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

Section	Objectives
Data Governance	- Govern enterprise data 1. Demonstrate understanding of the Unity Catalog permission inheritance model 2. Create and add descriptions and metadata to enterprise data to improve discoverability
Debugging and Deploying	- Debugging and Troubleshooting 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 3. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines - Deploying CI/CD 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment 2. Build and deploy Databricks resources using Databricks Asset Bundles
Monitoring and Alerting	- Monitoring 1. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 2. Use system tables for observability of resource utilization, cost, auditing, and workloads 3. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use Query Profile and Spark UI to monitor workloads - Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
Cost & Performance Optimization	- Optimize cost and performance 1. Apply Change Data Feed to address streaming table limitations and improve latency 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Design and implement scalable data models using Delta Lake to manage large datasets 3. Simplify data layout decisions and optimize query performance using liquid clustering 4. Design dimensional models for analytical workloads with efficient querying and aggregation
Data Sharing and Federation	- Share and federate data 1. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform 3. Configure Lakehouse Federation with appropriate governance across supported source systems
Ensuring Data Security and Compliance	- Ensuring Compliance 1. Implement compliant batch and streaming pipelines that detect and mask PII 2. Develop data purging solutions that comply with data retention policies - Applying Data Security Mechanisms 1. Use ACLs to secure workspace objects and enforce the principle of least privilege 2. Use row filters and column masks to protect sensitive table data 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 2. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 3. Explain the advantages and disadvantages of streaming tables compared to materialized views 4. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 6. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 7. Create pipeline components using control flow operators such as if/else and foreach 8. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools - Using Python and Tools for Development 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 3. Develop User-Defined Functions using Pandas/Python UDF

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

NEW QUESTION # 139
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?

A. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
B. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
C. Require the analytics team to use a tool that supports Delta table.
D. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.

Answer: A

Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.

NEW QUESTION # 140
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. latitude
B. Date
C. User_id
D. Post_id
E. Post_time

Answer: B

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 141
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Yields faster deployment and execution times
B. Improves the quality of your data
C. Ensures that all steps interact correctly to achieve the desired end result
D. Validates a complete use case of your application
E. Troubleshooting is easier since all steps are isolated and tested individually

Answer: E

NEW QUESTION # 142
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
B. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
C. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
D. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.

Answer: C

Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.

NEW QUESTION # 143
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. Perform an insert-only merge with a matching condition on a unique key.
B. Rely on Delta Lake schema enforcement to prevent duplicate records.
C. Set the configuration delta.deduplicate = true.
D. VACUUM the Delta table after each batch completes.
E. Perform a full outer join on a unique key and overwrite existing data.

Answer: A

Explanation:
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.

NEW QUESTION # 144
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