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What are all of the BigQuery operations that Google charges for?
You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: * Data engineers, which require lull data lake access * Analytic users, which require access to curated data You need to assign access rights to these two groups. What should you do?
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?
You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?
Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?
You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training dat a. However, when tested against new data, it performs poorly. What method can you employ to address this?
You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?
You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

Question 323 - Professional Data Engineer discussion

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You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

A.
1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Provide each data engineering team access to the virtual lake.
Answers
A.
1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Provide each data engineering team access to the virtual lake.
B.
1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone. 3. Assign permissions to the data engineering teams at the zone level.
Answers
B.
1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone. 3. Assign permissions to the data engineering teams at the zone level.
C.
1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
Answers
C.
1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
D.
1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
Answers
D.
1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
Suggested answer: D

Explanation:

This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12.By creating a Dataplex virtual lake for each data product, you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3.By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.

Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2.Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34.Option C is better than option A and B, but it does not create multiple zones for landing, raw, and curated data, which may compromise the security and quality of the data products5.Reference:

1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog

2: Data Mesh - 7 Effective Practices to Get Started - Confluent

3: Best practices | Dataplex | Google Cloud

4: Secure your lake | Dataplex | Google Cloud

5: Zones | Dataplex | Google Cloud

[6]: Managing a Data Mesh with Dataplex -- ROI Training

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asked 18/09/2024
EduBP srl De Sanctis
35 questions
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