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?
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model’s predictions will be used to adapt each user’s game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
You work at an organization that manages a popular payment app. You built a fraudulent transaction detection model by using scikit-learn and deployed it to a Vertex AI endpoint. The endpoint is currently using 1 e2-standard-2 machine with 2 vCPUs and 8 GB of memory. You discover that traffic on the gateway fluctuates to four times more than the endpoint ' s capacity. You need to address this issue by using the most cost-effective approach. What should you do?
You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE ‘myproject.mydataset.training‘ AS
(SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE RAND() < = 0.8);
CREATE OR REPLACE TABLE ‘myproject.mydataset.validation‘ AS
(SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE RAND() < = 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
