You have just completed training a machine learning model on customer behavior data. Your next step is to evaluate its performance to decide if it’s ready for deployment. Which two evaluation metrics are critical for assessing the quality of a model designed to predict customer churn? (Select two)
Your team is loading data into BigQuery from various sources, including CSV files, JSON logs, and a relational database. During ingestion, you notice mismatched schemas, inconsistent null handling, and duplicate records. What should you do to identify and resolve these issues before loading the data?
A financial services company processes data on stock trades and needs a pipeline to automate data ingestion, transformation, and loading every hour. They require a solution with fine-grained scheduling capabilities, dependency management between tasks, and automatic retries in case of failure. The company also wants to monitor job status and receive alerts when issues occur. Which Google Cloud service best meets these requirements?
Your marketing team wants a Looker dashboard that shows customer engagement trends over time, including metrics like average session duration, total page views, and click-through rate (CTR) for campaigns. They also want to be able to see specific data for individual campaigns and share their findings easily with other team members. Which of the following would best meet these requirements?
An organization wants to keep track of different versions of their customer churn prediction model, ensuring that each version’s metadata, performance metrics, and deployment status are easily accessible. What should they prioritize in Vertex AI Model Registry to manage these requirements?