Online Access Free Google.Professional-Machine-Learning-Engineer.v2023-01-24.q55 Practice Test (Page 2)

Professional-Machine-Learning-Engineer Exam Question 1

Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A.Cloud Composer, BigQuery ML , and Al Platform Prediction

B.Cloud Composer, Al Platform Training with custom containers , and App Engine

C.Kubeflow Pipelines and App Engine

D.Kubeflow Pipelines and Al Platform Prediction

Professional-Machine-Learning-Engineer Exam Question 2

You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

A.Compare the loss performance for each model on a held-out dataset.

B.Compare the loss performance for each model on the validation data

C.Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

D.Compare the mean average precision across the models using the Continuous Evaluation feature

Professional-Machine-Learning-Engineer Exam Question 3

You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

A.Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

B.Import the TensorFlow model with BigQuery ML, and run the ml.predict function.

C.Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.

D.Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.

Professional-Machine-Learning-Engineer Exam Question 4

You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

A.Embed the augmentation functions dynamically in the tf.Data pipeline.

B.Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.

C.Embed the augmentation functions dynamically as part of Keras generators.

D.Use Dataflow to create all possible augmentations, and store them as TFRecords.

Professional-Machine-Learning-Engineer Exam Question 5

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A.Three individual features binned latitude, binned longitude, and one-hot encoded car type

B.One feature obtained as an element-wise product between latitude, longitude, and car type

C.One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

D.Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

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