Google Professional-Machine-Learning-Engineer Web-Based Practice Exam Features

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The Google Professional-Machine-Learning-Engineer exam comprises multiple-choice questions, performance-based tasks, and case studies that assess the candidate's ability to design and implement machine learning solutions using Google Cloud's machine learning tools and services. Professional-Machine-Learning-Engineer exam is designed to test the candidate's knowledge of key machine learning concepts, such as supervised and unsupervised learning, deep learning, natural language processing, and computer vision. Professional-Machine-Learning-Engineer Exam also evaluates the candidate's understanding of how to build scalable and reliable machine learning models that can handle large datasets.

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Google Professional Machine Learning Engineer Sample Questions (Q247-Q252):

NEW QUESTION # 247
You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the - raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

A. Run one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials
B. Run one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter
C. Run two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a common validation set. and select the set of hyperparameters with the least training loss
D. Run one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

Answer: D

NEW QUESTION # 248
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?

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

Answer: C

Explanation:
The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization.
Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction. The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:
* Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.
* Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training.
* Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training. References:
* Vertex AI Training | Google Cloud
* Kubeflow on Google Cloud | Google Cloud
* TFJob for training TensorFlow models with Kubernetes | Kubeflow
* Compute Engine | Google Cloud
* Slurm Workload Manager

NEW QUESTION # 249
You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job. What should you do?

A. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.
B. Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.
C. Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.
D. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.

Answer: D

Explanation:
According to the web search results, Reduction Server is a faster GPU all-reduce algorithm developed at Google that uses a dedicated set of reducers to aggregate gradients from workers12. Reducers are lightweight CPU VM instances that are significantly cheaper than GPU VMs2. Therefore, the third worker pool should not have any accelerators, and should use a machine type that has high network bandwidth to optimize the communication between workers and reducers2. TPUs are not supported by Reduction Server, so the first two worker pools should have GPUs and use a container image that contains the training code12. The reduction- server container image is provided by Google and should be used for the third worker pool2.

NEW QUESTION # 250
You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

Answer: B

NEW QUESTION # 251
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks.
You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

A. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
C. Use BigQuery ML to run several regression models, and analyze their performance.
D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

Answer: C

Explanation:
* Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries1. You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression2. You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve3. BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools4.
* Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task. Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools5. SparkML is a library that provides ML algorithms and utilities for Spark.
However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.
* Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.
* Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data
* from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.
References:
* BigQuery ML overview
* Creating a model in BigQuery ML
* Evaluating a model in BigQuery ML
* BigQuery ML benefits
* Dataproc overview
* [SparkML overview]
* [Vertex AI Workbench overview]
* [Scikit-learn overview]
* [TensorFlow overview]
* [Vertex AI overview]

NEW QUESTION # 252
......

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GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER WEB-BASED PRACTICE EXAM FEATURES

Google Professional-Machine-Learning-Engineer Web-Based Practice Exam Features