Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?

• A.Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
• B.Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
• C.Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("streaming_vw")
• D.Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("streaming_vw")
• E.Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")
• F.You can not create a view on streaming data source.

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Which of the following tool provides Data Access control, Access Audit, Data Lineage, and Data discovery?

• A.Lakehouse
• B.DELTA lake
• C.Unity Catalog
• D.Data Governance
• E.DELTA LIVE Pipelines

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You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

• A.AS DEFAULT (CAST(orderTime as DATE))
• B.GENERATED ALWAYS AS (CAST(orderTime as DATE))
  Correct)
• C.GENERATED DEFAULT AS (CAST(orderTime as DATE))
• D.AS (CAST(orderTime as DATE))
• E.Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process

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The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode.
what is the expected outcome after clicking Start to update the pipeline?

• A.All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated
• B.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped
• C.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional development and testing
• D.All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional development and testing
• E.All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline

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You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

• A.Databricks Repos allows multiple users to make changes
• B.Databricks Repos allows merge and conflict resolution
• C.Databricks Repos has a built-in version control system
• D.Databricks Repos automatically saves changes
• E.Databricks Repos allow you to add comments and select the changes you want to commit.

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