Google Professional Data Engineer Practice Test - Questions Answers, Page 37

To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:

Dual-Region Bucket:

A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.

The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.

Turbo Replication:

Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.

This minimizes data loss in case of a regional failure.

Running Dataproc Cluster:

Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.

In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.

Steps to Implement:

Create a Dual-Region Bucket:

Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.

Enable turbo replication to ensure rapid data replication between the regions.

Deploy Dataproc Cluster:

Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.

Set Up Failover Plan:

Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region.

Ensure that the failover process is well-documented and tested to minimize downtime and data loss.

Google Cloud Storage Dual-Region

Turbo Replication in Google Cloud Storage

Dataproc Documentation

To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:

Analytics Hub:

Analytics Hub allows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.

This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.

Data Publishing and Subscribing:

Teams can publish datasets they control, allowing them to manage access and updates independently.

Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.

Minimized Operational Tasks and Costs:

This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.

By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.

Steps to Implement:

Set Up Analytics Hub:

Enable Analytics Hub in your Google Cloud project.

Provide training to teams on how to publish and subscribe to data exchanges.

Publish Data:

Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.

Subscribe to Data:

Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.

Analytics Hub Documentation

Publishing Data in Analytics Hub

Subscribing to Data in Analytics Hub

To deploy a batch pipeline in Dataflow that adheres to the organizational constraint of using only internal IP addresses, ensuring Private Google Access is the most effective solution. Here's why option D is the best choice:

Private Google Access:

Private Google Access allows resources in a VPC network that do not have external IP addresses to access Google APIs and services through internal IP addresses.

This ensures compliance with the organizational constraint of using only internal IPs while allowing Dataflow to access Cloud Storage and BigQuery.

Dataflow with Internal IPs:

Dataflow can be configured to use only internal IP addresses for its worker nodes, ensuring that no external IP addresses are assigned.

This configuration ensures secure and compliant communication between Dataflow, Cloud Storage, and BigQuery.

Firewall and Network Configuration:

Enabling Private Google Access requires ensuring the correct firewall rules and network configurations to allow internal traffic to Google Cloud services.

Steps to Implement:

Enable Private Google Access:

Enable Private Google Access on the subnetwork used by the Dataflow pipeline

gcloud compute networks subnets update [SUBNET_NAME] \

--region [REGION] \

--enable-private-ip-google-access

Configure Dataflow:

Configure the Dataflow job to use only internal IP addresses

gcloud dataflow jobs run [JOB_NAME] \

--region [REGION] \

--network [VPC_NETWORK] \

--subnetwork [SUBNETWORK] \

--no-use-public-ips

Verify Access:

Ensure that firewall rules allow the necessary traffic from the Dataflow workers to Cloud Storage and BigQuery using internal IPs.

Private Google Access Documentation

Configuring Dataflow to Use Internal IPs

VPC Firewall Rules

To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here's why option C is the best choice:

Materialized Views:

Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.

They can be automatically refreshed to reflect changes in the underlying data.

Incremental Updates:

The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.

This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.

Performance and Maintenance:

By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.

This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.

Steps to Implement:

Create Materialized Views:

Define materialized views for the visualization queries with the necessary configurations

CREATE MATERIALIZED VIEW project.dataset.view_name

AS

SELECT ...

FROM ...

WHERE ...

Enable Incremental Updates:

Ensure that the materialized views are set up to handle incremental updates automatically.

Update the data visualization tool to reference the materialized views instead of running the original queries directly.

BigQuery Materialized Views

Optimizing Query Performance

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

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

Reference:

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.

To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here's why option B is the best choice:

Separate Reservations for SLA and Ad-hoc Projects:

Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.

The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.

Enterprise Edition Reservation for SLA Project:

Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.

Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.

On-Demand Billing for Ad-hoc Project:

Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.

This model suits the less predictable and often lower-utilization nature of ad-hoc queries.

Steps to Implement:

Set Up Enterprise Edition Reservation for SLA Project:

Create a reservation with a baseline of 300 slots.

Enable autoscaling to allow up to an additional 500 slots as needed.

Configure On-Demand Billing for Ad-hoc Project:

Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.

Monitor and Adjust:

Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.

BigQuery Slot Reservations

BigQuery On-Demand Pricing

To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:

Materialized Views:

Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.

They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.

Efficiency and Cost Reduction:

By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.

Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.

Minimized Maintenance:

Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.

Steps to Implement:

Identify Aggregation Queries:

Analyze the existing queries to identify common aggregation patterns at the day and month levels.

Create Materialized Views:

Create materialized views in BigQuery for the identified aggregation patterns. For example

CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS

SELECT

DATE(transaction_time) AS day,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

day;

CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS

SELECT

EXTRACT(YEAR FROM transaction_time) AS year,

EXTRACT(MONTH FROM transaction_time) AS month,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

year, month;

Query Using Materialized Views:

Update existing queries to use the materialized views instead of directly querying the base table.

BigQuery Materialized Views

Optimizing Query Performance