Associate-Data-Practitioner Exam Dumps - Google Cloud Associate Data Practitioner (ADP Exam)

To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.

Creating amaterialized viewwith the limited data in a new dataset and granting the data analyst theBigQuery Data Viewerrole on the dataset and theBigQuery Job Userrole in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

TheDataflow Developerrole provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as itgrants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.

UsingCloud Composerto design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.

Comprehensive and Detailed In-Depth Explanation:

The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:

Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.

Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).

Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.

Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

Dataform workflowsare the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let’s evaluate:

Option A: Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.

Option B: Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.

Option C: Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.

Setting thetable partition expiration periodto one year using theALTER TABLEstatement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.

Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery" (https://cloud.google.com/bigquery/docs/partitioned-tables#expiration ):"Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."