Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?

• A.Databricks Repos can store the single-source-of-truth Git repository
• B.Databricks Repos can facilitate the pull request, review, and approval process before merging branches
• C.Databricks Repos can merge changes from a secondary Git branch into a main Git branch
• D.Databricks Repos can commit or push code changes to trigger a CI/CD process
• E.Databricks Repos can be used to design, develop, and trigger Git automation pipelines

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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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What type of table is created when you create delta table with below command?
CREATE TABLE transactions USING DELTA LOCATION "DBFS:/mnt/bronze/transactions"

• A.Managed delta table
• B.External table
• C.Managed table
• D.Temp table
• E.Delta Lake table

Correct Answer: B

Explanation
Anytime a table is created using the LOCATION keyword it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/" format -> DELTA, JSON, CSV, PARQUET, TEXT I created the table command based on the above question, you can see it created an external table,

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An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

• A.date = spark.conf.get("date")
• B.input_dict = input()
  date= input_dict["date"]
• C.import sys
  date = sys.argv[1]
• D.date = dbutils.notebooks.getParam("date")
• E.dbutils.widgets.text("date", "null")
  date = dbutils.widgets.get("date")

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A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

• A.Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• B.Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
• C.Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
• D.Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• E.Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

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