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 Dataflow with a custom template for the transformation logic.
• D. Use Dataproc to run the transformations on a cluster.

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. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
• B. 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
• C. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
• D. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

• A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• B. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
• C. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

• A. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
• B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
• C. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
• D. Create a Cloud Run function that runs daily and deletes files older than seven days.

You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

• A. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
• C. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• D. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.