Google Professional Data Engineer Exam Questions

https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas.

Here's why creating a BigQuery reservation for the project is the most suitable solution:

Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.

Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.

BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.

Why other options are not suitable:

A . Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.

B . Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.

C . Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.

By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.

To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:

Compatibility with Hive:

Dataproc is a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.

This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.

Cost-Effective Storage:

Storing the ORC files in Cloud Storage is cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.

Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.

Hive Integration:

Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.

This setup enables the use of existing Hive queries and scripts without significant modifications.

Steps to Implement:

Copy ORC Files to Cloud Storage:

Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.

Deploy Dataproc Cluster:

Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.

Configure Hive:

Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.

Provide Access to Data Scientists:

Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.

Dataproc Documentation

Hive on Dataproc

Google Cloud Storage Documentation

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