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

Section	Objectives
Topic 1: 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. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles
Topic 2: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Design and implement scalable data models using Delta Lake
Topic 3: 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. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets
Topic 4: 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
Topic 5: 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 6: Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Use APPLY CHANGES APIs for change data capture 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Configure environments, dependencies, memory, and retry behavior 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 5. Use control flow operators in pipeline components 6. Develop unit and integration tests for data processing code 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 8. Compare streaming tables and materialized views - Using Python and Tools for Development 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 2. Develop User-Defined Functions using Pandas/Python UDFs 3. Manage and troubleshoot third-party library installations and dependencies
Topic 7: 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 8: 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 system tables for resource, cost, audit, and workload monitoring 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
Topic 9: Cost & Performance Optimisation	- 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 - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Understand deletion vectors and liquid clustering 3. Use Change Data Feed to address streaming table limitations and improve latency
Topic 10: Ensuring Data Security and Compliance	- Data Security 1. Use row filters and column masks for sensitive data 2. Use ACLs to secure workspace objects and enforce least privilege 3. Apply anonymization and pseudonymization techniques - Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies

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Databricks Certified Data Engineer Professional Sample Questions (Q134-Q139):

NEW QUESTION # 134
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. overwriteSchema
B. partitionBy
C. mergeSchema
D. checkpointLocation

Answer: D

Explanation:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates.
Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.

NEW QUESTION # 135
Which statement describes Delta Lake optimized writes?

A. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
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. 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.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

Answer: C

Explanation:
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.

NEW QUESTION # 136
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. import sys
date = sys.argv[1]
B. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
C. date = spark.conf.get("date")
D. input_dict = input()
date= input_dict["date"]
E. date = dbutils.notebooks.getParam("date")

Answer: B

Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter.
For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.

NEW QUESTION # 137
An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A. Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.
B. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
C. Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.
D. Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.

Answer: A

Explanation:
Databricks recommends applying data masking or anonymization before persisting PII to ensure compliance with privacy regulations such as GDPR and HIPAA. In a Lakeflow Declarative Pipeline, developers can define custom Python or SQL-based masking functions to standardize PII handling across both batch and streaming inputs.
This approach ensures that data entering the Delta Lake is already anonymized, guaranteeing consistent and auditable behavior. By applying masking during ingestion (in the Bronze layer), audit trails are preserved through pipeline event logs.
While Unity Catalog column masks (option C) can enforce dynamic masking at query time, they do not prevent PII storage. Thus, option D aligns with the best practice of securing PII before storage, while still supporting reproducibility and analytics usability.

NEW QUESTION # 138
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. Apply liquid clustering using the merge join keys.
B. Partition the table by date.
C. Overwrite the table instead of Merge.
D. Enable deletion vectors on the table if not already enabled.
E. Use ZORDER on high-cardinality columns.

Answer: A,D

Explanation:
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.

NEW QUESTION # 139
......

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