A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

• A.Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
• B.Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
• C.Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
• D.Modify the overwrite logic to include a field populated by calling
  spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
• E.Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

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A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

• A.Use Repos to merge all differences and make a pull request back to the remote repository.
• B.Use repos to merge all difference and make a pull request back to the remote repository.
• C.Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
• D.Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
• E.Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.

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A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

• A.Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
• B.Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
• C.Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
• D.Call/api/2.1/jobs/runs/list with the job_id and include_history parameters

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A data engineer wants to refactor the following DLT code, which includes multiple table definitions 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 these 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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An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

• A.Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
• B.Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
• C.Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
• D.Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
• E.Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

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