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Google Professional-Machine-Learning-Engineer Exam Dumps FAQs

What is the Google Professional Machine Learning Engineer Exam

The Google Professional Machine Learning Engineer Exam certifies your ability to build, evaluate, productionize, and optimize ML models using Google Cloud technologies. It assesses your knowledge of proven models, techniques, and responsible AI practices.

Who is the target audience for Google Professional Machine Learning Engineer Exam

Professionals with at least 3 years of industry experience, including 1 or more years designing and managing solutions using Google Cloud, are ideal candidates for this certification.

What topics are covered in the Google Professional-Machine-Learning-Engineer Exam

The Google Professional-Machine-Learning-Engineer exam focuses on core competencies across the machine learning lifecycle on GCP, including:

Machine Learning Fundamentals (20%)
Data Management (20%)
Model Training (20%)
Model Evaluation and Tuning (15%)
Model Deployment and Monitoring (25%)

How many questions are there in the Google Professional-Machine-Learning-Engineer Exam

The Google Professional-Machine-Learning-Engineer exam format includes 50-60 multiple-choice and multiple-select questions.

How long is the Google Professional-Machine-Learning-Engineer Exam

The Google Professional-Machine-Learning-Engineer exam duration is 2 hours.

How long does the Google Professional-Machine-Learning-Engineer certification last

The Google Professional-Machine-Learning-Engineer certification is valid for three years.

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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

Options:

Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile

application to send batch requests during prediction.

Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application

to use the mlmodel file directly.

Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.

Questions 2

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?

Options:

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:

Explanation:

Option A is incorrect because migrating your model to TensorFlow, and training it using Vertex AI Training, is not the easiest way to improve the model’s training time. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI Training is a service that allows you to train and optimize ML models using built-in algorithms or custom containers. However, this option requires significant code changes, as TensorFlow and scikit-learn have different APIs and functionalities. Moreover, this option does not leverage the parallelism or the scalability of the cloud, as it only uses a single instance.

Option B is incorrect because training your model in a distributed mode using multiple Compute Engine VMs, is not the most convenient way to improve the model’s training time. Compute Engine is a service that allows you to create and manage virtual machines that run on Google Cloud. You can use Compute Engine to run your scikit-learn model in a distributed mode, by using libraries such as Dask or Joblib. However, this option requires more effort and resources than option D, as it involves creating and configuring the VMs, installing and maintaining the libraries, and writing and running the distributed code.

Option C is incorrect because training your model with DLVM images on Vertex AI, and ensuring that your code utilizes NumPy and SciPy internal methods whenever possible, is not the most effective way to improve the model’s training time. DLVM (Deep Learning Virtual Machine) images are preconfigured VM images that include popular ML frameworks and tools, such as TensorFlow, PyTorch, or scikit-learn1. You can use DLVM images on Vertex AI to train your scikit-learn model, by using a custom container. NumPy and SciPy are libraries that provide numerical and scientific computing functionalities for Python. You can use NumPy and SciPy internal methods to optimize your scikit-learn code, as they are faster and more efficient than pure Python code2. However, this option does not leverage the parallelism or the scalability of the cloud, as it only uses a single instance. Moreover, this option may not have a significant impact on the training time, as scikit-learn already relies on NumPy and SciPy for most of its operations3.

Option D is correct because training your model using Vertex AI Training with GPUs, is the best way to improve the model’s training time. A GPU (Graphics Processing Unit) is a hardware accelerator that can perform parallel computations faster than a CPU (Central Processing Unit)4. Vertex AI Training is a service that allows you to train and optimize ML models using built-in algorithms or custom containers. You can use Vertex AI Training with GPUs to train your scikit-learn model, by using a custom container and specifying the accelerator type and count5. By using Vertex AI Training with GPUs, you can leverage the parallelism and the scalability of the cloud, and speed up the training process significantly, without changing your code.

References:

DLVM images

NumPy and SciPy

scikit-learn dependencies

GPU overview

Vertex AI Training with GPUs

[scikit-learn overview]

[TensorFlow overview]

[Compute Engine overview]

[Dask overview]

[Joblib overview]

[Vertex AI Training overview]

Questions 3

You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website.

You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application. You expect higher traffic to the website during

nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?

Options:

Configure the model deployment settings to use an n1-standard-32 machine type.

Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.

Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.

Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.

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