What Is Azure Databricks?: 10 practice questions

Question 1 of 10

Which description best identifies Azure Databricks?

1. AA network security service for configuring identities and virtual networks
2. BA dedicated file archive for retaining unprocessed enterprise data without analytics
3. CA business intelligence desktop tool restricted to local report development
4. DA unified, open analytics platform for enterprise data, analytics, and AI solutions

Question 2 of 10

What does the data lakehouse architectural pattern combine?

1. AThe functions of virtual networks and identity directories
2. BThe benefits of data lakes and data warehouses
3. CThe pricing models of serverless and classic compute
4. DThe interfaces of notebooks and source control repositories

Question 3 of 10

Which Azure Databricks capability provides the unified governance model for the lakehouse?

1. AApache Spark Structured Streaming
2. BUnity Catalog
3. CDatabricks Runtime for Machine Learning
4. DA Databricks SQL warehouse

Question 4 of 10

What outcome does data engineering provide according to the Azure Databricks product overview?

1. ACompute that is permanently running and sized for the largest possible workload
2. BReports that are stored locally and disconnected from governed source data
3. CData that is available, clean, and modeled for efficient discovery and use
4. DNetworks that are isolated from every analytics and machine learning workload

Question 5 of 10

Which component supplies scalable compute for end users who run analytic queries?

1. AA SQL warehouse
2. BA Unity Catalog metastore
3. CA Git folder
4. DAn access control list

Question 6 of 10

Which language set is identified for composing ETL logic in Azure Databricks?

1. APython, HTML, and CSS
2. BScala, PowerShell, and Bicep
3. CSQL, Python, and Scala
4. DSQL, YAML, and Markdown

Question 7 of 10

Which technology processes streaming data and incremental changes in Azure Databricks?

1. AApache Spark Structured Streaming
2. BDeclarative Automation Bundles
3. CDatabricks Runtime for Machine Learning
4. DUnity Catalog access control lists

Question 8 of 10

An organization keeps separate copies of data for BI and machine learning, spends time synchronizing them, and wants scalable processing over one consistent foundation. Which architectural direction best fits?

1. AAdopt a lakehouse so BI and machine learning can use the same governed data foundation
2. BReplace both data systems with a source control repository that stores notebooks and libraries
3. CKeep separate lake and warehouse copies but add more frequent synchronization jobs between them
4. DMove the BI copy into local desktop files and leave machine learning on the existing lake

Question 9 of 10

Business users need to run lakehouse queries through a SQL interface, while administrators want the users insulated from cloud compute complexity. Which platform component should provide execution?

1. AA Git folder synchronized for analytic query execution
2. BA scalable SQL warehouse configured for analytic queries
3. CA Unity Catalog access list configured for analytic queries
4. DAn ML runtime cluster configured for analytic dashboards

Question 10 of 10

A team must process incremental records continuously and enforce a unified governance model over the resulting lakehouse data. Which pairing matches those two responsibilities?

1. ASQL warehouses for incremental processing and Git folders for governance
2. BAutomation Bundles for incremental processing and MLflow for governance
3. CStructured Streaming for incremental processing and Unity Catalog for governance
4. DUnity Catalog for incremental processing and SQL warehouses for governance