFX components with Dataflow to encode the text features and scale the numerical features.

2 Export results to Cloud Storage as TFRecords.

3 Feed the data into Vertex Al Training.

D.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2 Perform the one-hot text encoding in BigQuery.

3. Feed the resulting BigQuery view into Vertex Al Training.

Question 9

You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?

Options:

A.

Configure your model to use bfloat16 instead float32

B.

Reduce the global batch size from 1024 to 256

C.

Reduce the number of layers in the model architecture

D.

Reduce the dimensions of the images used un the model

Question 10

You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

Options:

A.

Use the class distribution to generate 10% positive examples

B.

Use a convolutional neural network with max pooling and softmax activation

C.

Downsample the data with upweighting to create a sample with 10% positive examples

D.

Remove negative examples until the numbers of positive and negative examples are equal

Question 11

You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model’s predictions will be used to adapt each user’s game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

Options:

A.

Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL

B.

Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.

C.

Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

D.

Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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