Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer

Your organization's security policy states that prediction traffic for a patient-risk model must never traverse the public internet, and that data exfiltration from the project must be blocked even by users with valid credentials. You need to deploy the model accordingly. What should you do?

• A. Deploy to a Private Service Connect endpoint reachable from your VPC, and configure a VPC Service Controls perimeter around the project's Agent Platform and storage services.
• B. Deploy to a public endpoint, and restrict access with an IAM allow policy limited to the service account of the calling application.
• C. Deploy to a public endpoint, and encrypt request payloads in the application before sending them.
• D. Deploy to a public endpoint behind Cloud Armor with an IP allowlist for the corporate network range.

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. Use BigQuery ML to run several regression models, and analyze their performance.
• C. Read the data from BigQuery using Dataproc, and run several models using SparkML.
• D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process. High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number, serial number, dimensions, and weight. You need to configure model training and serving while maximizing model accuracy. What should you do?

• A. Convert the images into an embedding representation. Import this data into BigQuery, and train a BigQuery ML K-means clustering model with two clusters. Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.
• B. Import the tabular data into BigQuery, use Vertex AI Data Labeling Service to label the data and train an AutoML tabular classification model. Deploy the model, and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.
• C. Use Vertex AI Data Labeling Service to label the images, and tram an AutoML image classification model. Deploy the model, and configure Pub/Sub to publish a message when an image is categorized into the failing class.
• D. Use Vertex AI Data Labeling Service to label the images, and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

• A. Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
• B. Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.
• C. Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
• D. Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.

You are using generative AI to build a conversational agent to support bank customers' inquiries about their product base and transaction history. You must ensure that the agent does not include personally identifiable information (PII) in its responses. You want to implement this requirement with minimal effort. What should you do?

• A. Configure the safety_settings thresholds within the Agent Platform model parameters to Block_Most.
• B. Use sampled Shapley (SHAP) values to understand the model's predictions and ensure that it is not biased toward revealing PII.
• C. Implement Model Armor with Sensitive Data Protection to detect and redact PII from the agent's responses.
• D. Develop a post-processing wrapper using Python's re module with patterns r'\b(?:\d{4}[- ]?){3}\d{4}\b' to identify and redact credit card numbers.