Your large e-commerce company uses BigQuery for its data warehousing needs. They have two primary types of workloads:
- Analyst dashboards: Interactive queries run by business analysts
throughout the day, requiring low latency for real-time insights.
- ETL jobs: Large, complex batch queries that load and transform data
overnight. These jobs are cost-sensitive and can tolerate longer
execution times, and they do not have the same performance requirements as the dashboards.
Analysts are reporting inconsistent performance of their dashboards, particularly during peak business hours. You need to ensure reliable performance for both workload types while optimizing costs. What should you do?

Your company uses separate Google Cloud projects for development, staging, and production.
Developers need edit access in the development project, read-only access in the staging project, and no access in the production project. You need an effective and manageable solution to assign and enforce these permissions according to Google-recommended practices. What should you do?

You currently use a SQL-based tool to visualize your data stored in BigQuery. The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

You are creating the CI/CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?