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Free Professional-Machine-Learning-Engineer Google Updates

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Total 285 questions
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Google Professional Machine Learning Engineer Questions and Answers

Question 61

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model’s underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

Options:

A.

Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

B.

Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

C.

Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.

D.

Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Question 62

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

Options:

A.

Configure a Compute Engine instance with Spark and use Jupyter notebooks.

B.

Set up a Dataproc cluster with Spark and use Jupyter notebooks.

C.

Set up a Vertex AI Workbench instance with a Spark kernel.

D.

Use Colab Enterprise with a Spark kernel.

Question 63

You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

Options:

A.

Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model’s feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.

B.

Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.

C.

Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.

D.

Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.

Question 64

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

Options:

A.

Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.

B.

Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.

C.

Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model

output.

D.

Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.

Page: 16 / 21
Total 285 questions
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