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

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

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q43-Q48):

질문 # 43
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete.
Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
B. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.

정답：D

설명：
Databricks Catalog Explorer provides a feature called AI Generate that automatically produces intelligent comments for columns. This feature uses metadata such as column names, types, patterns, and sampled values to generate human-readable documentation. According to the documentation, this is the recommended method to rapidly enrich schema metadata and improve data discoverability, especially at enterprise scale. Unlike DESCRIBE HISTORY or DESCRIBE TABLE, which only surface technical schema details, AI Generate directly produces business-oriented descriptions. PySpark statistical functions (df.describe) only return numeric statistics and cannot generate descriptive metadata. Thus, AI Generate in Catalog Explorer is the correct approach.

질문 # 44
A data engineer is creating a daily reporting job. There are two reporting notebooks-one for weekdays and one for weekends. An "if/else condition" task is configured as {{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones.
Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
B. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
C. Merge, as the job configuration looks good.
D. Reject, as they should use {{job.trigger_time.is_weekday}} instead.

정답：A

설명：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

질문 # 45
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?

A. Run SQL command DESCRIBE HISTORY table_name
B. Review workspace audit logs
C. Review event logs in the Workspace
D. Check Databricks SQL Audit logs
E. Run SQL SHOW HISTORY table_name

정답：D

질문 # 46
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. Query the event_log_table for events with event_type = ' data_quality ' and directly select the passed_records and failed_records fields.
B. 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.
C. 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.
D. Access the event_log_table, filter for events where event_type = ' expectation_result ' , and extract the expectation metrics from the details field.

정답：D

설명：
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.

질문 # 47
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
C. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
D. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.

정답：D

설명：
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.
Reference:
Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta/optimizations/auto-optimize.html

질문 # 48
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Databricks-Certified-Professional-Data-Engineer 100％시험패스 덤프: https://www.itcertkr.com/Databricks-Certified-Professional-Data-Engineer_exam.html

참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Databricks Databricks-Certified-Professional-Data-Engineer 시험 문제집이 있습니다: https://drive.google.com/open?id=1yAFm1L9MYOwZM-60ddAIwLqFV-ZESNuh