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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
| Topic 2: Data Ingestion | 15-20% | - Streaming ingestion
|
| Topic 3: Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Topic 4: Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Topic 5: Delta Lake | 20-25% | - Delta Lake fundamentals
|
>> Databricks-Certified-Professional-Data-Engineer최신 업데이트버전 인증시험자료 <<
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질문 # 149
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors' group can view the job but not modify/run it.
* Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?
정답：B
설명：
Comprehensive and Detailed Explanation from Databricks Documentation:
Databricks Asset Bundles (DABs) allow jobs, clusters, and permissions to be defined as code in YAML configuration files. According to the Databricks documentation on job permissions and bundle deployment, when defining permissions within a job resource, they must be scoped directly under that specific job's definition. This ensures that permissions are applied only to the intended job resource and not inadvertently propagated to other jobs or resources.
In this scenario, the data engineer must grant the data-engineers group CAN_MANAGE access, allowing them to configure, edit, and manage the job, while the auditors group should only have CAN_VIEW, giving them read-only access to see configurations and results without the ability to modify or execute. Importantly, no additional groups should be granted permissions, in order to follow the principle of least privilege.
Options A and B introduce unnecessary or unintended groups (like admin-team in A) or define permissions outside of the job scope (as in B). Option C improperly separates the permissions block outside the job resource, which is not aligned with Databricks bundle best practices.
Option D is the correct approach because it defines the job resource my-job with its name, tasks, clusters, and the exact intended permissions (CAN_MANAGE for data-engineers and CAN_VIEW for auditors). This aligns with Databricks' principle of least privilege and ensures compliance with governance standards in Unity Catalog-enabled workspaces.
질문 # 150
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?
정답：D
설명：
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id. While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.
질문 # 151
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?
정답：C
설명：
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines. Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features" section.
질문 # 152
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?
정답：D
설명：
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. Reference:
Grant privileges on a database: https://docs.databricks.com/en/security/auth-authz/table-acls/grant-privileges-database.html Privileges you can grant on Hive metastore objects: https://docs.databricks.com/en/security/auth-authz/table-acls/privileges.html
질문 # 153
What is the main difference between the silver layer and gold layer in medallion architecture?
정답：B
설명：
Explanation
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence
질문 # 154
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