A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

• A.Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
• B.Stop the existing pipeline; use the returned settings in a reset command
• C.Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
• D.Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline

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A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

• A.Use apply_changes without CDF and filter rows where _soft_deleted is true.
• B.Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.
• C.Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
• D.Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.

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How do you handle failures gracefully when writing code in Pyspark, fill in the blanks to complete the below statement
1._____
2.
3. Spark.read.table("table_name").select("column").write.mode("append").SaveAsTable("new_table_name")
4.
5._____
6.
7. print(f"query failed")

• A.try: failure:
• B.try: catch:
• C.try: except:
• D.try: fail:
• E.try: error:

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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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The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

• A.Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
• B.Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
• C.Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
• D.Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
• E.Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.

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