Databricks Databricks-Certified-Data-Engineer-Professional시험패스가능한인증덤프자료 - Databricks-Certified-Data-Engineer-Professional인기덤프공부

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

Section	Weight	Objectives
Topic 1: Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules
Topic 2: Data Sharing and Federation	5%	- Implement Lakehouse Federation - Use Delta Sharing for secure data sharing - Manage cross-platform data access
Topic 3: Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions
Topic 4: Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs
Topic 5: Data Governance	7%	- Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards
Topic 6: Data Modelling	6%	- Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning
Topic 7: Monitoring and Alerting	10%	- Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics
Topic 8: Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Topic 9: Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines
Topic 10: Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming

>> Databricks Databricks-Certified-Data-Engineer-Professional시험패스 가능한 인증덤프자료 <<

시험패스에 유효한 Databricks-Certified-Data-Engineer-Professional시험패스 가능한 인증덤프자료 최신버전 덤프데모문제 다운로드

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q144-Q149):

질문 # 144
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
B. Rewrite their code to avoid putting memory pressure on the driver node.
C. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
D. Run the notebook on a single node cluster to keep driver from falling.

정답：B

설명：
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

질문 # 145
What is the first line of a Databricks Python notebook when viewed in a text editor?

A. # Databricks notebook source
B. -- Databricks notebook source
C. # MAGIC %python
D. %python
E. // Databricks notebook source

정답：A

설명：
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

질문 # 146
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

A. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
B. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
C. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
D. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

정답：B

설명：
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.

질문 # 147
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
B. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
C. When the workspace is being configured, make sure that external cloud object storage has been mounted.
D. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
E. When a database is being created, make sure that the LOCATION keyword is used.

정답：D

설명：
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

질문 # 148
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
B. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
C. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
D. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
E. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

정답：D

설명：
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.

질문 # 149
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