QUESTION: 125

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?

Use the Vertex AI Training to submit training jobs using any framework.
Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Answer(s): A

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QUESTION: 126

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
Rewrite your input function to resize and reshape the input images.
Rewrite your input function using parallel reads, parallel processing, and prefetch.

Answer(s): D

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QUESTION: 127

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

Remove the rows with missing values, and upsample your dataset by 5%.
Replace the missing values with the feature’s mean.
Replace the missing values with a placeholder category indicating a missing value.
Move the rows with missing values to your validation dataset.

Answer(s): C

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QUESTION: 128

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

Create an object detection model that can localize the rust spots.
Develop an image segmentation ML model to locate the boundaries of the rust spots.
Develop a template matching algorithm using traditional computer vision libraries.
Develop an image classification ML model to predict the presence of the disease.

Answer(s): B

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Free Professional Machine Learning Engineer Exam Braindumps (page: 32)

QUESTION: 125

QUESTION: 126

QUESTION: 127

QUESTION: 128