Explanation
The answer is Single job can be used to set up both notebook and DLT pipeline, use two different tasks with linear dependency, Here is the JOB UI
1.Create a notebook task
2.Create DLT task
a.add notebook task as dependency
3.Final view
Create the notebook task
Graphical user interface, text, application, email Description automatically generated

DLT task
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Final view
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Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.schemaLocation" option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. Reference:
Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion/auto-loader/schema.html Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Create an external table" section.

Explanation
Smooth the estimates:consider flipping a coin for which the probability of heads is p, where p is unknown, and
our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide
by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very
reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get
heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it
seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such
rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or
add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of
flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is
equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is
that it avoids estimating any probabilities to be zero, even for events never observed in the data. Laplace
add-one smoothing now assigns too much probability to unseen words