최신업데이트버전Databricks-Certified-Professional-Data-Engineer최신버전덤프덤프문제공부

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

Section	Weight	Objectives
Topic 1: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Spark SQL queries and functions 2. Window functions 3. DataFrame operations and transformations - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques
Topic 2: Data Ingestion	15-20%	- Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Topic 3: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Task dependencies and orchestration 3. Monitoring and alerting
Topic 4: Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order - Delta Lake operations 1. Schema evolution and enforcement 2. Delta Live Tables 3. Merge, update, delete operations
Topic 5: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Data governance fundamentals 3. Bronze, silver, gold data layers

>> Databricks-Certified-Professional-Data-Engineer최신버전덤프 <<

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q281-Q286):

질문 # 281
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Three columns will be returned, but one column will be named "redacted" and contain only null values.
B. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
C. The email and ltv columns will be returned with the values in user itv.
D. Only the email and itv columns will be returned; the email column will contain all null values.
E. The email, age. and ltv columns will be returned with the values in user ltv.

정답：B

설명：
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

질문 # 282
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository.
Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use Repos to merge all differences and make a pull request back to the remote repository.
B. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
C. Use repos to merge all difference and make a pull request back to the remote repository.
D. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

정답：B

설명：
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
:
Databricks documentation on using Repos with Git: https://docs.databricks.com/repos.html

질문 # 283
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.sql("ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days')")
B. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "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：A

설명：
Comprehensive and Detailed
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 # 284
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. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D. No: the change data feed only tracks inserts and updates not deleted records.

정답：C

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 certainretention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

질문 # 285
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity.
Which SQL query should the team run from the System catalog to achieve this?

A. SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B. SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C. SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D. SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10

정답：D

설명：
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non-existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

질문 # 286
......

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최신업데이트버전Databricks-Certified-Professional-Data-Engineer최신버전덤프덤프문제공부

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

인기자격증 Databricks-Certified-Professional-Data-Engineer최신버전덤프 시험덤프공부

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q281-Q286):