You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

• A. Use BigQuery ML to create a logistic regression model for purchase prediction.
• B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
• C. Use Colab Enterprise to develop a custom model for purchase prediction.
• D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

• A. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
• B. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
• C. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
• D. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

• A. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
• B. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
• C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
• D. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

• A. Create a multi-region bucket, and upload the files to this bucket.
• B. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
• C. Create a dual-region bucket, and upload the files to this bucket.
• D. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

• A. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
• B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
• C. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
• D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.