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. 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.
• B. 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.
• C. 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.
• D. 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.
• E. 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.

A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

• A. Set the pipelines, reset, allowed property to false on raw_iot
• B. Set the pipelines, reset, allowed property to false on bpm_stats
• C. Set the SkipChangeCommits flag to true raw_lot
• D. Set the skipChangeCommits flag to true on bpm_stats

A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

• A. @dlt.table(partition_cols=["is_quarantined", ])
  @dlt.expect_all(rules)
  def trips_data_quarantine():
  return (
  spark.readStream.table("raw_trips_data")
  .withColumn("is_quarantined", expr(quarantine_rules))
  )
• B. @dlt.table
  @dlt.expect_all_or_drop(rules)
  def trips_data_quarantine():
  return spark.readStream.table("raw_trips_data")
• C. @dlt.table(name="trips_data_quarantine")
  def trips_data_quarantine():
  return (
  spark.readStream.table("raw_trips_data")
  .filter(expr(quarantine_rules))
  )
• D. @dlt.view
  @dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
  return spark.readStream.table("ride_and_go.telemetry.trips")

A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

• A. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
• B. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
• C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
• D. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
• E. The write will fail completely because of the constraint violation and no records will be inserted into the target table.