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

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

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q88-Q93):

88. Frage
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Partition the table by date.
B. Apply liquid clustering using the merge join keys.
C. Use ZORDER on high-cardinality columns.
D. Enable deletion vectors on the table if not already enabled.
E. Overwrite the table instead of Merge.

Antwort: B,D

Begründung:
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

89. Frage
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

Antwort: D

Begründung:
This query correctly aggregates usage at a daily granularity by truncating the usage start timestamp to the day and summing the usage quantity, which represents DBUs. Grouping by both the derived daily value and the SKU name ensures usage is accurately categorized by product tier across all workspaces.

90. Frage
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Workspace audit logs
B. Ganglia
C. Driver's log file
D. Cluster Event Log
E. Executor's log file

Antwort: D

Begründung:
The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries.

91. Frage
A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

A. Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
B. Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
C. Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
D. Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)

Antwort: D

Begründung:
Z-ordering optimizes data skipping for selective queries on high-cardinality columns without physically repartitioning the table, making it flexible if query patterns change. Using OPTIMIZE ...
ZORDER BY (user_id, product_id, event_timestamp) improves query performance for filters and joins while allowing incremental writes, avoiding the data skew and maintenance overhead that explicit partitioning or clustering could introduce.

92. Frage
A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A. Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
B. Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
C. Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
D. Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.

Antwort: A

Begründung:
The Databricks documentation specifies that for Lakeflow Declarative Pipelines, detailed data quality metrics are logged as events of type expectation_result within the event log. Each record of this type contains fields including expectation_name, dataset_name, passed_records, and failed_records. Filtering on event_type = 'expectation_result' and expanding the details field allows retrieving metrics for each expectation from the most recent pipeline update. While flow_progress provides summary statistics and data_quality events aggregate results, only expectation_result events provide granular, per-expectation metrics required for audit and monitoring automation.

93. Frage
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