Google Cloud Certified Professional Data Engineer Exam Questions

To ensure that the advertising department receives messages within 30 seconds of the click occurrence, and given the current system lag and data freshness metrics, the issue likely lies in the processing capacity of the Dataflow job. Here's why option B is the best choice:

System Lag and Data Freshness:

The system lag of 5 seconds indicates that Dataflow itself is processing messages relatively quickly.

However, the data freshness of 40 seconds suggests a significant delay before processing begins, indicating a backlog.

Backlog in Pub/Sub Subscription:

A backlog occurs when the rate of incoming messages exceeds the rate at which the Dataflow job can process them, causing delays.

Optimizing the Dataflow Job:

To handle the incoming message rate, the Dataflow job needs to be optimized or scaled up by increasing the number of workers, ensuring it can keep up with the message inflow.

Steps to Implement:

Analyze the Dataflow Job:

Inspect the Dataflow job metrics to identify bottlenecks and inefficiencies.

Optimize Processing Logic:

Optimize the transformations and operations within the Dataflow pipeline to improve processing efficiency.

Increase Number of Workers:

Scale the Dataflow job by increasing the number of workers to handle the higher load, reducing the backlog.

Dataflow Monitoring

Scaling Dataflow Jobs

To ensure that all employees can search and find tables with GDPR tags while restricting data access to sensitive tables only to the HR group, follow these steps:

Data Catalog Tag Template:

Use Data Catalog to create a tag template named 'gdpr' with a boolean field 'has sensitive data'. Set the visibility to public so all employees can see the tags.

Roles and Permissions:

Assign the datacatalog.tagTemplateViewer role to the all employees group. This role allows users to view the tags and search for tables based on the 'has sensitive data' field.

Assign the bigquery.dataViewer role to the HR group specifically on tables that contain sensitive data. This ensures only HR can access the actual data in these tables.

Steps to Implement:

Create the GDPR Tag Template:

Define the tag template in Data Catalog with the necessary fields and set visibility to public.

Assign Roles:

Grant the datacatalog.tagTemplateViewer role to the all employees group for visibility into the tags.

Grant the bigquery.dataViewer role to the HR group on tables marked as having sensitive data.

Data Catalog Documentation

Managing Access Control in BigQuery

IAM Roles in Data Catalog

To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:

ELT Development Technique:

ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.

BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.

Dataform:

Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.

It provides tools for managing SQL as code, including version control and collaborative development.

Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.

Intuitive Coding Environment:

Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.

It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.

Managing SQL as Code:

Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.

This allows for better collaboration, code reviews, and version tracking.

Dataform Documentation

BigQuery Documentation

Managing ELT Pipelines with Dataform

To resolve issues related to increased memory usage and worker pod evictions in your Cloud Composer 2 environment, the following steps are recommended:

Increase Memory Available to Airflow Workers:

By increasing the memory allocated to Airflow workers, you can handle more memory-intensive tasks, reducing the likelihood of pod evictions due to memory limits.

Increase Maximum Number of Workers and Reduce Worker Concurrency:

Increasing the number of workers allows the workload to be distributed across more pods, preventing any single pod from becoming overwhelmed.

Reducing worker concurrency limits the number of tasks that each worker can handle simultaneously, thereby lowering the memory consumption per worker.

Steps to Implement:

Increase Worker Memory:

Modify the configuration settings in Cloud Composer to allocate more memory to Airflow workers. This can be done through the environment configuration settings.

Adjust Worker and Concurrency Settings:

Increase the maximum number of workers in the Cloud Composer environment settings.

Reduce the concurrency setting for Airflow workers to ensure that each worker handles fewer tasks at a time, thus consuming less memory per worker.

Cloud Composer Worker Configuration

Scaling Airflow Workers

To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:

Clustering in BigQuery:

Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.

Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.

Filter Efficiency:

With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.

This directly addresses the performance issue of the dashboard queries that apply filters on these fields.

Steps to Implement:

Redesign the Table:

Create a new table with clustering on countryname and username:

CREATE TABLE project.dataset.new_table

CLUSTER BY countryname, username AS

SELECT * FROM project.dataset.customer_order;

Migrate Data:

Transfer the existing data from the original table to the new clustered table.

Update Queries:

Modify the dashboard queries to reference the new clustered table.

BigQuery Clustering Documentation

Optimizing Query Performance