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Google Professional-Machine-Learning-Engineer Practice Exam (Desktop & Web-Based)

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Google Professional Machine Learning Engineer Certification Exam is recognized globally as a standard of excellence in the field of machine learning engineering. It is a valuable credential that can enhance the career prospects of individuals by demonstrating their expertise and proficiency in machine learning engineering to potential employers.

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Google Professional Machine Learning Engineer Sample Questions (Q289-Q294):

NEW QUESTION # 289
You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery.
The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

A. Use BQML XGBoost regression to train the model
B. Use AutoML Tables to train the model without early stopping.
C. Create a custom TensorFlow DNN model.
D. Use AutoML Tables to train the model with RMSLE as the optimization objective

Answer: D

Explanation:
AutoML Tables is a service that allows you to automatically build, analyze, and deploy machine learning models on tabular data. It is suitable for large-scale regression and classification problems, and it supports various optimization objectives, data splitting methods, and hyperparameter tuning algorithms. AutoML Tables can handle both categorical and numerical features, and it can also handle missing values and outliers.
AutoML Tables is a good choice for this problem because it minimizes the effort and training time required to train a regression model, while maximizing the model performance.
RMSLE stands for Root Mean Squared Logarithmic Error, and it is a metric that measures the average difference between the logarithm of the predicted values and the logarithm of the actual values.RMSLE is useful for regression problems where the target variable can include negative values, and where large differences between small values are more important than large differences between large values. For example, RMSLE penalizes underestimating a value of 10 by 2 more than overestimating a value of 1000 by
20. RMSLE is a good optimization objective for this problem because it can handle negative values in the target variable, and it can reduce the impact of outliers and large errors.
For more information about AutoML Tables and RMSLE, see the following references:
* AutoML Tables: end-to-end workflows on AI Platform Pipelines
* Predict workload failures before they happen with AutoML Tables
* How to Calculate RMSE in R

NEW QUESTION # 290
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
B. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
C. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
D. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

Answer: B

Explanation:
https://linuxtut.com/en/f4771efee37658c083cc/
https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb
; https://v0-5.kubeflow.org/docs/pipelines/reusable-components/

NEW QUESTION # 291
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

A. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
B. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
C. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
D. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

Answer: B

NEW QUESTION # 292
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Use Al Platform to run the classification model job configured for hyperparameter tuning
B. Configure AutoML Tables to perform the classification task
C. Run a BigQuery ML task to perform logistic regression for the classification
D. Use Al Platform Notebooks to run the classification model with pandas library

Answer: B

Explanation:
https://cloud.google.com/automl-tables/docs/beginners-guide

NEW QUESTION # 293
You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A. Change the health Route value in your models configuration to /heal thcheck.
B. Change the HTTP port in your model's configuration to the default value of 8080
C. Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.
D. Add a logging configuration to your application to emit logs to Cloud Logging.

Answer: C

NEW QUESTION # 294
......

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