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

Section Weight Objectives
Topic 1: Monitoring, Logging, and Troubleshooting ~8% - Use Spark UI, Query Profiler, and system tables
- Diagnose common pipeline and job failures
Topic 2: Cost and Performance Optimization ~13% - Leverage system tables and observability tools
- Optimize queries, clusters, and storage
Topic 3: Data Modeling ~10% - Apply dimensional modeling techniques
- Design scalable Delta Lake schemas and clustering
Topic 4: Data Transformation, Cleansing, and Quality ~12% - Apply advanced Spark transformations
- Enforce data quality and quarantine bad data
Topic 5: Developing Code for Data Processing using Python and SQL ~22% - Implement scalable Python/SQL code and project structures
- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
- Manage dependencies, libraries, and UDFs
Topic 6: Data Sharing and Federation ~8% - Configure Delta Sharing and Lakehouse Federation
Topic 7: Streaming Workloads and Change Data Capture ~11% - Apply AUTO CDC APIs and exactly-once semantics
- Implement reliable streaming pipelines
Topic 8: CI/CD, Testing, and Deployment ~6% - Deploy with Declarative Automation Bundles, CLI, and REST API
- Implement testing and deployment pipelines
Topic 9: Security and Governance ~10% - Implement row-level security, column masking, and compliance
- Manage Unity Catalog permissions and ACLs

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最新的 Databricks Certification Certified-Data-Engineer-Professional 免費考試真題 (Q85-Q90):

問題 #85
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 = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
  • B. Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
  • C. 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.
  • D. Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.

答案：D

解題說明：
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.


問題 #86
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

  • A. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
    ('delta.deletedFileRetentionDuration' = 'interval 15 days')")
  • B. spark.sql("VACUUM my_table RETAIN 15 HOURS")
  • C. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
  • D. from delta.tables import *
    deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
    deltaTable.deletedFileRetentionDuration = "interval 15 days"

答案：A

解題說明：
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.


問題 #87
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

  • A. Set the pipelines, reset, allowed property to false on raw_iot
  • B. Set the SkipChangeCommits flag to true raw_lot
  • C. Set the skipChangeCommits flag to true on bpm_stats
  • D. Set the pipelines, reset, allowed property to false on bpm_stats

答案：A

解題說明：
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.


問題 #88
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

  • A. Dynamic data masking is applied only at rest, so it does not affect query performance.
  • B. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
  • C. PII masking is only required for direct identifiers.
  • D. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.

答案：D

解題說明：
Consistent PII masking in distributed batch and streaming pipelines requires centrally defined and governed masking logic. Standardizing masking functions and enforcing them through Unity Catalog ensures that the same rules are applied uniformly across all datasets and execution paths, preventing inconsistencies across nodes and workloads.


問題 #89
When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

  • A. In the Query Profiler, under the Stages tab
  • B. In the Query Profiler, under Query Source
  • C. In the Spark UI, under the SQL/DataFrame tab
  • D. In the Spart UI, under the Jobs tab

答案：C

解題說明：
The Spark UI provides detailed visibility into how queries are executed. The SQL/DataFrame tab displays the logical and physical query plans, allowing engineers to visualize execution details and understand how the workload is processed across stages and operators.


問題 #90
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