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Professional Cloud Data Engineer
Connecting to tools
Your organization uses a custom on-premises visualization to…

Professional Cloud Data Engineer

professional-cloud-data-engineer

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Description
Sample Question
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Practice Test

Secure On Premises Bi Tool Bigquery Integration

Section: Connecting to tools

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

Your organization uses a Looker dashboard connected to BigQuery to analyze sales performance. The dashboard displays the current month's data and relies on a query that joins a massive sales_fact table with multiple smaller dimension tables. Users are reporting high dashboard latency, and the repeated execution of these complex joins is driving up analytical costs. You need to optimize the data layout and leverage BigQuery BI Engine to minimize query latency and reduce costs. What should you do?

Connecting to tools

AdvancedProfessional Cloud Data Engineer

Your enterprise stores sensitive sales and customer data in BigQuery. The dataset implements row-level security (RLS) to restrict sales data access based on each manager's region (SESSION_USER()), along with column-level security policy tags to mask personally identifiable information (PII). You need to configure access for business intelligence (BI) visualization dashboards in Looker Studio so that multiple regional sales managers can view their respective metrics securely while strictly adhering to the principle of least privilege. Which configuration should you implement?

Connecting to tools

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 maintains hundreds of terabytes of clickstream logs stored in Amazon S3 within the us-east-1 region. The data analytics team needs to combine and analyze this remote data alongside core customer demographic tables residing natively in BigQuery in the US multi-region. The solution must satisfy the following constraints: Minimize cross-cloud egress charges and network bandwidth consumption. Eliminate the operational overhead of deploying, managing, or scheduling ETL/ELT pipelines. Enforce unified, fine-grained access governance across cloud providers using standard identity delegation. Which architecture should the team implement?

Designing for data and application portability (e.g., multi-cloud and data residency requirements)

AdvancedProfessional Cloud Data Engineer

An enterprise architecture team is designing a real-time streaming ingestion pipeline on Google Cloud using Cloud Pub/Sub to process events across multiple microservices. The Pub/Sub topic enforces an Apache Avro schema that evolves over time as upstream services add optional fields, creating multiple schema revisions. The downstream subscriber applications maintain their own reader schema and must reliably deserialize incoming binary-encoded messages that may have been published under different schema revisions. How should the subscriber application be architected to handle messages across evolving schema revisions?

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AdvancedProfessional Cloud DevOps Engineer

Your enterprise uses automated CI/CD pipelines that dynamically provision ephemeral Google Cloud projects to run integration tests and validate containerized workloads. These temporary environments create Compute Engine instances, Cloud Storage buckets, and analytical clusters before being destroyed. Your security team mandates the following governance requirements for all ephemeral environments: Prevent the generation of persistent service account key files across all short-lived resources. Mitigate data exfiltration risks by enforcing context-based perimeter security around Google Cloud APIs and storage resources. Allow developers to request audited, just-in-time elevated permissions for troubleshooting failed test runs without distributing persistent administrative privileges. Which combination of Google Cloud security controls and configuration management policies should you implement?

Managing ephemeral environments

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Last updated August 3, 2026

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Professional Cloud Data Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

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Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

Your organization uses a custom on-premises visualization tool to generate business intelligence reports. The tool currently connects to relational databases using standard SQL protocols and does not natively support the BigQuery API.

You need to integrate this tool with BigQuery to analyze large datasets. Security policies mandate that the connection must not traverse the public internet, and BigQuery resources must be protected from data exfiltration by restricting access strictly to your hybrid network environment.

How should you securely integrate the on-premises tool with BigQuery?

Install the BigQuery JDBC or ODBC drivers on the on-premises server. Route traffic through Cloud VPN or Cloud Interconnect and enable Private Google Access to access BigQuery without using public IPs. Configure a VPC Service Controls perimeter around the BigQuery resources.

Install the BigQuery JDBC or ODBC drivers on the on-premises server. Configure VPC Network Peering to route traffic from the on-premises network directly to BigQuery. Secure the connection using Identity-Aware Proxy (IAP).

Export the BigQuery data to an on-premises database using the BigQuery Data Transfer Service over Cloud VPN. Connect the visualization tool to the local database to avoid public internet exposure.

Modify the custom tool to integrate directly with the BigQuery API. Route traffic over the public internet and use Google Cloud Armor to restrict access to the on-premises network's public IP range.

Install the BigQuery JDBC or ODBC drivers on the on-premises server. Route traffic through Cloud VPN or Cloud Interconnect and enable Private Google Access to access BigQuery without using public IPs. Configure a VPC Service Controls perimeter around the BigQuery resources.

What the solution is

The Simba JDBC and ODBC drivers for BigQuery allow legacy or custom applications that rely on standard SQL protocols to connect to BigQuery without requiring native API integration. Private Google Access (or Private Service Connect) allows on-premises networks connected via Cloud VPN or Cloud Interconnect to reach Google APIs using internal IP addresses. VPC Service Controls creates a secure perimeter around Google Cloud resources to mitigate data exfiltration risks.

How it meets the requirements

Integrate without native API support: The JDBC/ODBC drivers act as a bridge, translating standard SQL queries from the custom tool into BigQuery API requests seamlessly.
No public internet: By configuring Private Google Access for on-premises hosts, API traffic from the on-premises server routes through the private VPN or Interconnect tunnel directly to Google's network, ensuring data never traverses the public internet.
Protect from data exfiltration: VPC Service Controls enforces a strict security perimeter that blocks any access to BigQuery from outside the authorized hybrid network, satisfying the zero-trust access requirements.

Key advantages

Seamless integration: Eliminates the need to rewrite custom applications to use REST or gRPC APIs, saving significant development time.
Enhanced security: Secures data in transit using private, dedicated network paths rather than relying on public endpoints.
Enterprise-grade protection: Provides robust exfiltration protection for sensitive data warehouses, ensuring data cannot be copied to unauthorized external buckets or projects.

Why this is optimal

This architecture seamlessly integrates legacy BI tools with modern cloud data warehouses while adhering to strict zero-trust and private networking mandates. It leverages purpose-built drivers for application compatibility and native Google Cloud networking security controls to ensure end-to-end protection.

Install the BigQuery JDBC or ODBC drivers on the on-premises server. Configure VPC Network Peering to route traffic from the on-premises network directly to BigQuery. Secure the connection using Identity-Aware Proxy (IAP).

Export the BigQuery data to an on-premises database using the BigQuery Data Transfer Service over Cloud VPN. Connect the visualization tool to the local database to avoid public internet exposure.

Modify the custom tool to integrate directly with the BigQuery API. Route traffic over the public internet and use Google Cloud Armor to restrict access to the on-premises network's public IP range.