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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q122-Q127):

질문 # 122
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
    @dlt.expect_all_or_drop(rules)
    def trips_data_quarantine():
    return spark.readStream.table("raw_trips_data")
  • B. @dlt.table(name="trips_data_quarantine")
    def trips_data_quarantine():
    return (
    spark.readStream.table("raw_trips_data")
    .filter(expr(quarantine_rules))
    )
  • C. @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))
    )
  • 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")

정답：B

설명：
Comprehensive and Detailed
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).
Why not the others?
B: Uses @dlt.expect_or_drop, which drops invalid records instead of quarantining them. This violates the requirement that quarantined data should be available.
C: Same as B, but applies expectations in bulk with expect_all_or_drop. Again, bad data is dropped, not quarantined.
D: Adds an is_quarantined flag in the same table. While it marks bad records, it does not separate them into a distinct quarantine table as required by the business use case.
Therefore, Option A is the only solution aligned with Databricks documentation for quarantining invalid data into a dedicated table while keeping valid data in the main pipeline.


질문 # 123
The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

  • A. The job_id and number of times the job has been run are concatenated and returned.
  • B. The job_id is returned in this field.
  • C. The number of times the job definition has been run in this workspace.
  • D. The globally unique ID of the newly triggered run.

정답：D

설명：
* Exact extract: "run_id: The canonical identifier of a run."
References: Databricks Jobs API/CLI response fields.


질문 # 124
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

  • A. The source query failed to update properly for three consecutive minutes and then restarted
  • B. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
  • C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
  • D. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
  • E. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query

정답：C

설명：
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.


질문 # 125
Which statement describes Delta Lake Auto Compaction?

  • A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  • B. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
  • C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
  • D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
  • E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

정답：B

설명：
Explanation
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size


질문 # 126
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

  • A. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
  • B. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  • C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  • D. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
  • E. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

정답：A

설명：
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.


질문 # 127
......

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