Google Professional Machine Learning Engineer : Professional-Machine-Learning-Engineer Exam

You have recently trained a scikit-learn model that you plan to deploy on Vertex AI. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code. What should you do?

• A. 1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model.
  2. Upload your scikit learn model container to Vertex AI Model Registry.
  3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job
• B. 1. Upload your model to the Vertex AI Model Registry by using a prebuilt scikit-ieam prediction container.
  2. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
• C. 1. Create a custom container for your scikit learn model.
  2. Upload your model and custom container to Vertex AI Model Registry.
  3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
• D. 1. Create a custom container for your scikit learn model.
  2. Define a custom serving function for your model.
  3. Upload your model and custom container to Vertex AI Model Registry.
  4. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job.

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

• A. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
• B. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
• C. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
• D. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

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. 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.
• B. 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.
• C. 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.
• D. 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.

You are an ML engineer at a large retail company. You have a BigQuery table containing five years of daily sales data for 50,000 distinct product SKUs. You need to generate a daily demand forecast for the next 30 days for each individual SKU. The solution must automatically account for holidays and weekly seasonality. You want to minimize data egress costs and operational complexity by keeping the data and model within the data warehouse. What should you do?

• A. Use the gcloud ai custom-jobs create command to run a PyTorch LSTM container on Agent Platform that pulls data from a CSV into Cloud Storage.
• B. Use the CREATE MODEL... OPTIONS(model_type='LINEAR_REG') statement in BigQuery to predict sales using the transaction date as a feature.
• C. Use the CREATE MODEL... OPTIONS(model_type='ARIMA_PLUS') statement in BigQuery to generate a forecast based on historical SKU sales.
• D. Use the google-cloud-aiplatform SDK to create a TimeSeriesDataset and trigger an AutoMLForecastingTrainingJob.

You are developing an AI text generator that will be able to dynamically adapt its generated responses to mirror the writing style of the user and mimic famous authors if their style is detected. You have a large dataset of various authors' works, and you plan to host the model on a custom VM. You want to use the most effective model. What should you do?

• A. Fine-tune a BERT-based model from Hugging Face.
• B. Use the latest Gemini Flash foundational model.
• C. Fine-tune the latest Llama model from Model Garden on Agent Platform Serverless Training.
• D. Deploy the latest Llama model from Model Garden, and use prompt engineering.