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

Section	Objectives
Debugging and Deploying	- Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders - 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
Ensuring Data Security and Compliance	- Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information - Data Security 1. Use ACLs to secure workspace objects and enforce least privilege 2. Apply anonymization and pseudonymization techniques 3. Use row filters and column masks for sensitive data
Developing Code for Data Processing using Python and SQL	- 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 - Building and Testing ETL Pipelines 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 2. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 4. Configure environments, dependencies, memory, and retry behavior 5. Use control flow operators in pipeline components 6. Compare streaming tables and materialized views 7. Use APPLY CHANGES APIs for change data capture 8. Develop unit and integration tests for data processing code
Data Modelling	- Scalable Data Models 1. Understand Liquid Clustering versus partitioning and Z-Ordering 2. Optimize data layout using Liquid Clustering 3. Design and implement scalable data models using Delta Lake - Dimensional Modelling 1. Design dimensional models for analytical workloads
Data Sharing and Federation	- Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Share live Lakehouse data with external computing platforms 3. Configure sharing with external platforms using the open sharing protocol - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance
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
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Cost & Performance Optimisation	- Delta Optimization 1. Understand deletion vectors and liquid clustering 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Apply data skipping and file pruning techniques - 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
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
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 system tables for resource, cost, audit, and workload monitoring 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Query Profiler and Spark UI to monitor workloads

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Databricks Certified Data Engineer Professional Sample Questions (Q111-Q116):

NEW QUESTION # 111
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. VACUUM the Delta table after each batch completes.
B. Perform a full outer join on a unique key and overwrite existing data.
C. Rely on Delta Lake schema enforcement to prevent duplicate records.
D. Set the configuration delta.deduplicate = true.
E. Perform an insert-only merge with a matching condition on a unique key.

Answer: E

Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

NEW QUESTION # 112
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
C. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
D. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
E. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

Answer: C

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.

NEW QUESTION # 113
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
B. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
D. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.

Answer: A

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

NEW QUESTION # 114
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
B. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
C. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
D. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
E. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

Answer: D

Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.

NEW QUESTION # 115
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. Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
B. Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
C. Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
D. Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)

Answer: D

Explanation:
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.

NEW QUESTION # 116
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