You are noticing job cluster is taking 6 to 8 mins to start which is delaying your job to finish on time, what steps you can take to reduce the amount of time cluster startup time

• A.Setup a second job ahead of first job to start the cluster, so the cluster is ready with re-sources when the job starts
• B.Use All purpose cluster instead to reduce cluster start up time
• C.Reduce the size of the cluster, smaller the cluster size shorter it takes to start the clus-ter
• D.Use cluster pools to reduce the startup time of the jobs
• E.Use SQL endpoints to reduce the startup time

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Data engineering team is required to share the data with Data science team and both the teams are using different workspaces in the same organizationwhich of the following techniques can be used to simplify sharing data across?
*Please note the question is asking how data is shared within an organization across multiple workspaces.

• A.Data Sharing
• B.Unity Catalog
• C.DELTA lake
• D.Use a single storage location
• E.DELTA LIVE Pipelines

Correct Answer: B

Explanation
The answer is the Unity catalog.
Diagram Description automatically generated

Unity Catalog works at the Account level, it has the ability to create a meta store and attach that meta store to many workspaces see the below diagram to understand how Unity Catalog Works, as you can see a metastore can now be shared with both workspaces using Unity Catalog, prior to Unity Catalog the options was to use single cloud object storage manually mount in the second databricks workspace, and you can see here Unity Catalog really simplifies that.
Diagram Description automatically generated with medium confidence

sorry for the inconvenience watermark was added because other people on Udemy are copying my questions and images.
duct features
https://databricks.com/product/unity-catalog

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Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?

• A.1.order_date = dbutils.widgets.get("widget_order_date")
  2.
  3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '${order_date }' ")
• B.1.order_date = dbutils.widgets.get("widget_order_date")
  2.
  3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'f{order_date }'")
• C.1.order_date = dbutils.widgets.get("widget_order_date")
  2.
  3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '{order_date}' ")
  (Correct)
• D.1.order_date = dbutils.widgets.get("widget_order_date")
  2.
  3.spark.sql("SELECT * FROM sales WHERE orderDate = order_date")
• E.1.order_date = dbutils.widgets.get("widget_order_date")
  2.
  3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'order_date' ")

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Which of the following python statement can be used to replace the schema name and table name in the query statement?

• A.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from schema_name.table_name"
• B.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = "select * from {schema_name}.{table_name}"
• C.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from { schema_name}.{table_name}"
• D.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from + schema_name +"."+table_name"

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At the end of the inventory process, a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data in-crementally, schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically.
Below is the auto loader to command to load the data, fill in the blanks for successful execution of below code.
1.spark.readStream
2..format("cloudfiles")
3..option("_______","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("_______", "true")
9..table(table_name))

• A.format, checkpointlocation, schemalocation, overwrite
• B.cloudfiles.format, checkpointlocation, cloudfiles.schemalocation, overwrite
• C.cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema
• D.cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, overwrite
• E.cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, append

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