Google Associate-Data-Practitioner Exam Dumps

Comprehensive and Detailed in Depth

Why C is correct:Setting the Shared folder to 'View' ensures everyone can see the content.

Creating Looker groups simplifies access management.

Subfolders allow granular permissions for each team.

Granting 'Manage Access, Edit' allows teams to modify only their own content.

Why other options are incorrect:A: Grants View access only, so teams can't edit.

B: Moving content to personal folders defeats the purpose of sharing.

D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.

Looker Access Control: https://cloud.google.com/looker/docs/access-control

Looker Groups: https://cloud.google.com/looker/docs/groups

Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.

Using object holds and lifecycle management rules is the most efficient and compliant strategy for this scenario because:

Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.

Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.

Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.

Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.