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Databricks Databricks-Certified-Data-Engineer-Professional Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Data Engineer Professional Exam
Exam Number:Databricks-Certified-Data-Engineer-Professional
Related Certifications:Databricks Certified Data Engineer Associate
Exam Price:USD 200
Available Languages:English, Japanese, Portuguese (Brazil), Korean
Exam Format:Multiple choice
Exam Duration:120 minutes
Real Exam Qty:59-60
Certificate Validity Period:2 years
Passing Score:Not publicly disclosed
Recommended Training:Databricks Streaming and Lakeflow Spark Declarative Pipelines
Advanced Data Engineering with Databricks
Exam Registration:Databricks Certification Exam Registration
Sample Questions:Databricks Databricks-Certified-Data-Engineer-Professional Sample Questions
Exam Way:Online proctored or in-person test center
Pre Condition:No mandatory prerequisites; 1+ years hands-on data engineering experience and related training highly recommended
Official Syllabus URL:https://www.databricks.com/learn/certification/data-engineer-professional

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

SectionWeightObjectives
Data Modelling6%- Optimize table design and partitioning
- Design Medallion Architecture
- Implement dimensional and relational models
Data Governance7%- Use Unity Catalog for governance
- Manage data assets and metadata
- Enforce data policies and standards
Monitoring and Alerting10%- Monitor pipeline performance and health
- Track data lineage and metrics
- Set up alerts and notifications
Developing Code for Data Processing using Python and SQL22%- Use Databricks-specific libraries and APIs
- Implement complex data processing logic
- Write efficient and maintainable code
Data Sharing and Federation5%- Use Delta Sharing for secure data sharing
- Manage cross-platform data access
- Implement Lakehouse Federation
Debugging and Deploying10%- Implement CI/CD and DevOps practices
- Troubleshoot and debug pipelines
- Deploy using Asset Bundles, CLI, and APIs
Cost & Performance Optimisation13%- Improve query and pipeline performance
- Apply cost management best practices
- Optimize compute and storage resources
Data Ingestion & Acquisition7%- Use Auto Loader and structured streaming
- Ingest data from diverse sources
- Handle incremental and batch data loads
Data Transformation, Cleansing, and Quality10%- Apply data cleansing and validation rules
- Implement schema evolution and management
- Enforce data quality standards
Ensuring Data Security and Compliance10%- Implement access control and permissions
- Secure data at rest and in transit
- Ensure data privacy and compliance

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes the default execution mode for Databricks Auto Loader?

A) Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
B) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
C) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
D) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
E) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.


2. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data is automatically clustered during write operations.
B) New data is written to a staging area and clustered during scheduled maintenance.
C) New data remains unclustered until the next OPTIMIZE operation.
D) New data is rejected if it doesn't match the clustering pattern.


3. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A) An external table will be created in the storage container mounted to /mnt/finance eda bucket.
B) A managed table will be created in the DBFS root storage container.
C) A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D) An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
E) A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.


4. A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A) df.withColumn ("user_email", F.sha2 ("user_email"))
B) df.withColumn ("user_emai", F.expr("uuid()"))
C) df.withColumn ("hashed_email", sha2 ("user_email"))
D) df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))


5. In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

A)

B)

C)

D)


Solutions:

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

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