You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

• A. Use Dataform to define the transformations in SQLX.
• B. Use Cloud Data Fusion to visually design and manage the pipeline.
• C. Use Dataproc to run the transformations on a cluster.
• D. Use Dataflow with a custom template for the transformation logic.

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Archive storage.
• B. Store the data in Cloud Storage using Nearline storage.
• C. Store the data in Cloud Storage using Coldline storage.
• D. Store the data in Cloud Storage using Standard storage.

You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

• A. Compare the confusion matrix.
• B. Compare the two different models.
• C. Evaluate data drift.
• D. Evaluate the data skewness.

You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

• A. Use the gcloud storage command to transfer the data over the internet.
• B. Use Cloud Client Libraries to transfer the data over the internet.
• C. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
• D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

• A. Create a view that filters out rows that are older than one year.
• B. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
• C. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
• D. Require users to specify a partition filter using the alter table statement in SQL.