Databricks Databricks-Certified-Data-Engineer-Professional dumps - in .pdf

Databricks-Certified-Data-Engineer-Professional pdf
  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Aug 14, 2026
  • Q & A: 250 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Aug 14, 2026
  • Q & A: 250 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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Databricks Databricks-Certified-Data-Engineer-Professional dumps - Testing Engine

Databricks-Certified-Data-Engineer-Professional Testing Engine
  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Aug 14, 2026
  • Q & A: 250 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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

SectionWeightObjectives
Topic 1: Data Processing28%- Spark SQL
- Structured Streaming
- ETL Pipelines
- Data Transformation
Topic 2: Databricks Lakehouse Platform24%- Unity Catalog
- Delta Lake
- Lakehouse Architecture
- Data Management
Topic 3: Data Quality and Governance12%- Governance
- Data Quality
- Data Lineage
Topic 4: Monitoring and Troubleshooting16%- Troubleshooting
- Monitoring
- Performance Optimization
Topic 5: Data Modeling and Storage20%- Storage Optimization
- Data Modeling
- File Formats

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

A) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER
B) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
C) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW
D) permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]


2. A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A) Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
B) Store the date as string in ISO format (YYYY-MM-DD) for readability.
C) Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
D) Store only the date value and calculate all time attributes in queries.


3. A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A) spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
B) from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
C) spark.sql("VACUUM my_table RETAIN 15 HOURS")
D) spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")


4. Which statement describes integration testing?

A) Requires an automated testing framework
B) Validates an application use case
C) Validates interactions between subsystems of your application
D) Validates behavior of individual elements of your application
E) Requires manual intervention


5. A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

A)

B)

C)

D)


Solutions:

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

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