QUESTION: 97

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

Train a regression model using AutoML Tables.
Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
Develop a regression model using BigQuery ML.

Answer(s): A

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QUESTION: 98

You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?

Migrate your model to TensorFlow, and train it using Vertex AI Training.
Train your model in a distributed mode using multiple Compute Engine VMs.
Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.
Train your model using Vertex AI Training with GPUs.

Answer(s): C

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QUESTION: 99

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

Answer(s): B

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QUESTION: 100

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

Reinforcement learning
Recommender system
Recurrent Neural Networks (RNN)
Convolutional Neural Networks (CNN)

Answer(s): D

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Free Professional Machine Learning Engineer Exam Braindumps (page: 25)

QUESTION: 97

QUESTION: 98

QUESTION: 99

QUESTION: 100