A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

• A.Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
• B.The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
• C.Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
• D.One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
• E.The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

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Which of the statements are incorrect when choosing between lakehouse and Datawarehouse?

• A.Lakehouse can have special indexes and caching which are optimized for Machine learning
• B.Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.
• C.Lakehouse can be accessed through various API's including but not limited to Py-thon/R/SQL
• D.Traditional Data warehouses have storage and compute are coupled.
• E.Lakehouse uses standard data formats like Parquet.

Correct Answer: B

Explanation
The answer is Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.
Lakehouse can replace traditional warehouses by leveraging storage and compute optimizations like caching to serve them with low query latency with high reliability.
Focus on comparisons between Spark Cache vs Delta Cache.
https://docs.databricks.com/delta/optimizations/delta-cache.html
What Is a Lakehouse? - The Databricks Blog
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You are working on a process to query the table based on batch date, and batch date is an input parameter and expected to change every time the program runs, what is the best way to we can parameterize the query to run without manually changing the batch date?

• A.Create a notebook parameter for batch date and assign the value to a python variable and use a spark data frame to filter the data based on the python variable
• B.Create a dynamic view that can calculate the batch date automatically and use the view to query the data
• C.There is no way we can combine python variable and spark code
• D.Manually edit code every time to change the batch date
• E.Store the batch date in the spark configuration and use a spark data frame to filter the data based on the spark configuration.

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Which of the following are stored in the control pane of Databricks Architecture?

• A.Job Clusters
• B.All Purpose Clusters
• C.Databricks Filesystem
• D.Databricks Web Application
• E.Delta tables

Correct Answer: D

Explanation
The answer is Databricks Web Application
Azure Databricks architecture overview - Azure Databricks | Microsoft Docs Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
* The control plane includes the backend services that Databricks manages in its own Azure account.
Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
* The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
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A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

• A.Convert the list of configuration values to a dictionary of table settings, using table names as keys.
• B.Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
• C.Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
• D.Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

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