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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. Review the following error traceback:

Which statement describes the error being raised?

A) There is a type error because a DataFrame object cannot be multiplied.
B) There is no column in the table named heartrateheartrateheartrate
C) There is a syntax error because the heartrate column is not correctly identified as a column.
D) There is a type error because a column object cannot be multiplied.
E) The code executed was PvSoark but was executed in a Scala notebook.

2. Which statement describes the default execution mode for Databricks Auto Loader?

A) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
B) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
C) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
D) Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
E) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.

3. A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?

A) Use a shared user account and its OAuth client secret.
B) Use a service principal ID and its OAuth client secret.
C) Use a service principal with OAuth token federation.
D) Use a service principal and its Personal Access Token.

4. 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(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
B) @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))
)
C) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
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")

5. The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
B) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
C) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
E) No; the change data feed only tracks inserts and updates not deleted records.

Solutions: