QUESTION: 61

You are using transfer learning to train an image classifier based on a pre-trained EfficientNet model. Your training dataset has 20,000 images. You plan to retrain the model once per day. You need to minimize the cost of infrastructure. What platform components and configuration environment should you use?

A Deep Learning VM with 4 V100 GPUs and local storage.
A Deep Learning VM with 4 V100 GPUs and Cloud Storage.
A Google Kubernetes Engine cluster with a V100 GPU Node Pool and an NFS Server
An AI Platform Training job using a custom scale tier with 4 V100 GPUs and Cloud Storage

Answer(s): C

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QUESTION: 62

While conducting an exploratory analysis of a dataset, you discover that categorical feature A has substantial predictive power, but it is sometimes missing. What should you do?

Drop feature A if more than 15% of values are missing. Otherwise, use feature A as-is.
Compute the mode of feature A and then use it to replace the missing values in feature A.
Replace the missing values with the values of the feature with the highest Pearson correlation with feature A.
Add an additional class to categorical feature A for missing values. Create a new binary feature that indicates whether feature A is missing.

Answer(s): A

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QUESTION: 63

You work for a large retailer and have been asked to segment your customers by their purchasing habits. The purchase history of all customers has been uploaded to BigQuery. You suspect that there may be several distinct customer segments, however you are unsure of how many, and you don’t yet understand the commonalities in their behavior. You want to find the most efficient solution. What should you do?

Create a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
Create a new dataset in Dataprep that references your BigQuery table. Use Dataprep to identify similarities within each column.
Use the Data Labeling Service to label each customer record in BigQuery. Train a model on your labeled data using AutoML Tables. Review the evaluation metrics to understand whether there is an underlying pattern in the data.
Get a list of the customer segments from your company’s Marketing team. Use the Data Labeling Service to label each customer record in BigQuery according to the list. Analyze the distribution of labels in your dataset using Data Studio.

Answer(s): B

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QUESTION: 64

You recently designed and built a custom neural network that uses critical dependencies specific to your organization’s framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

Use a built-in model available on AI Platform Training.
Build your custom container to run jobs on AI Platform Training.
Build your custom containers to run distributed training jobs on AI Platform Training.
Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

Answer(s): C

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Free Professional Machine Learning Engineer Exam Braindumps (page: 16)

QUESTION: 61

QUESTION: 62

QUESTION: 63

QUESTION: 64