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Question 52

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?

Options:

A.

Use the Vertex AI Training to submit training jobs using any framework.

B.

Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.

C.

Create a library of VM images on Compute Engine, and publish these images on a centralized repository.

D.

Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Question 53

You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model’s performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?

Options:

A.

Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.

B.

Identify temporal patterns in your model’s performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.

C.

Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.

D.

Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.

Question 54

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

Options:

A.

Use BigQuerys scheduling service to run the model retraining query periodically.

B.

Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.

C.

Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.

D.

Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.

Question 55

You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

Options:

A.

Randomly redistribute the data, with 70% for the training set and 30% for the test set

B.

Use sparse representation in the test set

C.

Apply one-hot encoding on the categorical variables in the test data.

D.

Collect more data representing all categories

Page: 13 / 20
Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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