2026 PassTIP 최신 Databricks-Certified-Professional-Data-Engineer PDF 버전 시험 문제집과 Databricks-Certified-Professional-Data-Engineer 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1wlJjHXXdHpdNuDrwQvWzuPHvIvodsDKD
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| Section | Objectives |
|---|---|
| Data Modeling and Storage | - Design scalable data lakehouse architectures - Delta Lake table design and optimization - Schema evolution and data partitioning strategies |
| Security, Governance, Monitoring, and Optimization | - Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads - Cost optimization and performance tuning |
| Production Pipelines and Orchestration | - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs |
| Data Ingestion and Transformation | - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks |
>> Databricks-Certified-Professional-Data-Engineer최고품질 덤프문제 <<
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질문 # 41
A data architect is deploying Delta Sharing as part of a data-governance strategy to support secure data collaboration with external partners and internal business units. The architect must define a permission framework that lets designated data stewards create shares for their respective domains while preserving security boundaries and audit compliance.
Which specific permissions and roles must be assigned to allow users to create, configure, and manage Delta Shares while preserving appropriate security governance and access controls?
정답：C
설명：
Delta Sharing uses Unity Catalog permissions to control the creation and administration of data shares.
To create a share, a user must be a metastore administrator or possess the CREATE SHARE privilege on the metastore.
This permits organizations to delegate share creation to authorized data stewards without granting unrestricted workspace administration privileges.
After creating a share, ownership and additional object-level permissions determine who can modify its contents and manage access.
Option A is incorrect because share creation is not restricted to workspace administrators. Option B describes an invalid workspace-level permission. Option D is incorrect because USE CATALOG permits access to a catalog but does not authorize creating Delta Shares.
Organizations should assign the minimum required privileges, manage share ownership carefully, and audit changes to recipient access and shared objects.
질문 # 42
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?
정답：C
질문 # 43
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should havefive workers,one driverof type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?
정답：C
설명：
Comprehensive and Detailed In-Depth Explanation:
TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
* Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).
* Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.
* Workers:--num-workers 5 sets the number of worker nodes to 5.
* Node Type:--node-type-id i3.xlarge defines the hardware configuration.
* Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
* Option A (databricks clusters create ...)
* Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.
* The correct CLI command for cluster creation is databricks compute create.
* Option B (databricks clusters add ...)
* Incorrect:databricks clusters addis not a valid CLI command.
* Option C (databricks compute add ...)
* Incorrect:databricks compute addis not a valid CLI command.
* Option D (databricks compute create ...)
* Correct:databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime
14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer isD.
References:
* Databricks CLI Documentation
질문 # 44
The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
정답：E
설명：
Explanation
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
질문 # 45
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?
정답：D
설명：
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
* Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick- start.html#sql-views
질문 # 46
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