Databricks Databricks-Certified-Professional-Data-Engineer Practice Test Material in 3 Different Formats

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

Section	Weight	Objectives
Topic 1: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs
Topic 2: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 3: Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization
Topic 4: Data Governance	7%	- Unity Catalog management - Policy enforcement - Data lineage and metadata tracking
Topic 5: Data Ingestion & Acquisition	7%	- Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion
Topic 6: Data Modelling	6%	- Medallion Architecture implementation - Schema design and management - Delta Lake table design
Topic 7: Monitoring and Alerting	10%	- Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging
Topic 8: Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching
Topic 9: Ensuring Data Security and Compliance	10%	- Data encryption and masking - Access control and permissions - Compliance standards implementation
Topic 10: Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
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. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
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. 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.
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.

Answer: C

Explanation:
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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.

NEW QUESTION # 140
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A. The five Minute Load Average remains consistent/flat
B. Total Disk Space remains constant
C. CPU Utilization is around 75%
D. Bytes Received never exceeds 80 million bytes per second
E. Network I/O never spikes

Answer: C

NEW QUESTION # 141
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?

A. 1. SELECT DISTINCT *
2. FROM my_table;
B. 1. DROP DUPLICATES
2. FROM my_table;
C. 1. SELECT *
2. FROM my_table
3. WHERE duplicate = False;
D. 1. MERGE INTO my_table a
2. USING new_records b;
E. 1. MERGE INTO my_table a
2. USING new_records b ON a.id = b.id
3. WHEN NOT MATCHED
4. THEN INSERT *;

Answer: A

NEW QUESTION # 142
You are looking to process the data based on two variables, one to check if the department is supply chain or check if process flag is set to True

A. if department == "supply chain" | process == TRUE:
B. if department == "supply chain" | if process == TRUE:
C. if department = "supply chain" | process:
D. if department == "supply chain" or process = TRUE:
E. if department == "supply chain" or process:

Answer: E

NEW QUESTION # 143
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data . They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline.
Which configuration should the team use when writing data to the Delta table?

A. overwriteSchema = true
B. validateSchema = false
C. mergeSchema = true
D. ignoreChanges = false

Answer: C

NEW QUESTION # 144
......

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