Databricks-Certified-Professional-Data-Engineer Schulungsunterlagen, Databricks-Certified-Professional-Data-Engineer Prüfungsfragen

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

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

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Databricks Databricks-Certified-Professional-Data-Engineer Prüfung Übungen und Antworten

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q82-Q87):

82. Frage
A particular job seems to be performing slower and slower over time, the team thinks this started to happen when a recent production change was implemented, you were asked to take look at the job history and see if we can identify trends and root cause, where in the workspace UI can you perform this analysis?

A. Under Compute UI, select Job cluster and select the job cluster to see last 60 day his-torical runs
B. Under Workspace logs, select job logs and select the job you want to monitor to view the last 60 day historical runs
C. Under jobs UI select the job cluster, under spark UI select the application job logs, then you can access last 60 day historical runs
D. Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run
E. Historical job runs can only be accessed by REST API

Antwort: D

Begründung:
Explanation
The answer is,
Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run

83. Frage
While investigating a data issue, you wanted to review yesterday's version of the table using below command, while querying the previous version of the table using time travel you realized that you are no longer able to view the historical data in the table and you could see it the table was updated yesterday based on the table history(DESCRIBE HISTORY table_name) command what could be the reason why you can not access this data?
SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

A. You currently do not have access to view historical data
B. By default, historical data is cleaned every 180 days in DELTA
C. Time travel is disabled
D. A command VACUUM table_name RETAIN 0 was ran on the table
E. Time travel must be enabled before you query previous data

Antwort: D

Begründung:
Explanation
The answer is, VACUUM table_name RETAIN 0 was ran
The VACUUM command recursively vacuums directories associated with the Delta table and re-moves data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. The default is 7 Days.
When VACUUM table_name RETAIN 0 is ran all of the historical versions of data are lost time travel can only provide the current state.

84. Frage
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

A. f"{region}{store}_sales_{year}"
B. "{region}+{store}+"_sales_"+{year}"
C. "{region}+{store}+_sales_+{year}"
D. "{region}{store}_sales_{year}"
E. f"{region}+{store}+_sales_+{year}"

Antwort: A

85. Frage
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse.
The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Antwort: C

Begründung:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

86. Frage
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
C. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
D. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Antwort: D

Begründung:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.
References:
* Databricks best practices for securing data: https://docs.databricks.com/security/index.html

87. Frage
......

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