Free Google Professional-Machine-Learning-Engineer Exam Dumps Questions & Answers

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

• A.Build your custom containers to run distributed training jobs on AI Platform Training.
• B.Build your custom container to run jobs on AI Platform Training.
• C.Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.
• D.Use a built-in model available on AI Platform Training.

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?

• A.Train your model using Vertex AI Training with GPUs.
• B.Train your model in a distributed mode using multiple Compute Engine VMs.
• C.Migrate your model to TensorFlow, and train it using Vertex AI Training.
• D.Train your model using Vertex AI Training with CPUs.

You lead a data science team that is working on a computationally intensive project involving running several experiments. Your team is geographically distributed and requires a platform that provides the most effective real-time collaboration and rapid experimentation. You plan to add GPUs to speed up your experimentation cycle, and you want to avoid having to manually set up the infrastructure. You want to use the Google-recommended approach. What should you do?

• A.Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.
• B.Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
• C.Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
• D.Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

• A.AutoML Vision model
• B.AutoML Vision Edge mobile-low-latency-1 model
• C.AutoML Vision Edge mobile-versatile-1 model
• D.AutoML Vision Edge mobile-high-accuracy-1 model

You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the training job while following Google-recommended practices. What should you do?

• A.Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the custom dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.
• B.Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the custom dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.
• C.Store the data in a Cloud Storage bucket, and create a custom container with your training application and its custom dependencies. In your training application, read the data from Cloud Storage and train the model.
• D.Create a custom container that includes the data and the custom dependencies. In your training application, load the data into a pandas DataFrame and train the model.