Google 2cram all Professional-machine-learning-engineer Test Inside Google Questions by Montgomery q116 vce pdf

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Exam Name:	Google Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer Dumps
Vendor:	Google	Certification:	Machine Learning Engineer
Questions:	270 Q&A's	Shared By:	montgomery

Question 44

You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:

• Optimizer: SGD

• Image shape = 224x224

• Batch size = 64

• Epochs = 10

• Verbose = 2

During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

Options:

Change the optimizer

Reduce the batch size

Change the learning rate

Reduce the image shape

Discussion

Answer:

Explanation:

Explanation:

A ResourceExhaustedError: out of memory (OOM) when allocating tensor is an error that occurs when the GPU runs out of memory while trying to allocate memory for a tensor. A tensor is a multi-dimensional array of numbers that represents the data or the parameters of a machine learning model. The size and shape of a tensor depend on various factors, such as the input data, the model architecture, the batch size, and the optimization algorithm1.

For the use case of training a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine, the best option to resolve the error is to reduce the batch size. The batch size is a parameter that determines how many input examples are processed at a time by the model. A larger batch size can improve the model’s accuracy and stability, but it also requires more memory and computation. A smaller batch size can reduce the memory and computation requirements, but it may also affect the model’s performance and convergence2.

By reducing the batch size, the GPU can allocate less memory for each tensor, and avoid running out of memory. Reducing the batch size can also speed up the training process, as the GPU can process more batches in parallel. However, reducing the batch size too much may also have some drawbacks, such as increasing the noise and variance of the gradient updates, and slowing down the convergence of the model. Therefore, the optimal batch size should be chosen based on the trade-off between memory, computation, and performance3.

The other options are not as effective as option B, because they are not directly related to the memory allocation of the GPU. Option A, changing the optimizer, may affect the speed and quality of the optimization process, but it may not reduce the memory usage of the model. Option C, changing the learning rate, may affect the convergence and stability of the model, but it may not reduce the memory usage of the model. Option D, reducing the image shape, may reduce the size of the input tensor, but it may also reduce the quality and resolution of the image, and affect the model’s accuracy. Therefore, option B, reducing the batch size, is the best answer for this question.

References:

ResourceExhaustedError: OOM when allocating tensor with shape - Stack Overflow
How does batch size affect model performance and training time? - Stack Overflow
How to choose an optimal batch size for training a neural network? - Stack Overflow

Question 45

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

Options:

1 Create a Vertex Al managed dataset

2 Use a Vertex Ai training pipeline to train your model

3 Generate batch predictions in Vertex Al

1 Use a Vertex Al Pipelines custom training job component to train your model

2. Generate predictions by using a Vertex Al Pipelines model batch predict component

1 Upload your dataset to BigQuery

2. Use a Vertex Al custom training job to train your model

3 Generate predictions by using Vertex Al SDK custom prediction routines

1 Use Vertex Al Experiments to train your model.

2 Register your model in Vertex Al Model Registry

3. Generate batch predictions in Vertex Al

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Question 46

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.

1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.

2 Predictions will be stored in BigQuery

3. The model will be stored in a Cloud Storage bucket and will be updated frequently

You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

Options:

Write a Cloud Function that loads the model into memory for prediction Configure the function to be

triggered when messages are sent to Pub/Sub.

Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing

Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.

Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for

prediction.

Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

Discussion

Question 47

You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

Options:

Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.

Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.

Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

Discussion

Answer:

Explanation:

Explanation:

Customer churn is a binary classification problem, where the target variable is whether a customer has churned or not. Therefore, a logistic regression model is more suitable than a linear regression model, which is used for regression problems. A logistic regression model can output the probability of a customer churning, which can be used to rank the customers by their churn risk and take appropriate actions1.

BigQuery ML is a service that allows you to create and execute machine learning models in BigQuery using standard SQL queries2. You can use BigQuery ML to create a logistic regression model for customer churn prediction by using the CREATE MODEL statement and specifying the LOGISTIC_REG model type3. You can use the historical customer data as the input table for the model, and specify the features and the label columns3.

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models4. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases4. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction.

By registering the BigQuery ML model in Vertex AI Model Registry, you can leverage the Vertex AI features to evaluate and monitor the model performance4. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model’s prediction.

The other options are not suitable for your scenario, because they either use the wrong model type, such as linear regression, or they do not use Vertex AI to evaluate the model performance, which would limit the insights and actions you can take based on the model results.

References: