Databricks-Certified-Professional-Data-Engineer Zertifizierung - Databricks-Certified-Professional-Data-Engineer Übungsmaterialien

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

SectionWeightObjectives
Topic 1: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Bronze, silver, gold data layers
  • 2. Differences between data lake, data warehouse, and lakehouse
  • 3. Data governance fundamentals
Topic 2: Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Built-in and user-defined functions
  • 2. Performance optimization techniques
  • 3. Spark APIs in Python
- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions
Topic 3: Delta Lake20-25%- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Time travel and data versioning
  • 3. Optimize and Z-order
- Delta Lake operations
  • 1. Delta Live Tables
  • 2. Schema evolution and enforcement
  • 3. Merge, update, delete operations
Topic 4: Data Ingestion15-20%- Streaming ingestion
  • 1. Structured streaming fundamentals
  • 2. Kafka integration
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. DBR autoloader
  • 3. Spark APIs for ingestion
Topic 5: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Monitoring and alerting
  • 2. Jobs and job scheduling
  • 3. Task dependencies and orchestration

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q196-Q201):

196. Frage
How to determine if a table is a managed table vs external table?

Antwort: D

Begründung:
Explanation
The answer is Run SQL command DESCRIBE EXTENDED table_name and check type Example of External table Graphical user interface, text, application Description automatically generated

Example of managed table
Graphical user interface, text, application, Teams Description automatically generated


197. Frage
A table named user_ltv is being used to create a view that will be used by data analysis on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

Antwort: D

Begründung:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member( ' auditing ' ) condition. Records not meeting the age > 18 condition will not be displayed.


198. Frage
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

Antwort: E

Begründung:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. Reference:
https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html


199. Frage
A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog.
What describes the privilege inheritance behavior in Unity Catalog?

Antwort: D

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
In Unity Catalog, privileges are non-cascading-meaning that granting a privilege (like SELECT) on a catalog does not automatically grant the same privilege on contained objects (schemas, tables, or views). Each object type has its own independent access control hierarchy.
According to the Databricks access control documentation: "Privileges do not automatically cascade from catalog to schema or table levels." Administrators must explicitly grant privileges on each level if users need access across objects. This design ensures tighter governance and least-privilege enforcement. Therefore, option B correctly describes Unity Catalog's privilege model, while A and D incorrectly imply automatic inheritance.


200. Frage
You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability
of this words is coming in either email could be zero. So which of the following algorithm can help you to
avoid zero probability?

Antwort: C

Begründung:
Explanation
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more
robust and will not fail completely when data that has never been observed in training shows up.


201. Frage
......

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