Online Access Free Google.Professional-Machine-Learning-Engineer.v2026-10-09.q179 Practice Test (Page 12)

Professional-Machine-Learning-Engineer Exam Question 51

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN), within the same module.
You are using the training_methodargument to select one of the two methods, and you are using the learning_rateand num_hidden_layersarguments in the DNN. You plan to use Vertex AI's hypertuning service with a budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance. What should you do?

A.Run one hypertuning job for 100 trials. Set num_hidden_layersas a conditional hyperparameter based on its parent hyperparameter training_method, and set learning_rateas a non-conditional hyperparameter.

B.Run two separate hypertuning jobs, a linear regression job for 50 trials, and a DNN job for 50 trials. Compare their final performance on a common validation set, and select the set of hyperparameters with the least training loss.

C.Run one hypertuning job with training_methodas the hyperparameter for 50 trials. Select the architecture with the lowest training loss, and further hypertune it and its corresponding hyperparameters tor 50 trials.

D.Run one hypertuning job for 100 trials. Set num_hidden_layersand learning_rateas conditional hyperparameters based on their parent hyperparameter training_method.

Professional-Machine-Learning-Engineer Exam Question 52

You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts. Your team has assembled a set of annotated images from damage claim documents in the company's database. The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to train models on Google Cloud. You need to quickly create an initial model. What should you do?

A.Train an object detection model in AutoML by using the annotated image data.

B.Train an object detection model in Vertex AI custom training by using the annotated image data.

C.Download a pre-trained object detection model from TensorFlow Hub. Fine-tune the model in Vertex AI Workbench by using the annotated image data.

D.Create a pipeline in Vertex AI Pipelines and configure the AutoMLTrainingJobRunOp component to train a custom object detection model by using the annotated image data.

Professional-Machine-Learning-Engineer Exam Question 53

You developed a Transformer model in TensorFlow to translate text. Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the cluster's configuration. What should you do?

A.Create a Vertex AI custom distributed training job with Reduction Server. Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.

B.Create a training job that uses Cloud TPU VMs. Use tf.distribute.TPUStrategy for distribution.

C.Create a Vertex AI custom training job with a single worker pool of A2 GPU machine type instances. Use tf.distribute.MirroredStrategv for distribution.

D.Create a Vertex AI custom training job with GPU accelerators for the second worker pool. Use tf.distribute.MultiWorkerMirroredStrategy for distribution.

Professional-Machine-Learning-Engineer Exam Question 54

You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables.
This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A.Manually combine all columns that contain a time signal into an array.
AIlow AutoML to interpret this array appropriately.
Choose an automatic data split across the training, validation, and testing sets.

B.Submit the data for training without performing any manual transformations.
AIlow AutoML to handle the appropriate transformations.
Choose an automatic data split across the training, validation, and testing sets.

C.Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column.
AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.

D.Submit the data for training without performing any manual transformations.
Use the columns that have a time signal to manually split your data.
Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.

Professional-Machine-Learning-Engineer Exam Question 55

You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per hour than expected throughout the day. You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency. What should you do?

A.Set the target utilization percentage in the autoscailngMetricSpecs configuration to a higher value

B.Configure an appropriate minReplicaCount value based on expected baseline traffic

C.Deploy two models to the same endpoint, and distribute requests among them evenly

D.Change the model's machine type to one that utilizes GPUs

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Professional-Machine-Learning-Engineer Exam Question 51

Professional-Machine-Learning-Engineer Exam Question 52

Professional-Machine-Learning-Engineer Exam Question 53

Professional-Machine-Learning-Engineer Exam Question 54

Professional-Machine-Learning-Engineer Exam Question 55

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