Databricks-Certified-Data-Engineer-Professional Vorbereitung - Databricks-Certified-Data-Engineer-Professional Unterlage

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

Section	Objectives
Topic 1: Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture
Topic 2: Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement
Topic 3: Data Ingestion and Processing	- ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader
Topic 4: Production Pipelines and Orchestration	- Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies
Topic 5: Data Modeling and Transformation	- Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations

>> Databricks-Certified-Data-Engineer-Professional Vorbereitung <<

Databricks Databricks-Certified-Data-Engineer-Professional Unterlage - Databricks-Certified-Data-Engineer-Professional Fragenpool

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q34-Q39):

34. Frage
A table named user_ltv is being used to create a view that will be used by data analysts 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:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All records from all columns will be displayed with the values in user_ltv.
B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
E. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

Antwort: D

Begründung:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
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.

35. Frage
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Set the configuration delta.deduplicate = true.
B. Perform an insert-only merge with a matching condition on a unique key.
C. VACUUM the Delta table after each batch completes.
D. Perform a full outer join on a unique key and overwrite existing data.
E. Rely on Delta Lake schema enforcement to prevent duplicate records.

Antwort: B

Begründung:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

36. Frage
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
B. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
C. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
D. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.

Antwort: D

Begründung:
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

37. Frage
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
B. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
C. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
D. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
E. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

Antwort: A

Begründung:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.

38. Frage
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
B. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
C. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
D. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
E. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor

Antwort: E

39. Frage
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