Databricks-Certified-Professional-Data-Engineer유효한인증덤프최신시험최신덤프자료

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

Section	Objectives
Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines
Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance
Data Modeling and Storage	- Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization
Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control

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Databricks-Certified-Professional-Data-Engineer유효한 인증덤프 인기자격증 덤프공부

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q26-Q31):

질문 # 26
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.
Which code snippet correctly sets this retention period for deleted files?

A. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days')")
C. spark.sql("VACUUM my_table RETAIN 15 HOURS")
D. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"

정답：B

설명：
In Delta Lake, the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
By default, this retention duration is set to 7 days.
To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days') This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
B: This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
C: VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration.
D: Setting spark.conf applies a session-level configuration and does not permanently update the table's retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.

질문 # 27
A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A. Caching the DataFrame df.
B. Use broadcast join.
C. Repartition by region before aggregation.
D. Use coalesce() after the aggregation.

정답：C

설명：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documents that shuffle occurs when Spark redistributes data across partitions for grouping or joining. To optimize aggregation performance, repartitioning by the grouping key (region) ensures rows with the same key are co-located in the same partition, thus minimizing shuffle movement. Caching improves reuse of DataFrames but does not reduce shuffle volume. coalesce() reduces the number of partitions after computation and cannot prevent shuffle. Broadcast joins are unrelated to single-table aggregations. The recommended practice for reducing shuffle in aggregation is explicit repartitioning by the grouping column.

질문 # 28
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 overwritten and new customers are appended.
B. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
D. 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.
E. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

정답：D

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 # 29
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: the change data feed only tracks inserts and updates not deleted records.
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

정답：D

설명：
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

질문 # 30
Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

A. Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.
B. Use Incremental refresh to run at 8 AM every day.
C. A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.
D. Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard
E. The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.

정답：E

설명：
Explanation
The answer is,
The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at
8 AM.
Automatically refresh a dashboard
A dashboard's owner and users with the Can Edit permission can configure a dashboard to auto-matically refresh on a schedule. To automatically refresh a dashboard:
* Click the Schedule button at the top right of the dashboard. The scheduling dialog appears.
* Graphical user interface, text, application, email, Teams Description automatically generated
* 2.In the Refresh every drop-down, select a period.
* 3.In the SQL Warehouse drop-down, optionally select a SQL warehouse to use for all the queries.
If you don't select a warehouse, the queries execute on the last used SQL ware-house.
* 4.Next to Subscribers, optionally enter a list of email addresses to notify when the dashboard is automatically updated.
* Each email address you enter must be associated with a Azure Databricks account or con-figured as an alert destination.
* 5.Click Save. The Schedule button label changes to Scheduled.

질문 # 31
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