Google Professional Data Engineer Practice Test - Questions Answers, Page 35

To help the marketing team establish a consistent BigQuery analytics spend each month, you can use BigQuery reservations to allocate dedicated slots for their queries. This provides predictable costs by reserving a fixed amount of compute resources.

BigQuery Reservations:

BigQuery Reservations allow you to purchase dedicated query processing capacity in the form of slots.

By reserving slots, you can control costs and ensure that the marketing team has the necessary resources for their queries without unexpected increases in spending.

Baseline Slots:

Setting a baseline of 500 slots without autoscaling ensures a consistent allocation of resources.

This provides a predictable monthly cost, as the marketing team will be billed for the reserved slots regardless of actual usage.

Billing Back:

The marketing team's usage can be billed back based on the fixed reservation cost, ensuring budget predictability.

This approach avoids the variability associated with on-demand billing, where costs can fluctuate based on query volume and complexity.

No Autoscaling:

By not enabling autoscaling, you prevent additional costs from being incurred due to temporary increases in query demand.

This fixed reservation ensures that the marketing team only uses the allocated 500 slots, maintaining a consistent monthly spend.

Google Data Engineer

Reference:

BigQuery Reservations Documentation

BigQuery Slot Reservations

Managing BigQuery Costs

Using a fixed reservation of 500 slots provides the marketing team with predictable costs and the necessary resources for their queries without unexpected billing variability.

To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:

BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.

Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.

Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub/Sub topic upon failure, you can create a decoupled and scalable notification system.

Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.

Implementation Steps:

Set up a BigQuery Scheduled Query:

Create a scheduled query in BigQuery to run your SQL transformation every two hours.

Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.

Create a Pub/Sub Topic:

Create a Pub/Sub topic that will receive messages from the scheduled query.

Develop a Cloud Function:

Write a Cloud Function that subscribes to the Pub/Sub topic.

Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.

BigQuery Scheduled Queries

Pub/Sub Documentation

Cloud Functions Documentation

SendGrid Email API

Gmail API

To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:

Apache Beam and Dataflow:

Apache Beam SDK provides a unified programming model for both batch and stream data processing.

Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.

Customization for Different Use Cases:

By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.

This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.

Data Masking with Cloud DLP:

Google Cloud Data Loss Prevention (DLP) API can be integrated into your Apache Beam pipeline to de-identify and mask sensitive data programmatically before loading it into BigQuery.

This ensures that sensitive data is handled securely and complies with privacy requirements.

Cost Efficiency:

Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.

The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.

Implementation Steps:

Set up Apache Beam Pipeline:

Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.

Add transformations for data processing, including the integration with Cloud DLP for data masking.

Configure Dataflow:

Deploy the Apache Beam pipeline on Google Cloud Dataflow.

Customize the pipeline options for both streaming and batch use cases.

Load Data into BigQuery:

Set BigQuery as the sink for your data in the Apache Beam pipeline.

Ensure the processed and masked data is loaded into the appropriate BigQuery tables.

Apache Beam Documentation

Google Cloud Dataflow Documentation

Google Cloud DLP Documentation

BigQuery Documentation

To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:

Data Mesh Architecture:

Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.

Dataplex Lakes and Zones:

Lakes in Dataplex are logical containers for managing data at scale, and zones are subdivisions within lakes for organizing data based on domains, teams, or other criteria.

Domain and Team Management:

By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.

This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.

Implementation Steps:

Create Lakes and Zones:

Create separate lakes in Dataplex for each team (analytics and data science).

Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).

Attach BigQuery Datasets:

Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.

Decentralized Management:

Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.

Dataplex Documentation

BigQuery Documentation

Data Mesh Principles

To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:

Retention Policy:

A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.

Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.

Locking the Retention Policy:

Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.

Steps to Implement:

Set the Retention Policy:

Define a retention period for the bucket to ensure that all objects are protected for the required duration.

Lock the Retention Policy:

Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.

Cloud Storage Retention Policy Documentation

How to Set a Retention Policy

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 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