You work for a company that is developing an application to help users with meal planning. You want to use machine learning to scan a corpus of recipes and extract each ingredient (e.g., carrot, rice, pasta) and each kitchen cookware (e.g., bowl, pot, spoon) mentioned. Each recipe is saved in an unstructured text file. What should you do?

• A. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.
• B. Create a multi-label text classification dataset on Vertex AI. Create a test dataset, and label each recipe that corresponds to its ingredients and cookware. Train a multi-class classification model. Evaluate the model's performance on a holdout dataset.
• C. Create a text dataset on Vertex AI for entity extraction. Create as many entities as there are different ingredients and cookware. Train an AutoML entity extraction model to extract those entities. Evaluate the model's performance on a holdout dataset.
• D. Create a text dataset on Vertex AI for entity extraction Create two entities called ingredient and cookware, and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.

You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?

• A. Use Cloud Composer to build the training pipelines for custom deep learning-based models
• B. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
• C. Use Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
• D. Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models

You work for a telecommunications company. You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery and the predictive features that are available for model training include:
- Customer_id
- Age
- Salary (measured in local currency)
- Sex
- Average bill value (measured in local currency)
- Number of phone calls in the last month (integer)
- Average duration of phone calls (measured in minutes)
You need to investigate and mitigate potential bias against disadvantaged groups, while preserving model accuracy.
What should you do?

• A. Determine whether there is a meaningful correlation between the sensitive features and the other features. Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features.
• B. Define a fairness metric that is represented by accuracy across the sensitive features. Train a BigQuery ML boosted trees classification model with all features. Use the trained model to make predictions on a test set. Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.
• C. Train a BigQuery ML boosted trees classification model with all features. Use the ML.GLOBAL_EXPLAIN method to calculate the global attribution values for each feature of the model. If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature.
• D. Train a BigQuery ML boosted trees classification model with all features. Use the ML.EXPLAIN_PREDICT method to calculate the attribution values for each feature for each customer in a test set. If for any individual customer, the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.

You work in the credit card fraud detection department of a bank that processes millions of transactions per day. You need to create and serve a real-time fraud detection model. To address the challenge of feature engineering at this scale with limited raw data, you plan to create time- related aggregate features, such as the average transaction amount within the last month or the amount of time since the last transaction. How should you process the data for model training and serving?

• A. Ingest the transaction data into BigQuery. Use a TRANSFORM_ONLY model in BigQuery ML to create a reusable feature engineering pipeline. Use this pipeline to preprocess the data.
• B. Orchestrate a workflow in Agent Platform Pipelines that exports raw transaction data from BigQuery to Cloud Storage. Perform feature engineering using a Python script in a custom component in the pipeline.
• C. Ingest the transaction data into BigQuery. Use the TRANSFORM clause in BigQuery ML to create new features by applying SQL functions to the raw transaction data.
• D. Create streaming Dataflow pipelines that read transaction data from Pub/Sub. Use a windowing function to aggregate transaction data over time. Store the engineered features in Agent Platform Feature Store.

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

• A. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
• B. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
• C. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
• D. Use the AI Platform custom containers feature to receive training jobs using any framework.