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

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

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Databricks Certified-Data-Engineer-Professional Fragen Beantworten, Certified-Data-Engineer-Professional Zertifikatsfragen

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q26-Q31):

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

Antwort: D

Begründung:
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language-SQL

27. Frage
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
C. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
D. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
E. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

Antwort: C

Begründung:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed.

28. Frage
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 EXTENDED dev.pii test
B. DESCRIBE DETAIL dev.pii test
C. DESCRIBE HISTORY dev.pii test
D. SHOW TBLPROPERTIES dev.pii test
E. SHOW TABLES dev

Antwort: A

Begründung:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.

29. Frage
A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A. Only workspace admins can create and manage shares
B. Any user with USE_CATALOG privilege can create shares
C. Users need the MANAGE SHARES permission on the workspace
D. Users need to be metastore admins or have CREATE SHARE privilege for the metastore

Antwort: D

Begründung:
Creating and managing Delta Shares requires elevated governance privileges at the metastore level. Assigning users as metastore admins or granting them the CREATE SHARE privilege allows designated data stewards to create and configure shares within defined security boundaries, while preserving centralized auditability and access control.

30. Frage
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
B. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
C. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
D. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
E. The activity details table already exists; CHECK constraints can only be added during initial table creation.

Antwort: A

Begründung:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and
180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation.

31. Frage
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Certified-Data-Engineer-Professional Fragen Beantworten: https://www.zertsoft.com/Certified-Data-Engineer-Professional-pruefungsfragen.html