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Databricks Certified Professional Data Engineer Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen

Frage #1

A junior data engineer seeks to leverage Delta Lake ' s Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true . They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
C. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Frage #2

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
B. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
C. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
D. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
E. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

Frage #3

A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment . The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters.
Which approach should the data engineer use?

A. Add the .whl file to Databricks Git Repos and assume automatic installation.
B. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
C. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
D. Set up a private PyPI repository and install via pip index URL.

Frage #4

A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of rows will be returned from cached results unless REFRESH is run
B. The total count of records is calculated from the Delta transaction logs
C. The total count of records is calculated from the Hive metastore
D. The total count of records is calculated from the parquet file metadata
E. The total count of rows is calculated by scanning all data files

Frage #5

A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
* Trigger an alert when the daily average of percent_null exceeds 5% for three consecutive days .
* Ensure that notifications are not spammed during sustained issues.
Options:

A. WITH daily_avg AS (
SELECT DATE_TRUNC( ' DAY ' , window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC( ' DAY ' , window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once
B. SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end > = CURRENT_TIMESTAMP - INTERVAL ' 1 ' DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours
C. SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end > = CURRENT_TIMESTAMP - INTERVAL ' 3 ' DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated
D. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end > = CURRENT_TIMESTAMP - INTERVAL ' 3 ' DAY Alert Condition: violation_days > = 3 Notification Frequency: Just once

Fragen und Antworten: