Databricks Databricks Certified Data Engineer Professional Exam - Databricks-Certified-Data-Engineer-Professional Valid Dumps

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

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

Databricks Certified Data Engineer Professional Sample Questions:

1. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A) DESCRIBE DETAIL dev.pii test
B) SHOW TBLPROPERTIES dev.pii test
C) DESCRIBE EXTENDED dev.pii test
D) DESCRIBE HISTORY dev.pii test
E) SHOW TABLES dev


2. A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

A) Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.
B) Use Unity Catalog's row filters based on the user roles and column masks based on the region.
C) Create dynamic views for each user role and manage access with ACLs.
D) Use Unity Catalog's row filters based on the region and column masks based on user roles.


3. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
B) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C) A user can only transfer job ownership to a group if they are also a member of that group.
D) Other than the default "admins" group, only individual users can be granted privileges on jobs.
E) Only workspace administrators can grant "Owner" privileges to a group.


4. A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A) The table name change is recorded in the Delta transaction log.
B) A new Delta transaction log Is created for the renamed table.
C) The table reference in the metastore is updated and all data files are moved.
D) The table reference in the metastore is updated and no data is changed.
E) All related files and metadata are dropped and recreated in a single ACID transaction.


5. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A)

B)

C)

D)

E)


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: C

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