Your organization is planning to migrate a legacy data warehouse to BigQuery. Before migrating, you need to perform a technical inventory of the source system to determine migration complexity and identify optimization opportunities. You have the following requirements for the assessment: Identify tables and databases that are frequently accessed together in a single query to understand data dependencies. Discover tables with no recent usage or writes to potentially exclude them from the migration and reduce storage costs. Identify low-latency queries that could be accelerated using BigQuery BI Engine. Which solution should you implement to achieve these requirements?

Use the BigQuery migration assessment to analyze the source data warehouse logs: What the BigQuery migration assessment is The BigQuery migration assessment is a specialized tool designed to evaluate legacy data warehouse environments by analyzing their historical execution logs. It provides comprehensive reports on system utilization, data dependencies, and query patterns to facilitate a smooth migration to Google Cloud. How it meets the requirements Understand data dependencies: The Database Coupling view explicitly maps out which databases and tables are frequently accessed together within single queries, helping architects plan migration waves. Discover tables with no usage or writes: By analyzing historical logs, the assessment generates Tables With No Usage and Tables With No Writes views, allowing organizations to identify obsolete data and optimize target storage costs. Identify low-latency queries: The Low-Latency Queries view analyzes query runtimes and highlights queries executing in under one second, specifically identifying candidates that would benefit from BigQuery BI Engine acceleration. Key advantages Automates the complex process of analyzing legacy data warehouse workloads. Reduces migration costs by identifying unused assets before they are moved. Provides actionable optimization recommendations for the target BigQuery environment. Groups similar query patterns to streamline SQL translation efforts. Why this is optimal Unlike generic data movement or metadata tools, the BigQuery migration assessment is purpose-built for the planning and technical inventory phase of a data warehouse migration. It directly parses source system logs to extract the exact usage metrics, dependencies, and performance characteristics required by the scenario.

Bigquery Migration Assessment Data Warehouse | Professional Cloud Data Engineer

AdvancedProfessional Cloud Data Engineer

You are formulating a target state migration plan to move an on-premises data processing environment to Google Cloud VMware Engine. The environment consists of interconnected data integration applications, large database servers, and several VMs with pending system updates. You need to design a phased migration roadmap that minimizes downtime, avoids multiple power cycles for VMs, and mitigates the risk of resource contention in the target architecture. Which strategy should you incorporate into your migration plan?

Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state

AdvancedProfessional Cloud Data Engineer

You are conducting a stakeholder and user-centric analysis for an enterprise migrating its legacy data warehouse to Google Cloud. Through your analysis, you have documented the following requirements and constraints: Business Analysts (Downstream Users): Require immediate performance improvements for their BI dashboards. Their interaction pattern involves heavy concurrent querying. Data Engineering Team: Currently manages complex batch ETL pipelines writing to the legacy warehouse. They lack the capacity to rewrite these upstream pipelines for at least six months. Business Leadership: Requires measurable success criteria showing user adoption of BigQuery within the next 30 days. How should you design the initial phase of the migration to satisfy these stakeholder requirements?

Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state

AdvancedProfessional Cloud Data Engineer

An enterprise is designing a multi-environment data processing architecture across separate Development and Production Google Cloud environments. The architecture must satisfy the following governance and compliance requirements: Data Classification and Access Control: A centralized taxonomy must classify sensitive personally identifiable information (PII). Production analysts with explicit clearance must be able to view cleartext values, while developers and testers in the development environment must only see masked data. Cryptographic Security: All persistent data in BigQuery datasets and Cloud Storage buckets must be encrypted using Customer-Managed Encryption Keys (CMEK) via Cloud KMS/Cloud HSM, with strict cryptographic key isolation between environments. Lifecycle Management: Development storage buckets must automatically delete intermediate test data older than 30 days to limit exposure and reduce costs, whereas production audit data must be retained under an immutable compliance policy. Which combination of architectural controls and services best satisfies these requirements?

Multi-environment use cases (development vs. production)

AdvancedProfessional Cloud Data Engineer

An enterprise analytics team needs to enrich several terabytes of unstructured customer feedback records already stored in BigQuery standard tables. The team wants to leverage a Gemini foundation model hosted on Vertex AI to perform sentiment summarization and structured entity extraction. The solution must satisfy the following criteria: Execute large-scale batch inference directly on data residing in the warehouse using standard SQL Avoid moving or exporting data to external intermediate storage Minimize operational overhead by eliminating the need to manage dedicated endpoint virtual machines or custom serving infrastructure Which architecture should the data engineering team implement?

AI data enrichment

AdvancedProfessional Cloud Data Engineer

An enterprise data platform running on Google Cloud utilizes Google Cloud Backup and DR Service to protect business-critical databases and data workloads across multiple zones and regions. The analytics engineering team establishes stringent business continuity metrics: Recovery Point Objective (RPO): Under 1 hour for high-change transactional staging data Recovery Time Objective (RTO): Near-instantaneous recovery to enable fast failover and data validation during disaster recovery drills During high-volume periods, the backup storage snapshot performance pool approaches safe capacity thresholds due to rapid snapshot creation and high data change rates. Which strategy should the team implement to alleviate snapshot pool exhaustion while preserving the defined RPO and near-zero RTO targets?

Disaster recovery and fault tolerance

IntermediateProfessional Cloud Architect

API Major Versioning with Path-Based Routing

API management best practices (e.g., Apigee)

Professional Cloud Data Engineer

Bigquery Migration Assessment Data Warehouse

Related questions

API Major Versioning with Path-Based Routing

Professional Cloud Data Engineer

Bigquery Migration Assessment Data Warehouse

What the BigQuery migration assessment is

How it meets the requirements

Key advantages

Why this is optimal

Related questions

API Major Versioning with Path-Based Routing

What the BigQuery migration assessment is

How it meets the requirements

Key advantages

Why this is optimal