The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

• A.Model, predict (df, columns)
• B.Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
• C.Df. Select (''customer_id''.
  Model (''columns) alias (''predictions'')
• D.Df.apply(model, columns). Select (''customer_id, prediction''

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Which of the following table constraints that can be enforced on Delta lake tables are supported?

• A.Primary key, foreign key, Not Null, Check Constraints
• B.Primary key, Not Null, Check Constraints
• C.Default, Not Null, Check Constraints
• D.Not Null, Check Constraints
• E.Unique, Not Null, Check Constraints

Correct Answer: D

Explanation
The answer is Not Null, Check Constraints
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-constraints
* CREATE TABLE events( id LONG,
* date STRING,
* location STRING,
* description STRING
* ) USING DELTA;
ALTER TABLE events CHANGE COLUMN id SET NOT NULL;
ALTER TABLE events ADD CONSTRAINT dateWithinRange CHECK (date > '1900-01-01'); Note: Databricks as of DBR 11.1 added support for Primary Key and Foreign Key when Unity Catalog is enabled but this is for information purposes only these are not actually enforced. You may ask then why are we defining these if they are not enforced, so especially these information constraints are very helpful if you have a BI tool that can benefit from knowing the relationship between the tables, so it will be easy when creating reports/dashboards or understanding the data model when using any Data modeling tool.
Primary and Foreign Key
Graphical user interface, text, application, email Description automatically generated

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A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

• A.Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
• B.Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
• C.Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
• D.By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

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The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

• A.A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
• B.The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
• C.An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
• D.An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
• E.No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

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An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

• A.A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
• B.An external table will be created in the storage container mounted to /mnt/finance eda bucket.
• C.A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
• D.An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
• E.A managed table will be created in the DBFS root storage container.

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