DATALAKEHOUSE V2 CERTIFICATION

In which of the following ways do serverless compute resources differ from classic compute resources within the Databricks Lakehouse Platform? Select two responses.

• They are located within the cloud

• They exist within the Databricks cloud account

• They exist within the customer cloud account

• They are always running and reserved for a single, specific customer when needed

• They result in lower costs by not overprovisioning

Which of the following architecture benefits is provided directly by the Databricks Lakehouse Platform? Select three responses.

• Scalable, redundant cloud-based data storage

• Efficient on-premises optimized hardware

• Unified security and governance approach for all data assets

• Built on open source and open standards

• Available on and across multiple clouds

A data architect is evaluating data warehousing solutions for their organization to use. As a part of this, the architect is considering the Databricks Lakehouse Platform.

Which of the following is a benefit of using the Databricks Lakehouse Platform for warehousing? Select four responses.

• Engineering capabilities supporting warehouse source data

• Best available price/performance

• Built-in governance for single-source-of-truth data

• A rich ecosystem of business intelligence (BI) integrations

• Local development software to integrate with other capabilities

Maintaining and improving data quality is a major goal of modern data engineering.

Which of the following contributes directly to high levels of data quality within the Databricks Lakehouse Platform? Select two responses.

• Data expectations enforcement

• Business intelligence (BI) tool integrations

• Apache Spark’s data format flexibility

• Simplified machine learning model serving

• Table schema evolution

Which of the following compute resources is available in the Databricks Lakehouse Platform? Select two responses.

• On-premises clusters

• Serverless clusters

• Local Databricks SQL warehouses

• Classic clusters

• Serverless Databricks SQL warehouses

In the past, a lot of data engineering resources needed to be contributed to the development of tooling and other mechanisms for creating and managing data workloads. In response, Databricks developed and released a declarative ETL framework so data engineers can focus on helping their organizations get value from their data

Which of the following technologies is being described above? Select one response.

• Delta Lake

• Databricks Jobs

• Databricks SQL Queries

• Delta Live Tables

• Autologging

While the Databricks Lakehouse Platform provides support for many types of data, analytics, and machine learning workloads, some organizations prefer to continue using other preferred vendors for use cases like data ingestion, data transformation, business intelligence, and machine learning.

• Databricks can use cloud service provider capabilities to efficiently share data with other data tools and platforms.

• Databricks can be used locally to allow developers to manually integrate with other systems.

• Databricks can be integrated directly with a large number of Databricks partners.

• Databricks cannot be used alongside other big data tools and platforms.

• Databricks can be used on-premises to allow for secure, in-house integrations.

Many organizations use a variety of open-source and proprietary tools for data orchestration, but these tools often have their own limitations. To address the orchestration needs of these organizations, Databricks developed Databricks Workflows.

Which of the following is a benefit of using Databricks Workflows for orchestration purposes? Select two responses.

• Databricks Workflows supports automating workloads as long as they are not in notebooks

• Databricks Workflows provides multiple-task workflow functionality only for Delta Live Tables workloads

• Databricks Workflows provides Git-backed version control capabilities to notebooks

• Databricks Workflows supports workloads across multiple cloud service providers and tools

• Databricks Workflows supports tasks for data ingestion, data engineering, machine learning, and business intelligence (BI)

It can be challenging for a data lakehouse to provide both performance and scalability for all of its query-based workloads to the standards of a data warehouse and a data lake. As a result, Databricks has introduced a technology built atop Apache Spark to further speed up and scale these varied workloads.

Which of the following technologies is being described in the above statement? Select one response.

• AutoML

• Photon

• AutoML

• Unity Catalog

• Delta Lake

Which of the following Databricks Lakehouse Platform services or capabilities provides a data warehousing experience to its users? Select one response.

• Data Science and Engineering Workspace

• Unity Catalog

• Databricks SQL

• Delta Lake

• Databricks Machine Learning