Google Updated Professional-Data-Engineer Exam Questions and Answers by stevie

To securely connect multiple applications with dynamic public IP addresses to a Cloud SQL instance using public IP, the Cloud SQL Auth proxy is the best solution. This proxy provides secure, authorized connections to Cloud SQL instances without the need to configure authorized networks or deal with IP whitelisting complexities.

Cloud SQL Auth Proxy:

The Cloud SQL Auth proxy provides secure, encrypted connections to Cloud SQL.

It uses IAM permissions and SSL to authenticate and encrypt the connection, ensuring data security in transit.

By using the proxy, you avoid the need to constantly update authorized networks as the proxy handles dynamic IP addresses seamlessly.

Authorized Network Configuration:

Leaving the authorized network empty means no IP addresses are explicitly whitelisted, relying solely on the Auth proxy for secure connections.

This approach simplifies network management and enhances security by not exposing the Cloud SQL instance to public IP ranges.

Dynamic IP Handling:

Applications with dynamic IP addresses can securely connect through the proxy without the need to modify authorized networks.

The proxy authenticates connections using IAM, making it ideal for environments where application IPs change frequently.

Google Data Engineer References:

Using Cloud SQL Auth Proxy

Cloud SQL Security Overview

Setting up the Cloud SQL Auth Proxy

By using the Cloud SQL Auth proxy, you ensure secure, authorized connections for applications with dynamic public IPs without the need for complex network configurations.

Comprehensive and Detailed Explanation:

This question describes a classic, scalable streaming analytics architecture on Google Cloud.

Option C is the correct answer as it combines the best-in-class managed services for each part of the pipeline.

Pub/Sub is a fully managed, highly scalable messaging service perfect for ingesting fluctuating volumes of streaming IoT data.

Dataflow is a fully managed, serverless service for stream and batch processing that automatically scales resources up and down to handle fluctuating data volumes.

BigQuery is a serverless, highly scalable data warehouse optimized for analytics.

Cloud KMS is the standard Google Cloud service for creating and managing your own cryptographic keys, which are then used to enable Customer-Managed Encryption Keys (CMEK) across services like Pub/Sub, Dataflow, and BigQuery, satisfying the security requirement.

Option A is incorrect because it specifies using a Google-managed encryption key, which violates the requirement for encryption with "your own key."

Option B is incorrect because Cloud SQL is a relational database (OLTP), not an analytical data warehouse (OLAP), making it unsuitable for storing and analyzing large volumes of streaming data. BigQuery is the appropriate choice.

Option D is incorrect because Dataproc is a managed Hadoop/Spark service. While powerful, it is less "managed" and serverless than Dataflow, as you still need to provision and manage clusters. For a fully elastic and managed solution, Dataflow is the preferred choice.

Reference (Google Cloud Documentation Concepts):

This architecture is a canonical pattern for streaming analytics on Google Cloud. The "Streaming analytics" solution guide frequently highlights the Pub/Sub -> Dataflow -> BigQuery pattern. Each of these services supports Customer-Managed Encryption Keys (CMEK) using keys from Cloud KMS, ensuring data is protected at rest with customer-controlled keys, which aligns with the principle of customer control over data security.

When designing a data mesh on Google Cloud using Dataplex to manage data in BigQuery and Cloud Storage, it is essential to simplify data asset permissions while ensuring that each user group has the appropriate access levels. Here’s why option A is the best choice:

Data Engineer Group:

Data engineers require full access to the data lake to manage and operate data assets comprehensively. Granting the dataplex.dataOwner role to the data engineer group on the customer data lake ensures they have the necessary permissions to create, modify, and delete data assets within the lake.

Analytic User Group:

Analytic users need access to curated data but do not require full control over all data assets. Granting the dataplex.dataReader role to the analytic user group on the customer curated zone provides read-only access to the curated data, enabling them to analyze the data without the ability to modify or delete it.

Steps to Implement:

Grant Data Engineer Permissions:

Assign the dataplex.dataOwner role to the data engineer group on the customer data lake to ensure full access and management capabilities.

Grant Analytic User Permissions:

Assign the dataplex.dataReader role to the analytic user group on the customer curated zone to provide read-only access to curated data.

Reference Links:

Dataplex IAM Roles and Permissions

Managing Access in Dataplex

https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml