Google Associate Data Practitioner Exam Questions

Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is to use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.

Using Cloud Composer to 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.

Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

Online transactions: Storing the transactional data in BigQuery is ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.

Customer feedback: Storing customer feedback in Cloud Storage is appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.

Social media activity: Storing real-time social media activity in BigQuery is optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.

Storing the data in a multi-region bucket ensures high availability and durability, even in the event of a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.

A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:

Option A: Cloud CDN caches content for web delivery but doesn't protect against underlying storage outages---it's for performance, not availability of the source data.

Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn't ensure availability during a zone failure (still tied to one location).

Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.

Option D: Nearline storage is a low-cost class with lower availability (single-region by default), not designed for outage resilience. Why C is Best: Multi-region storage offers geo-redundancy, guaranteeing data access during outages with no additional configuration. It's the simplest, most reliable choice for pipeline continuity. Extract from Google Documentation: From 'Cloud Storage Bucket Locations' (https://cloud.google.com/storage/docs/locations): 'Multi-region buckets replicate data across multiple regions within a geography, providing high availability and durability, ensuring access even if a single zone or region experiences an outage.' Reference: Google Cloud Documentation - 'Cloud Storage Locations' (https://cloud.google.com/storage/docs/locations).