You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

• A. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
• B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
• C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
• D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
• E. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Create an additional table with only the necessary columns.
• B. Create a view on the table to present to the virtualization tool.
• C. Export the data into a Google Sheet for virtualization.
• D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

• A. Remove the features that have null values for more than 50% of the training records.
• B. Eliminate features that are highly correlated to the output labels.
• C. Instead of feeding in each feature individually, average their values in batches of 3.
• D. Combine highly co-dependent features into one representative feature.

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

• A. Set the BigQuery dataset to be regiona
• B. Set the BigQuery dataset to be regiona
• C. In the event of an emergency, use a point-in-time snapshot to recover the data.
• D. Set the BigQuery dataset to be multi-regional
• E. Set the BigQuery dataset to be multi-regional
• F. In the event of an emergency, use the backup copy of the table.
• G. In the event of an emergency, use the backup copy of the table.
• H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
• I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
• J. In the event of an emergency, use a point-in-time snapshot to recover the data.

You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

• A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
• B. Create a temporary table in Cloud Spanner that will act as a buffer for new data
• C. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
• D. Use a larger instance type for your Cloud Dataflow workers
• E. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
• F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
• G. Increase the number of max workers