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

Section	Objectives
Topic 1: Data Governance	- Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model - Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data
Topic 2: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 2. Use Query Profiler and Spark UI to monitor workloads 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 4. Use system tables for resource, cost, audit, and workload monitoring
Topic 3: Cost & Performance Optimisation	- Delta Optimization 1. Apply data skipping and file pruning techniques 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling
Topic 4: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Topic 5: Ensuring Data Security and Compliance	- Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies - Data Security 1. Use row filters and column masks for sensitive data 2. Apply anonymization and pseudonymization techniques 3. Use ACLs to secure workspace objects and enforce least privilege
Topic 6: Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 2. Compare streaming tables and materialized views 3. Develop unit and integration tests for data processing code 4. Configure environments, dependencies, memory, and retry behavior 5. Use APPLY CHANGES APIs for change data capture 6. Use control flow operators in pipeline components 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 8. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader - Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Develop User-Defined Functions using Pandas/Python UDFs 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
Topic 7: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure sharing with external platforms using the open sharing protocol 2. Configure Databricks-to-Databricks Sharing 3. Share live Lakehouse data with external computing platforms
Topic 8: Data Transformation, Cleansing, and Quality	- Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data - Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations
Topic 9: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest data from message buses and cloud storage 2. Build append-only pipelines for batch and streaming data using Delta 3. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
Topic 10: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Understand Liquid Clustering versus partitioning and Z-Ordering 2. Design and implement scalable data models using Delta Lake 3. Optimize data layout using Liquid Clustering

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q145-Q150):

145. Frage
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
B. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
C. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
D. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
E. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Antwort: C

Begründung:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

146. Frage
What statement is true regarding the retention of job run history?

A. It is retained for 60 days, during which you can export notebook run results to HTML
B. It is retained until you export or delete job run logs
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, after which logs are archived
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Antwort: A

Begründung:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

147. Frage
Which of the following is true of Delta Lake and the Lakehouse?

A. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
B. Z-order can only be applied to numeric values stored in Delta Lake tables
C. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

Antwort: A

Begründung:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

148. Frage
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track updates to the static Delta table.
B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C. The checkpoint directory will be used to track state information for the unique keys present in the join.
D. Stream-static joins cannot use static Delta tables because of consistency issues.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.

Antwort: B

Begründung:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

149. Frage
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. Whenever a table is being created, make sure that the location keyword is used.
B. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
C. When tables are created, make sure that the external keyword is used in the create table statement.
D. Whenever a database is being created, make sure that the location keyword is used
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Antwort: A

Begründung:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

150. Frage
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